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How can MNEs gain the Competitive Advantage by effectively implementing
Knowledge Management through Artificial Intelligence?
IBM Watson Case Study
Cristiano Nico (B70743862/S3899322)
2/12/2019
Word Count: 15,000
Master’s Thesis in International Business and Management
Newcastle University / University of Groningen
Dr. Alan McKinlay / Dr. Hammad Ul Haq
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ACKNOWLEDGEMENTS
This dissertation came to life with the help and support of many people. First and foremost, I
would like to thank God for allowing me to embark on a 1.5-year Master of Science in
International Business and Management in the United Kingdom and the Netherlands. I would
like to thank my mother and father, Camilla and Pietro, for their unconditional love, for always
believing in my potential and for always pushing me to shoot for the stars and never give up.
I would like to thank my dissertation supervisors Alan McKinlay and Hammad Ul Haq, who
provided me with valuable input and advice that I used for improving this manuscript. Thank
you for your help and support. Many thanks go to the IBM Subject Matter Experts and
Consultants, who took their time from their work to answer my dissertation’s questions. Your
contribution was crucial for the dissertation’s development, and I will always be grateful for
it. I would like to thank my American family, Tina, Jack, Clair, Zachary, and John. Many
thanks to my friends Giulia, Anna, Jacopo, Giada, Riccardo, Mauro, Martina, and Cristina.
You all taught me that family is not bound by blood, but by individuals who believe in you
and will always be by your side. I would like to give thanks to my grandparents, Paola, Pietro,
Ada, and Carlo, who are no longer with me today. You are real role models who taught me
that through sacrifices, dedication, and hard work, anything can be achieved. Many thanks
also go to my uncle and aunt, Andrea and Daniela. You taught me that there’s always more to
learn and that academic rigor is a vital component of education. Many thanks to my little
cousin Alma Maria. This manuscript is dedicated to you all.
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ABSTRACT
In today’s society, knowledge management processes play a significant role among
multinational enterprises (MNEs). The analysis of current literature on the subject of
knowledge management (KM) and knowledge sharing (KS) allow us to understand how
strategic this field is and how it can guide management choices by leveraging multicultural
differences. This dissertation gives an overview of the literature in the field of artificial
intelligence (AI) applied to KM. It then deepens the argument through the development of a
case study of a leading MNE. The study will proceed with the analysis and interpretation of
the data obtained from the documentation collected and semi-structured interviews with
subject matter experts (SMEs). This investigation seeks to understand how MNEs can gain
competitive advantage through the effective use of knowledge management through the
implementation of AI tools, taking into account their international outreach. The analysis
seeks to deepen the application of these management practices.
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TABLE OF CONTENTS
Acknowledgements……………………………………………………………………. 2
Abstract………………………………………………………………………………... 3
Table of contents……………………………………………………………………..... 4
1. Introduction………………………………………………………………………... 6
2. Literature review…………………………………………………………………… 10
2.1 Knowledge Management……………………………………………...…………………………………………………… 13
2.2 Knowledge Management and Globalization…………………………………………………………………………….… 15
2.3 Knowledge Management and Artificial Intelligence……………………………………………………………………… 17
2.4 Ethical Aspect of Artificial Intelligence and Knowledge Management…………………………………………………... 20
2.5 Business Results and Future Perspectives of AI applied to KM…………………………………………………………... 21
3. Methodology…………………….…………………………………………………………………………………
3.1 Plan………………………………………………………………………………………………………………………………………………………………………………
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25
3.2 Design…………………………………………………………………………………………………………………….. 26
3.3 Prepare………………………………………………………………………………………………………………………………………………………………………… 28
3.4 Collect…………………………………………………………………………………………………………………………………………………………………………. 28
3.5 Analyze…………………………………………………………………………………………………………………………………………….………………………… 32
3.6 Report………………………………………………………………………………………………………………………………………………………………………….. 34
4. Findings………………………………………………………………………………………………………….…… 36
4.1.1 Main Strategies……………………………..……………………………………………………………………………. 36
4.1.2 Features: Company Perspectives and People Perspectives…...…………………………………….…………………… 37
4.1.3 Outcomes: Business Perspectives and Knowledge Perspectives………….…………………………………………….. 37
4.2 Exploratory Research Propositions……………...………………………………………………………………………… 39
5. Discussion…………………………………………………………………………………………………………… 52
6. Conclusion……………………………………………………………………………………………………..……. 57
Bibliography………………………………………………………………………….. 59
Appendix……………………………………………..………………………………. 75
Appendix A. Semi-structured Interview Questionnaires …………………………………………………………………………………………………… 75
Appendix B. Semi-structured Interview Transcripts ……………………………………………………………………………………………………….… 78
Appendix C. Data Supporting Interpretation - IBM Watson for Knowledge Management……………..………………………………….. 117
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Appendix D. Coding of Personal Interviews …………………………………………………………………………………………………………………..…. 126
Appendix E. Coding of Public Domain Interviews and Speeches……………………………………………………………………………………….. 143
Appendix F. Coding of IBM Documentation... …………………………………………………………………………………………………………………..
151
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1. INTRODUCTION
The IBM Watson case study presented in this dissertation aims to extend the literature
on the application of AI in the context of KM. This manuscript aims to explore how a leading
MNE leverages AI as a tool for everyday use. AI processes applied to KM are no longer
thought of as a desirable future, but are a present, applied, and measurable reality. Through a
set of semi-structured interviews addressed to IBM SMEs, the investigation seeks to analyze
the processes and practices of AI employed by a large MNE that makes use of them
worldwide. A 2019 report from the International Data Corporation (IDC), a U.S. provider of
market intelligence research and advisory consulting, estimates that global growth in AI
peaked at 35.8 percent as of 2018, with IBM holding the lead in market shares at 9.2 percent
(Jyoti et al., 2019). The IDC report highlighted the spillover effects of IBM Watson, the
company’s AI application solutions, into a variety of domains, spanning from agriculture and
manufacturing to human resources (HR) management and marketing communications. As a
result, the research highlights the main features that drive companies towards a digital
transformation in the field of KM.
IBM pioneered the use and implementation of AI in everyday business processes. In 1956,
American IBM employee Nathaniel Rochester was among the participants of the Dartmouth
workshop in Hanover, NH, later regarded as the “birth of AI” as a field of study (Crevier,
1993). Also, IBM anticipated the evolution of machine learning systems by showing the world
that its computers learned and managed to measure themselves against human intelligence.
Towards the end of the millennium, an IBM supercomputer was able to defeat chess champion
Kasparov, and 14 years later, a question-answer computer system won $1 million on a U.S.
quiz show against two human contestants (McCorduck, 2004; Markoff, 2011). Nowadays, AI
allows MNEs to collect data, analyze it, learn, and make better decisions. The enormous and
diversified corpora of information benefit from “intelligent” systems capable of ingesting,
analyzing, and reasoning across information in its various forms (Dorai, 2017). This
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dissertation expands the current literature on AI applied to KM by investigating the experience
of a leading MNE that has applied AI for enhancing intellectual capital and generating and
disseminating new knowledge derived from the use of IBM Watson.
KM is crucial for business success. Smith and Farquhar (2000) assert that the main
ambition of KM is to enhance organizational performance by empowering individuals to
acquire, exchange, and implement their shared knowledge in order to achieve optimal real-
time decisions. Carrillo et al. (2000) have extended this definition by suggesting that the
purpose of KM is to identify, optimize, and manage intellectual resources actively to generate
value, boost productivity, and obtain and maintain a competitive advantage. The dissertation
will borrow Sigalas et al.’s (2013) definition of competitive advantage: “the above industry
average manifested exploitation of market opportunities, neutralization of competitive threats
and reduction of costs.” Knowledge is an intellectual resource: in the global economy,
intellectual capital is overcoming traditional capital and labor assets as a crucial resource in
developed economies (Edvinsson, 2000). The success of organizations in the current global
and interconnected economy depends on how they can cope with rapid, effective, and efficient
sharing of information (Kumar et al., 2014). However, as they grow and expand, it becomes
a burden for them to deal with processing large volumes of organizational knowledge. As a
result, businesses have to devote considerable attention, time, and effort to implementing
effective KM processes. Combining AI to KM practices overcomes the amount of pressure
arising from handling a large amount of information, thus providing the organization’s
workforce with valuable information.
Studies on AI have enriched the way of collecting and processing large amounts of data.
These often arrive in an unstructured way (such as emails, images, chats, blogs, hypertexts)
and can be converted into predictive elements that help solve problems, make decisions, and
improve customer satisfaction (Snedaker, 2007). Therefore, KM needs AI to manage an
increasingly complex and articulated information system made up of raw data, informal
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communications, and heterogeneous documentation. Every employee of a company is a
potential contributor. KM instruments already incorporate some aspects of AI technology,
such as intelligent agents, data mining, ontologies, as well as Bayesian reasoning. Besides,
content management, personalization of human-computer interactions, user profiling, and
case-based retrieval techniques are some of the many AI techniques available to be used in
various aspects of core business processes as a result of Web-based technologies and
components-based software engineering (Tsui et al., 2000).
Business scenarios are becoming increasingly global. Products and services offered
worldwide by MNEs must face the challenges of multicultural differences. Information
systems help people to make decisions, to solve problems, but more generally to integrate the
contributions of people often located in scattered parts of the world. The development of IT
solutions provides valuable support in accelerating the process of knowledge acquisition in a
multicultural business environment. KM concerns organizational sharing of information and
collaboration. Teams and groups face increasingly complex decisions. Managers that support
group work and cross-cultural teams, where team members may work in multiple locations
and at different time zones, need to take into account communication issues, technology-
mediated cooperation, and work methodologies.
Two different approaches in developing IT solutions have been proposed to support the
challenges of managing knowledge in the global business scenario: structured data
management and unstructured data management. Structured data refers to “useful information
[…] such as classification, clustering, visualization and information extraction”, which
facilitates search, analysis, and integration with existing structured data (Sukanya and Birunta,
2012; Ise, 2016). Unstructured data, on the other hand, consists of unorganized, unclear, and
fragmented information, which results in ambiguities that are difficult to classify using
traditional IT programs (Rusu et al., 2013). Structured data and unstructured data have led to
the emergence of two leading-edge information technologies: Business Intelligence (BI) and
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KM. BI describes tools, techniques, and solutions that facilitate managers’ understanding of
business situations (Rouhani et al., 2012). BI deals mainly with extracting, integrating, and
analyzing business information gathered from internal and public databases to disclose
"strategic" business dimensions (Albescu et al., 2009; Rouhani et al., 2012). KM tools and
techniques provide expertise and global domain knowledge to facilitate the interpretation of
high-value business information (Albescu et al., 2009).
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2. LITERATURE REVIEW
Up until the early nineties, the literature on AI applied to KM was very limited. Many
companies lost interest over the potential of AI during this period. With "AI Winter," Crevier
(1993) indicates a time-period when organizations felt that high expectations on the subject
would lead to great disappointments. This has also meant a setback in research funding in this
area. Carbone and Kersberg (1993) proposed the development of an automatic system that
would facilitate the database interface in order to obtain a better data analysis. From 1993 to
2011, the field of AI and the related literature related to KM reborn, both because of
technological evolution, through the use of cheaper but also much more powerful computers,
and the desire to apply new scientific discoveries to different industrial fields. This phase can
be defined as the "AI Dream," where computers can participate actively in economic
development.
Over this period, several studies have enriched the literature on the subject of AI and KM.
Venugopal and Beats (1995), through a conceptual model of an integrated intelligent system,
proposed a case-based reasoning AI system that supports the learning and knowledge
processes within the organization. In the consulting environment, O’Leary (1998) discusses
how firms manage knowledge by following best practices using ontological and technical
forms of AI. Steier et al. (1998) discuss how AI can reduce problems at every stage of the
knowledge cycle and are confident that AI technologies for KM (e.g., document management,
user profiling) will receive important returns. At the same time, applying AI in KM must be
integrated with more conventional techniques, considering that the "human" and
organizational aspects are predominant (Edwards, 2000). Fowler (2000) and Tsui et al. (2000)
emphasized the importance of research and development (R&D) of AI tools as a support for
KM processes in the future and for better represented and structured knowledge. Therefore,
the research and the corresponding literature on the theme of AI-related to KM are mainly
related to future opportunities.
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By the very end of the 20th century, AI began to receive critical acclaim from the public.
In 1997, IBM supercomputer Deep Blue defeated Garry Kasparov, the world’s renowned
chess champion. Millions of viewers followed the event around the world (McCorduck, 2004).
In 2005, a Stanford University robot car won a U.S. competition by driving without human
aid for seven hours along a desert trail in southwest Las Vegas. Machines were able to drive
fast and safely without human intervention (Orenstein, 2005). In 2011, Watson, IBM’s AI
engine, beat the two most celebrated champions of the American quiz show “Jeopardy!” by a
significant margin (Markoff, 2011). The event marked a big step forward for AI, as machines
were able to understand, react to, and potentially substitute human beings. Applied to KM, AI
adds value to knowledge by analyzing and simulate human functions (Hoeschl and Barcellos,
2006).
Through AI implementation, organizations can leverage the breadth of their knowledge.
Distributed Artificial Intelligence allows the acquisition of knowledge and semantic analysis
of information obtained from the web (Gandon, 2002). Besides, AI tools and techniques not
only allow for knowledge analysis and management but also to generate new knowledge
(Liebowitz, 2001; Metaxiotis et al., 2003). AI systems identify the knowledge, acquire it,
generate it, organize it, integrate it, and distribute it, thus improving the quality of
organizations’ decision-making processes. The interrelation between AI and KM and the
result of their useful application is the ability to learn and solve complex problems (Becerra-
Fernandez et al. 2004). The effective use of AI in the business decision-making process
concerns the results that intelligent systems allow achieving in terms of the position of
advantage over an organization's competitors and better performance.
From 2011 up to 2016, the domains of knowledge and IT have had a profound evolution
towards the use of "Big Data." From the general interest and forecast of future scenarios,
companies have begun to invest heavily in concrete AI projects. This phase, drawing on the
words of Lohr (2016), can be defined as “AI Frenzy.” Specifically, the literature focuses on
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the positive effects of AI implementation in KM. Trends in the use of AI in KM focus on
optimizing document management, research, and information sharing via blogs and wikis
(Bizirniece, 2011). Mercier-Laurent (2014) explains how intellectual capital management is
one of the main assets of today's organizations. The use of innovative techniques using AI can
provide significant help in conserving, updating, visualizing, and searching for relevant
elements of human capital. These systems will be one of the main levers for the transformation
of companies towards digitalization and new forms of knowledge processing (Avdeenko et
al., 2016).
Since 2017, the literature on AI and KM has been moving towards practical application
and analysis of possible ethical implications. In this period, the use of AI leads to concrete
results, visible in different fields, such as medicine, education, or financial services. This
phase can be defined as "AI Factual" to indicate the real, concrete, and efficient application
of a system advantageous to companies to improve and distribute knowledge effectively.
Some companies use AI to allow managers to make more informed decisions (Paschek et al.,
2017; Duan et al., 2019). In other cases, AI is used by companies to free people from repetitive
and straightforward tasks by engaging them in more complex activities and the valorization
of HR and their knowledge (Botha, 2019). Technological infrastructures are preparing for a
new type of KM. AI is becoming more and more a part of human intelligence, and decision-
making processes are changing with an undoubted impact on human and organizational
behavior (Paschek et al., 2017; Zbuchea and Vidu, 2018; Duan et al., 2019). As a result, AI
can positively transform the business processes of their organization.
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Figure 2.1 Timeline of AI and KM literature
2.1. Knowledge Management
Numerous studies on KM (Drucker, 2001; Barclay and Murray, 1997; McAdam, 2000;
Rosenthal-Sabroux and Grundesten, 2008; Dalkir, 2005; Nonaka and Van Krogh, 2009) allow
us to understand how companies can transform primary data into structured information that
can become useful work tools for their employees. The field of KM is established for more
than 30 years and shifted from a vaguely defined concept to an essential element of
organizational life. Over time, the nature of KM has evolved. Over the last decade, the
challenge of determining an applied definition of the domain has moved from scholars to
professionals. The 21st century introduced definitions of KM across a broad spectrum of
disciplines (Girard and Girard, 2015). Jasimuddin et al. (2005) link KM to several disciplines,
such as information systems, organizational analysis, strategy, and HR management. Various
authors (O’Dell and Grayson, 1998; Davenport and Prusak, 1998) attempted to provide
general definitions of KM, which emphasize the effective and efficient use of resources that
enable organizations to improve their overall performance. Liebowitz (2012) claims that KM
is the combination of three components: people (how to create KS environment and culture in
the organization), process (how to manage KM processes and align the employees’ daily
tasks), and technology (how to create a platform for communication and KS among
employees).
One of the most critical distinctions in KM is between explicit knowledge and tacit
knowledge. On the one hand, explicit knowledge can be easily codified or written in text as
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well as exchanged (Tiwana, 2002). This form of knowledge is crucial for firms’ ability to
collect, send, and even sell, and it can be stored in written and electronic form. On the other
hand, tacit knowledge can be retained in people’s memories, and it is based on “intuition,
feelings, faith, life experiences, and organizational culture” (Domagała, 2017). Woo (2004)
defines tacit knowledge as the most critical asset for organizations seeking to gain a
comparative advantage. In order to fully grasp its hidden value and to exploit the overall
experience gained by individuals’ mental models over time, organizations must be able to
convert tacit knowledge into explicit knowledge, a process known as externalization.
Tacit knowledge is difficult to codify, and externalization methods of a whole body of
knowledge remain controversial. Johnson et al. (2002) argue that in the process of knowledge
conversion from tacit to explicit, some of its original features may disappear. In terms of
knowledge transfer and sharing within the organization, Polanyi (2009) identifies an array of
processes that convert workers’ knowledge and tacit knowledge into valuable knowledge
resources that allow an organization to gain a competitive advantage. Moreover, studies
(Brown and Duguid, 1991; Hedlund, 1994) posit that tacit knowledge is context-dependent
and is triggered and constrained by human relationships. McKinlay (2002) illustrates how a
U.S.-based international pharmaceutical corporation developed an online archive to codify
and disseminate tacit process knowledge beyond the single working group. Besides, research
has linked the success of organizations in the field of technological innovation to their ability
to leverage tacit knowledge gained over time (Seidler de Alwis and Hartman, 2008).
Therefore, tacit knowledge is a critical resource that has the potential to sustain organizations’
competitive advantage and innovation.
The concept of knowledge refers to the practical use of information already gathered for
a specific purpose. Organizational learning occurs when members of the organization can
draw conclusions from their own previous experiences and build on routine practices that
support their behavior. King (2009) defines KM as the "planning, organizing, motivating, and
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controlling of people, processes, and systems in the organization to ensure that its knowledge-
related assets are improved and effectively employed." KM is the entire workforce’s collective
knowledge targeted at achieving precise organizational goals: it results in strategies and
processes aimed at identifying, capturing, structuring, valuing, leveraging, and sharing the
intellectual resources of an organization to improve its performance and competitiveness
(Mohajan, 2017). KM systems can capture tacit forms of knowledge by externalizing and
integrating them (Nonaka and Takeuchi, 1995). Davenport and Prusak (1998) focus on how
organizations design processes that allow to capture, code, and transfer knowledge.
2.2 Knowledge Management and Globalization
At a later time, the KM literature was interested in how knowledge is created (Ocholla,
2011) and how organizations can apply it effectively to make decisions, innovate and create
a competitive advantage over competitors in the marketplace (King, 2009; Bouthillier and
Shearer, 2002; Gold at al., 2001; Wen, 2009; Birasnav, 2014). Significant theoretical
developments (Marquardt, 1996; Jamali et al., 2006) addressed how companies can adapt their
processes and technologies to transform information into applied knowledge. The world is
continuously changing, and the two key elements that characterize it are globalization and the
management of big data. In this case, the literature has addressed the problem through two
separate studies: studies concerning cross-cultural implications (Maham, 2013; Ling, 2011)
on KM; studies concerning the difficulty of collecting and managing big data (Marr, 2015;
Hilbert, 2016) and the opportunity to obtain hidden information and weak signals.
MNEs operating globally across international borders must oversee their knowledge assets
and a multicultural workforce (Albescu et al., 2009). Several studies on organizational
learning highlight the strong influence of individual employees’ cultural values in KS
practices and communication (Hambrick et al., 1998; Hofstede, 1998; Pfeffer and Sutton,
2000; Hutchings and Michailova, 2004). At the global level, intercultural KM is an ever more
essential factor in corporate practice and policy. In an effort to solve crucial cultural issues in
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researching diverse contexts, it is necessary to structure roles, responsibilities, and power
between several organizational components, such as teams, units, and management structures
(Del Giudice et al. 2012). Besides, identifying similarities and discrepancies in KS strategies
of managers of diverse national and ethnic workforces is a crucial requirement for successfully
designing flexible KM systems that can be adapted to the styles and needs of employees of
MNEs around the world (Ardichvili et al., 2005). As a result, cultural contexts shape KM
systems planning and implementation decisions, which must be in line with the different
employees’ managerial styles and work values.
KM in an international environment relies on the mental models of practitioners from
different countries and cultures. Former IBM employee Geert Hofstede (1991) identified five
cultural dimensions (individualism-collectivism, power distance, masculinity-femininity,
uncertainty avoidance, and long-term orientation) and assigned a score for each country based
on surveys across IBM subsidiaries in 64 countries. Bhagat et al. (2002) provided an
integrative framework using Hofstede’s (2001) “Culture Consequences” to account for how
national culture differences can influence KS among employees from different cultures. The
authors conclude that if the knowledge content presents elements compatible with the
dominant cultural model, organizations will manage and understand the transfer of knowledge
more efficiently. Chmielecki (2013) identified four critical factors that seek to link KS
behaviors with cultural differences. First, the author argues that culture has a significant
influence on the perception of useful, valuable, or legitimate knowledge for an organization.
Second, culture determines the level of organizational knowledge that an individual can
control, and determines who should retain particular knowledge, who should share it, and who
should accumulate it. Third, culture creates a context of interpersonal social interaction,
representing the rules and practices that determine the place where people communicate.
Fourth, culture is the cornerstone of generating and acquiring new knowledge (DeLong and
Fahey, 2004).
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National cultures deeply intertwine with organizational behavior, which in turn affects
KM decisions. As employees bring their individual culture to the organization through their
different customs and language, organizational culture, in turn, impacts their values,
behaviors, perceptions, and desires, including the willingness to sharing knowledge (Kreitner
et al., 2008; Usoro and Kuofie, 2006). A study on information technology systems (IT) and
KM supports the claim that business managers must adapt IT applications to the decision-
making styles of people from different countries and cultures (Martinsons and Davison, 2007).
What is more, research involving Chinese and American employees discovered that idioms,
different mentalities, and different degrees of perceptual credibility of voluntary KS were
three significant national culture differences impacting online KS in a multicultural context
(Li, 2010). Therefore, KM professionals must analyze and understand the national and
organizational cultural context in which they find themselves in order to apply knowledge
correctly in the business environment.
2.3 Knowledge Management and Artificial Intelligence
Practitioners can now deliver more resources for training, quality control, and refining of
AI results. Machines can increase the experience of their human counterparts and even assist
in creating new specialists. These new systems, more closely imitating human intelligence,
are becoming stronger than the large data-driven systems that preceded them. They could
impact the 48% of the US labor force who are knowledge workers—and the well over 230
million roles of knowledge workers globally (Daugherty and Wilson, 2019). As a
consequence, enterprises will ultimately have to redefine knowledge-work processes and
careers to exploit AI’s potential. AI technologies have an essential role to play in the analysis
and interpretation of all information obtained. This aspect requires particular consideration in
light of recent experiences in the application of AI.
An extensive literature dedicated to AI (Kok et al., 2009; Russell and Norvig, 2010; Smith
et al., 2006) studies the phenomenon through processes elaborated by the human mind and its
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complex machine learning system. AI studies are often complex owing to the specific nature
of the issues, which concern theoretical, practical, operational, philosophical, and ethical
aspects. AI intertwines with autonomy and adaptability by learning from a dynamic
environment (Miailhe and Hodes, 2017). Several studies (Strauß, 2018; Mangelsen and
Alexander, 2019) deal with the application of AI to organizations that want to manage large
amounts of data effectively. Tsui et al. (2000) explore how to make these massive amounts of
data usable in terms of knowledge, cognitive, and predictive systems that facilitate decision
making and problem-solving.
In order to apply KM successfully, AI technology must operate on a large population of
people that Drucker (1999) defines as "knowledge workers" within companies. Daugherty and
Wilson (2019) define knowledge workers as "people who reason, create, decide, and apply
insight in non-routine cognitive processes." They defined knowledge-worker productivity as
the most significant management challenge of the 21st century and the "first survival
requirement" for developed countries. Without it, it would be unimaginable for them to
maintain their leadership and living standards (Drucker, 1999). The organizations cannot learn
or develop sound knowledge independently of their human capital (Bogdanowicz and Bailey,
2002). If knowledge workers act in line with the objectives of acquiring, sharing, and reusing
knowledge, it is vital to understand how AI systems can address and solve problems through
some autonomy of action and how it is possible to assist people in developing effective
solutions.
In the work environment, professionals use AI to transform data and create new business
opportunities. With AI, data is not just collected, but used effectively to fuel trust in
organizations, accelerate research and scientific discovery, and enrich customer interactions.
IBM Watson is an AI platform that integrates workflows from any business area. It uses
machine learning techniques from small data sets and to develop new business ideas that
enhance daily work (Reisert, 2018). Watson applications designed for business purposes are
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an excellent solution for KM and BI: it is a computer system designed to address the needs of
the future. Organizations must optimize heavy workloads in order to meet the new challenges
of a future market that requires increasingly intelligent solutions. IBM Watson includes a set
of industry-specific analytics solutions that leverage a new way to analyze cognitive content
(Perrone, 2011). Watson develops through coherent reasoning of information to speed-up and
make better decisions, minimize costs, and optimize results.
Managing knowledge is essential in many organizational contexts, such as health care,
education, finance, transport, energy. IBM Watson is subject to continuous evolution by
world-renowned experts who regularly draw new knowledge from the domain of competence
and help people make informed decisions faster (Saravanakumar, 2019). Watson collects
information from a wide variety of data types, including unstructured data, without additional
integration, enabling the processing of extensive data archives. Through Watson, companies
can transform the way they manage knowledge sharing by exploiting forms of natural
language and generating hypotheses and new forms of learning. Watson combines several
processing technologies and parallel probabilistic systems to improve the way companies
solve problems. According to an IBM Watson document (2012), IBM's vision today is
defining, establishing, and guiding markets towards innovative cognitive systems. These
systems may be particularly useful where conventional approaches no longer work, the
development of a cognitive class fosters secure and scalable modular solutions, and where the
generated customer value is evident, demonstrable and quantifiable (IBM Corporation, 2012).
According to IBM data (IBM Watson, n.d.), IBM invested over $5 billion in R&D and
filed more than 8,000 industrial patents. Nearly 2,700 of these patents are related to AI.
Watson is the primary tool that demonstrates the technological advances achieved by the IBM
Research group (IBM Watson, n.d.). Lisa Latts (2016) describes an example of IBM Watson
AI for managing big data in the health care sector: over 80% of patient and disease data are
unstructured. Watson's goal is creating a new level of collaboration between humans and
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technology to help improve relationships, communications, and the use of knowledge by
accelerating the dissemination of information and decision support.
2.4 Ethical Aspects of Artificial Intelligence and Knowledge Management
Ethical dilemmas vary across cultures, religions, and beliefs. Nevertheless, organizations
can develop acceptable ethical frameworks to guide the reasoning and decision-making of AI
technology to account for their actions. Governmental institutions and law-enforcement
agencies contribute significantly to ensure that businesses and organizations adhere to and
enforce their code of ethics. Last year, the European Commission appointed 52 representatives
from academia, industry, and civil societies to form the “Independent High-Level Expert
Group on Artificial Intelligence.” Recently, the AI expert group published fundamental ethical
guidelines for ensuring “Trustworthy AI” (Independent High-Level Expert Group on
Artificial Intelligence, 2019). Trustworthy AI systems should abide by all applicable laws and
regulations and adhere to ethical standards and morals. Meanwhile, AI stakeholders and
practitioners must acknowledge any unintentional harm that AI systems can cause. Therefore,
the AI expert panel advanced four ethical principles for developers, deployers, and customers
dealing with AI systems: “respect for human autonomy, prevention of harm, fairness, and
explicability” (Independent High-Level Expert Group on Artificial Intelligence, 2019). AI
technology must be adaptable enough to undergo regular updates and improvement as
organizations identify and address ethical challenges.
Working life and job structures have rapidly changed with the rapid surge of
technology, and organizations are increasingly paying attention to ethical issues.
Multinational IT company IBM advises AI designers and developers when dealing with ethics
awareness: this includes, among other things, mitigating bias by promoting inclusive
representation of a diverse population and preserve user data privacy and control over her
access and uses (IBM Corporation, 2019). As a result, ethics and privacy ensure that AI and
humans work together, trust each other to bring the best in terms of data and customer
21
experience. An organization that adopts AI systems in compliance with ethical principles must
demonstrate transparency and reliability to the organization, its employees, and customers
(Morgan, 2017). Undeniably, people and organizations can use implement AI technology to
enable human self-actualization, fostering human agency, as well as enhancing social
capabilities and cohesion (Floridi et al., 2018). Ethics applied to AI gives organizations a
competitive advantage for recognizing and undertaking new and rewarding socially
acceptable opportunities. Ethics also enables organizations to identify and prevent, or at least
minimize, socially undesirable actions.
Successful organizations create and acquire new knowledge and use it to improve their
operations and services. Organizations and personnel implementing ethics in their best
practices can speed up quickly the conversion of explicit into implicit knowledge and vice
versa. Both employers and employees face ethical dilemmas. Employers can misuse
employees' knowledge without giving them credit for pooling the know-how. On the contrary,
employees may withhold or divert the knowledge of their employer or team for their personal
gains. Other ethical dilemmas concern the company's rights to limit access to knowledge and
society’s rights to share organizational knowledge for the common good (Land et al., 2007).
Rezaiian and Ghazinoory (2010) highlighted the relationship between integrity, mutual
respect, trust, accountability, empathy, commitment, and KM processes. A more recent study
reports that confidentiality, intellectual property, trust, confidence, and care in authenticity is
of utmost importance in encouraging employees and organizations shifting from explicit
personal knowledge to group and explicit organizational knowledge (Akhavan et al., 2013).
2.5 Business results and future perspectives of AI applied to KM
Several studies have addressed the possible effects of MNEs implementing AI techniques
in KM on business results and have outlined possible future scenarios. A McKinsey Global
Institute survey of 3,000 C-level business executives in 10 countries and 14 sectors identified
five strategies for maximizing AI's potential: planning growth, investing in people’s talent,
22
rethinking strategies, relying on a robust digital foundation, and develop integrated AI systems
(Bughin and Hazan, 2017). A study by Capgemini illustrates similar trends. The report
highlights the rise of “Smart Factories,” which are companies that use AI tools and can add
up to $1.5 trillion to the world economy through digital transformation: the report registers an
overall efficiency growth annually over the next five years, reaching seven times the growth
rate since 1990 (Capgemini, 2017). The literature agrees that there is a continuous drive
towards the company’s digitalization, a process that starts with a strong strategic vision and is
implemented in a pervasive way in every aspect of the organization. Kruhse-Lehtonen (2019)
argues that business leaders must create a business environment that supports digital
transformation by paying attention to how they train their people, setting attainable
organizational goals, and making substantial investments.
Managers willing to embrace AI and digital transformation must spread their message
across the whole organization. In order to achieve significant business results, organizational
decision-makers must communicate actively with their AI teams and stay abreast of
technological improvements (Moldoveanu, 2019). In this way, it will be easier to define a
robust strategy and a straightforward vision for the organization. A McKinsey survey
projected that early adopters of cognitive technology benefit from higher economic growth
than non-adopters (Chui et al., 2018), and Bughin (2018) warns that companies that are not
investing in AI could lose competitiveness in the market. As a result, the literature highlights
the importance of communicating decisions related to the digitalization and implementation
of AI systems at all levels of the organization is essential for gaining a competitive advantage
in the long-term.
The overarching framework that emerges from the literature review shows a growing
interest in AI applied to different business areas and KM processes. The first part of the
literature review traced the evolutionary analysis of the field of AI applied to KM from the
second half of the previous century to the present day. The second stage of the review
23
addressed several issues dealt with in the dissertation, covering not only crucial aspects related
to the literature on AI and KM, but also critical areas of intersection, such as the implications
of KM to globalization, and consideration on ethical aspects and data privacy. The literature
review concludes with studies that illustrate possible future scenarios in which people and
organizations benefit greatly from the use of automated systems. The research has not found
studies exploring in-depth how distinct MNEs apply AI in their KM processes. Precisely for
this reason, the present exploratory single case study aims to document IBM’s experience in
its application of AI tools and techniques in its KM practices.
24
3. METHODOLOGY
The research analysis follows a qualitative methodological approach. An inductive
approach will be adopted to measure the effectiveness of KM systems that use AI techniques
in MNEs. The study will proceed through the observation of specific cases applied to real
scenarios and will obtain results that will allow the development of theoretical propositions.
The research design will, therefore, be exploratory: the dissertation aims to explore the
phenomenon under study, and the qualitative method adopted is the case study approach.
Simmons (2017) argues that case study research is highly flexible and versatile. The case
study methodology is an approach that guides experiential observation. The choice of the use
of case studies to analyze issues related to AI and KM is particularly useful in deepening
context-dependent knowledge and experience (Flyvbjerg, 2006). The case study approach
allows examining the data within a specific context carefully. The aim is to consider one MNE,
deepening the analysis by collecting experiences through the review of the documentation
collected and through direct interaction with SMEs of the company involved.
The methodological application of the Case Study follows the indications of the social
scientist and President of the COSMOS Corporation, Robert K. Yin, reported in the text "Case
Study Research: Design and Methods" (2018). The organization of the case study develops
through a linear but iterative path characterized by 6 logical steps:
1. PLAN: Understand if the Case Study is an appropriate research method;
2. DESIGN: Identify the cases to be analyzed and which type of case study will help to
achieve the best results;
3. PREPARE: What to do before starting to collect Case Study data;
4. COLLECT: Collect the most appropriate sources that best fit the case study;
5. ANALYZE: Develop a general analysis strategy and proceed with the processing and
interpretation of the collected data;
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6. REPORT: Define how to represent the information collected concerning the purposes
of the dissertation.
3.1 Plan
The use of the case study is highly complementary to regular scientific research
(Eisenhardt, 1989). When deciding whether or not to use the case study approach, it is crucial
to consider the type of question the research wants to answer, the level of control that the
interviewer has over the behavioral events examined, and the events' development, focusing
on the historical analysis or the recent past, and its evolutionary perspective (Yin, 2018).
Besides, Yin (2018) argues that "Who, What, and Where" are issues sought through
documentation, archives, investigations, whereas case studies require a more in-depth and
detailed investigation that addresses “How and Why" questions. The research question for this
case study analysis is the following: “How can MNEs gain the Competitive Advantage by
effectively implementing KM through AI?”. The present exploratory research deals with a
subject area that is little dealt with in literature, difficult to quantify, and characterized by
thematic issues that require more in-depth investigation.
The case study analysis is then also appropriate because the research deals with
contemporary events in which the researcher cannot manipulate specific behaviors during the
investigation. The study will be exploratory and follows the evolution of the phenomenon
over time instead of merely measuring its frequency, as in the case of historical statistical
analysis. In carrying out the case study, data from different sources, such as documents and
interviews, will be taken into account. The number of units examined by the case study is
more limited compared to other research methods, such as surveys. Therefore, it is necessary
to identify which experts in the field have gained adequate knowledge and experience to
respond to and better describe specific events.
KM managed through AI techniques is a phenomenon whose boundaries are not clear and
defined. In our case, the phenomenon analyzed is not influenced by the research developer,
26
being related to observable and objective events. Observing how an MNE carries out its KM
by leveraging AI is an objective element that the researcher can only detect without being able
to influence it. Consequently, the orientation of the case study will be "realistic" because it
describes a single reality independent of the observer (Yin, 2018). As recalled by Schramm
(1971), a case study aims to enlighten a particular decision or multiple decisions.
When it comes to case studies, documentation, interviews, and secondary analysis are the
primary sources of data. Researchers are encouraged to make greater use of documents,
interview the right people, and make observations more unbiased (Yin, 2018). Moreover,
addressing a particular audience and focusing on critical decisions will help to focus on the
direction of the case study. The study examines the decision of some MNEs to use AI
techniques in KM: why they make this decision, how they implemented it, and what corporate
benefits they bring in terms of competitive advantage. Besides, the case study involves the
triangulation of data from different sources of evidence.
3.2 Design
The research design identifies the specific case under investigation and establishes the
rationale connecting the empirical data to the original research question and its outcome. The
research question will help to identify and collect the relevant information of the MNE under
examination and to delimit the scope of the survey. The MNE examined has been working for
years in the field of KM using AI techniques and exploiting its international presence to
enhance its intercultural dimension. The design of a single case study arises for several
reasons. First, the particular focus on this MNE allows the researcher to examine an
organization that was a pioneer in the field of AI and was able to employ it successfully in
business and KM processes. Second, the researcher has completed an internship at the
examined MNE, where he had the opportunity to understand the context better and to meet
people who helped him build a network of relationships, allowing him to deepen the
investigation. Third, to extend the scope of the investigation to a multiple case study, the
27
researcher would have spent a considerable amount of time contacting SMEs of other
companies, thus jeopardizing the overall focus on important issues related to AI and KM.
To answer the research question and develop propositions, it was decided to investigate
the case in-depth. The study examines a holistic, single case study. In order to evaluate the
research project’s overall quality, Yin (2018) suggests the application of construct validity,
internal validity, external validity, and reliability:
TESTS Case Study Tactic Phase of Research in
which Tactic Occurs
Construct
Validity
Use multiple source of evidence
Have key informants review draft
case study report
Data collection
Composition (case study
final report)
External
Validity
Theory in single case study
Replication logic in multiple case
study (not used)
Research design
Research design
Reliability Use Case Study Protocol
Develop Case Study Database
Maintain a Chain of Evidence
Data collection
Data collection
Data collection
Table 3.1 Case Study Tactics (Yin, 2018)
Based on Yin (2018), construct validity helps the research identifying the appropriate
operational measures for the case study: it includes the use of multiple evidence sources
through documentation analysis and interviews, as well as key informants (from SMEs) who
provide feedback on case study findings. External validity concerns the results’
generalizability beyond the immediate study. The use of a single case study deepens the
investigation of an MNE through the support of experts and consultants. In order to test
reliability, the Case Study Protocol helps to organize the documentation in detail and proceed
to the analysis with a defined operational method. Besides, the Case Study Database includes
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all the information collected and complete with the researcher's report. As a result, the research
guides the reader through the case study from the initial research question to the case study
discussion.
3.3 Prepare
Yin (2018) advises researchers to abide by critical practices in order to prepare the case
study adequately. Case study analysis requires them to know how to ask relevant questions
and interpret the interviewees’ input correctly. The research sets up questions at various levels.
The first group of questions will be more generic and address several SMEs, whereas the
second set of questions derive from analyzing the results of the first set and will allow for
further inquiry. Besides, Yin (2018) asks researchers to be good listeners, avoiding
preconceptions or existing ideologies. In order to capture large volumes of data without bias,
active “listening” skills are applied not only to semi-structured interviews but also
documentary evidence.
Yin (2018) argues that case study analysis demands to be adaptive, considering all
situations as opportunities and not as threats: during the analysis and deepening of the different
themes, an adaptation of the contents may be necessary to maintain an impartial and
unconditional position. The case study researcher must develop a mastery of the issues dealt
with and have a firm grasp of the relevant theoretical issues in order to make analytic
judgments when collecting data (Yin, 2018). A careful inquiry of the subject matter is carried
out through documents, websites, conferences, and online public domain interviews and
speeches. Moreover, the study adopts appropriate ethical behavior and consideration by
avoiding any bias and being sensitive to contrary evidence by developing ethical behavior.
3.4 Collect
The case study draws on semi-structured interviews and documentary information. The
research will collect multiple sources of evidence to confirm the same observation or to rebut
conflicting findings, an evaluation procedure known as data triangulation (Patton, 2015). Data
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triangulation aims to enhance the case study’s construct validity by providing multiple
measurements about the same phenomenon, and the diversification of data sources allows for
a broader and more complete development of themes (Yin, 2018). The research will organize
and document all the data collected through the construction of a case study database, which
will help to identify possible relationships, highlighting repetitive elements, and increasing
the transparency of the results. The analysis has employed word processing tools (i.e.,
Microsoft Excel and Microsoft Word) to arrange the data. In order to increase construct
validity, the research maintains a chain of evidence. The findings have narrative materials
derived from the case study database, referring to interviews and company documentation.
The researcher has selected the interview participants based on the skills, knowledge, and
activities that they were able to experience in their work. The interview respondents are SMEs
located in operating in the researched MNE’s offices located across Europe and use AI
platform IBM Watson daily. For the most part, the interviewees worked in Italy (8), except
for two SMEs working in the United Kingdom (1) and the Netherlands (1). The SMEs
interviewed hold degrees in different fields of study: Computer Science, Industrial
Engineering, Electronic Engineering, Biomedical Engineering, Physics, Economics,
Organizational Theory, and Master of Business Administration (MBA). As for the
documentation part, the research collected MNE’s reports, white papers, as well as public
domain interviews and speeches available on the Internet with SMEs who hold, or have held
in the past, prominent positions in the MNE and discussed the role of IBM Watson on work
practices.
Data collection allows us to analyze available data and examine the situational context.
The data collected and processed will provide an overview of the context at the multicultural
level and will examine a large MNE that employs advanced KS techniques. The data
collection process aims to lay out the background study through exploratory research of the
information available on the Internet, open data, and other publications on the topics under
30
investigation. Also, the research will integrate data collected through semi-structured
interviews with experts in the field. The research will examine one of the nine MNEs reported
by Forbes that exploit AI technologies’ potential (McKendrick, 2019). Information will be
collected and processed based on how these companies use AI and generate knowledge, how
knowledge is shared and becomes a company asset, and what results businesses have achieved
through the application of these tools.
The data will be processed to describe the different phenomena and lay the foundations
for the next phase of the study. The choice of a large MNE derives from the peculiar
characteristics and experiences gained by this company in recent years, both on the subject of
KM and the use of advanced AI techniques. The data was collected using multiple methods,
analyzing different sources, in order to triangulate the results. The data collection follows a
theoretical sampling developed in two phases: in the first phase (first level), the research
interviewed SMEs that had a broad view of the topic and collected documents to obtain data
to cover the whole spectrum of the research question. In the second phase (second level), the
analysis has deepened specific areas and researched data in order to confirm or modify the
categories of the developed theory. The data collected were organized through "sensitizing
concepts" (Bowen, 2006), that is, guiding principles that represent the starting point of the
research. The research mainly asked open-ended questions (What? How? Why?) to encourage
the development of divergent thinking by respondents.
The questionnaires were designed to gain an in-depth understanding of the subject,
overcoming possible biases about the research area. The collection of the relevant literature
aims to support or refute the interview findings (publications, articles, video interviews with
SMEs present in public sites). The multiple methods consisted of multi-level interviews of in-
depth analysis and collection of business documents. The data collection aimed at collecting
qualitative data by interviewing experts (SMEs). In the first stage of the data collection, the
research carried out a collection of generic documentation on the subject of AI and KM
31
(academic and business articles, webpages, and books). Subsequently, based on documentary
evidence, a first level direct interview questionnaire was set up (see Appendix A). At the end
of this activity, the processing of the data collected from the first interviews facilitated a
greater focus on the study context and the drafting of the second questionnaire of questions
used for the second level direct interviews. Further interviews and documentation of SMEs
found on public websites enriched the direct interviews. The following diagram summarizes
the sampling structure employed in the data collection:
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Quantitative Details of Interview and Document Data
Source description Level 1 Level 2
IBM AI Cognitive Delivery Manager (direct interview) X
IBM Client Executive AI SME (direct interview) X
IBM Technical Solution Architect Cloud & AI Cognitive (direct Interview) X
IBM Senior Managing Consultant & Research Scientist IBM Watson AI & Advanced
Analytics (direct interview)
X
IBM AI Cognitive & Analytics Consultant (direct interview) X
IBM Senior Watson AI Consultant (direct interview) X
IBM Europe Automation Practice & Delivery Leader – AI SME (direct interview) X
IBM AI IBM Watson Explorer Architect - IBM Analytics Europe (direct interview) X
IBM Information Technology Architect – AI IBM Watson Dev Squad Team (direct
interview)
X
IBM Project Manager Application Automation (direct interview) X
IBM Chairman, President and Chief Executive Officer (Ginni Rometty) (public video
interview)
IBM AI Ethics Global Leader, Distinguished Research Staff Member -- IBM Research AI
(Francesca Rossi) (public video interview)
IBM Strategy & Operations Lead, MIT-IBM Watson AI Lab
IBM Research (Mark Weber) (public video interview)
Former General Manager, IBM Watson Solutions (Saxena) (public video interview)
Former Global Leader - Cognitive Visioning and Strategy - IBM Watson
(Bjorn Austraat) (public video interview)
Former Chief Technology Officer, IBM Watson Solutions (Sridhar Sudarsan) (public
video interview)
Former Senior Vice President of IBM's Watson and Cognitive Solutions (David Kenny)
(public video interview)
IBM Watson reports and white papers (7)
Table 3.2: Quantitative Details of Interview and Document Data
3.5 Analyze
Following data collection, a thorough examination, classification, and organization of the
information will follow. The research seeks to build propositions that contain the meaning of
the objectives of the study that motivated the data collection. A proper analysis employs all
the information gathered, evaluates rival interpretations and explores them in-depth, focuses
on the case study’s most significant features, and draws on researchers and experts' prior
33
knowledge in an impartial and unbiased manner (Yin, 2018). A logical model briefly describes
the entities and relationships between the entities under investigation. The research conducted
an inductive analysis of the data through techniques borrowed from grounded theory (Glaser
and Strauss, 1967) and research into themes and forms of aggregation (Gioia et al. 2012).
The research has then proceeded to the “In Vivo” (Strauss and Corbin, 1990) coding of
the identified and aggregated concepts. First-order coding (Van Maanen, 1979) uses single
descriptive quotations to focus on critical concepts. As for data coding, the analysis explored
the relationships between concepts in order to highlight the emerging framework. The
technique used does not follow linear processes but recursive structures where the collected
data are gradually refined and contextualized (Locke, 1996). The data structure, as shown in
Figure 3.1, identifies the first-order key concepts, second-order key themes, and the three
dimensions of aggregation (Strategy, Features, and Outcomes):
Figure 3.1: Data Structure
Following the directions of Lincoln and Guba (1985), the research followed several steps
to ensure data reliability. The first action concerns the meticulous approach of collecting and
analyzing data, with the audio recording of all the information collected, interview transcripts
34
(see Appendix B), and the organization of data through key concept identification. The second
action concerned the splitting of the data collected in two stages in order to ensure a first
general view (level 1) and a deep dive on the topics under investigation (level 2). A third
action concerned the comparison of the concepts found in the interviews with public domain
documentation to detect any confirmations or discrepancies.
3.6 Report
In order to report case study results, the researcher will select the information to be
included in order to highlight the most significant results. Also, a practical analysis of the
results allows defining optimal forms of interpretation (Yin, 2018). The report will be
developed throughout the course of the case study and will be organized based on the
characteristics of the audience who may not be an IT expert. Therefore, the study will not go
into technical details. The reporting will highlight how the study will contribute to enriching
existing knowledge and developing new knowledge.
In the data analysis process, the main activity was the comparison and triangulation of
data. The detailed comparison of the contents allows to research similarities and discrepancies
between them (Busse, 1994, as cited in Böhm, 2004). The analysis organized the data coding
to facilitate its understanding, interpretation, and synthesis to develop a detailed picture of the
issues under investigation. The coding allowed the researcher to define a list of key themes
with an explanatory text collected from the sources examined. In the beginning, the analysis
identified codes directly linked to the data collected. The codes collected were first provisional
and then became increasingly specific, differentiated, and abstract during the analysis. When
the codes reached a good level of abstraction, they were organized into categories. The
operational approach used in coding follows the "open coding" procedure (Böhm, 2004). The
data are broken down in order to derive a series of concepts. Concepts are represented through
quotations, which are short text passages gathered from interviews and documentation.
35
In order to identify valid and relevant codes, the research has carried out an "in vivo"
codification. “In vivo” (Strauss and Corbin, 1990) coding consists of the elaboration of
quotations taken from interview transcripts and public domain documentation related to AI
best-practices at the MNE researched. In order to facilitate this process, initial training of the
interviewer on the specific topics studied allowed the researcher to have a thorough
understanding of the significance of the concepts expressed during the interviews. The open
coding used has been a continually expanding procedure, which adds meaning to the
interpretative text by adding new levels of observation of reality and new perspectives to be
pursued (Böhm, 2004). In order to preserve a holistic view of the issue under study, the
aggregated concept was continuously revised, ordered, and evaluated to obtain a
homogeneous and relevant outcome. By giving relevance and priority to the issue under
examination, the analysis selected the most important concepts and discarded those considered
minor or irrelevant for the research development. The research linked aggregated concepts to
trace an interpretative, effective, and coherent model of research. The conclusions of the study
outlined two propositions, opening up new horizons and questions on the issue that could be
the subject of further investigation.
36
4. FINDINGS
This chapter presents the research results through the processing of the data related to
the subject of AI applied to KM with particular focus on the experience gained by a leading
company in the field of International Business and IT. The research highlights the results
through a framework of synthesis and comparison with the main studies on the subject. Data
analysis has enabled the researcher to relate the three aggregate dimensions and to identify a
"Data Process" model (see Figure 4.1), which highlights the primary "Strategies" that drive
companies and individuals to apply new "Features." The "Features" are those tools and
techniques of AI that allow obtaining significant outcomes for the evolution of Business and
KM.
Figure 4.1: Data Process
4.1.1 Main Strategies
The starting point of the interrelationship model identified among the concepts elaborated
concerns the strategies that prompt companies towards decisions that will have an impact on
the market, products, services, behaviors, and expectations. The two main strategies concern
the choice to invest in the development of AI technologies, and to move all technological
processes towards the use of the Cloud, thereby encouraging the geographical distribution of
37
data. These two main strategies, peculiar characteristics of IBM, are also common to many
large companies that see these two elements as the key to their corporate vision. In order to
implement these two AI strategies distributed on the Cloud, it is necessary to define how to
accomplish these strategies.
4.1.2 Features: Company Perspectives and People Perspectives
The “Features” that define how to implement the two main strategies follow two
perspectives. The “Company Perspective” feature represents the key elements that drive
companies to use AI tools. The first element concerns the transition that companies make
towards the digitalization of information and knowledge. The second element refers to the
gradual integration of AI within all business processes. Lastly, the third element involves
opening up towards innovation and reassessing operational methods and tools in every
business process. The “People Perspective” feature represents the elements that impact on
people’s activities, the tools they use, the behaviors they take, and the results they obtain. AI
is no longer a mysterious and complex black box, but easy to use tools that improve the overall
standard of living, and consequently, the performance of those who work with them. AI
adoption improves decision-making processes and complex problem resolution by offering
systems capable of gathering new needs and insights that would have remained hidden and
unexplored without it.
4.1.3 Outcomes: Business Perspective and Knowledge Perspective
At IBM, the systematic application of AI leads to tangible and relevant outcomes. The
research has highlighted the experience gained by SMEs in their work activities. The results
revolve around two macro perspectives. The "Business Perspective" outcomes represent the
results that have a direct impact on the company's business processes. AI has led to the
identification of new ideas and has opened up new business opportunities. These experiences
have shown substantial economic benefits “with an average operating margin of 10%”
(Forrester, 2019). In a direct interview, IBM Europe Automation Practice and Delivery Leader
38
explained that through the use of Watson technologies, “the manager can make more use of
knowledge and create more content." Besides, the interviews have touched upon issues related
to cost savings. AI systems can perform faster and more accurate automated operations than
human beings. The interviewees have assessed these aspects positively, highlighting a more
efficient allocation of human resources. In other words, people will no longer engage in
repetitive tasks but will be involved in more qualifying activities.
The "Knowledge Perspective" outcomes represent how AI revolutionizes KM through an
innovative, effective, and continuously evolving approach. AI available on Cloud favors
knowledge dissemination, as evidenced by several interviewees. An IBM Senior Managing
Consultant claimed that "the computing capabilities of the hardware […] and the possibility
of sharing them on the network through the Internet, […] the cloud itself, and […] the richness
of statistical models and artificial intelligence that IBM develops for each case of application,
are combined." An IBM report confirms that “organizations can handle structured and
unstructured data in one platform, and they can capture and share models, dashboards, and
notebooks. Data scientists save a significant amount of time on finding and preparing data"
(Forrester, 2019). Former IBM Watson Solutions’ General Manager, Manoj Saxena, asserted
that AI could manage the “knowledge curve for humanity,” allowing machines to capture and
store experts’ current knowledge and experience so that future generations can learn and
benefit from their insights (TEDx Talks, 2013).
When working with massive volumes of both structured and unstructured information,
such as pictures, video, or audio, doubts arise as to the extent to which companies take data
privacy seriously. Past scandals (e.g., Cambridge Analytica) have shown people worry about
companies being fair, accounting for safety and the impact of the use of personal data on their
lives, and transparent. They demand organizations to be open regarding the way they collect
and manage personal data. The case study found that companies such as IBM carefully follow
rules that allow them to comply with ethical codes to protect customers and employees:
39
"Personal data can be controlled completely. AI with IBM Watson looks at who needs
information, then if the person has it in excess, then what is the level of content that the person
has, and what is the time frame for which that information needs to be provided.” (Interview
with IBM Europe Automation Practice & Delivery Leader, 2019). The framework below
outlines the most critical quotations collected from direct, semi-structured interviews, as well
as external documentation, grouping them into key concepts that summarize and interpret the
data collected
4.2 Exploratory research propositions
The analysis of the data collected through interviews with SMEs, interviews, public
domain speeches, and documentation of AI applied to KM has allowed to develop five
propositions that follow the Data Process model (as shown in Figure 4.1). Following a five
iterative steps framework, the research propositions illustrate the forward-looking and positive
impact of AI deployment of KM processes for organizations and people. A data supporting
table (see Appendix C) expands the themes dealt with in the exploratory research propositions.
PROPOSITION 1: Companies that implement robust strategies to support the use of
AI on cloud systems embark on an ongoing and sustained transformation process that will
yield concrete and significant positive results to their core business and KM processes.
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Figure 4.2: Step 1 – Defining a robust strategy
IBM has defined a clear strategy of developing AI on distributed cloud networks.
IBM’s vision is to develop a centralized platform of integrated services accessible by anyone
on the Cloud through the AI platform IBM Watson. IBM Technical Solution Architect defined
the nature of IBM as “a company founded on two main principles of information technology,
one is the principle of the cloud, and the other is the principle of artificial intelligence." IBM
has defined a strong corporate "vision" in which AI and Cloud computing are critical elements
for business development. The strategies outlined by IBM represent the way to achieve the
objectives set out in the vision.
Some SMEs have emphasized IBM’s strategic imperatives during semi-structured
interviews. IBM Information Technology Architect describes IBM Watson “as a fairly
centralized platform with precise strategies implemented centrally, […] a whole series of
services […] that can then be put together to build solutions that expose precisely intelligent
capabilities." IBM desired to develop a platform in which anyone “has all the tools needed to
do artificial intelligence,” according to an IBM Client Executive. The systematic use of AI in
business processes, which IBM has implemented for several years now, indicates that these
strategies are bringing tangible results. The same IBM Client Executive claimed: “The fact of
41
having believed in advance in the transformation that was able to bring cognitive artificial
intelligence to the time of machine learning, gave us a very good advantage in competitive
terms.” A strong belief in its AI and Cloud strategy allowed IBM to position itself “a couple
of years ahead” of its competitors, as deduced by IBM Cloud Executive.
In addition to AI, IBM's strategies are moving towards the use of cloud computing,
which allows data dissemination and advanced cognitive analysis tools to anyone, anywhere
in the world. Cloud computing is a revolution in the field of an industry historically very tied
to the strength of its brand. IBM’s cloud and hybrid cloud strategies are to develop “a platform
that is both distributed to [IBM’s] customers and to [IBM’s] centers,” in which everyone’s
knowledge is critical “to create the best possible service for our customers” (IBM Cloud
Executive). The acquisition of American software company Red Hat, Inc. by IBM proves the
abovementioned statement and the words of its CEO, Ginni Rometty, confirm it. Rometty
contended: “We actually had to do a lot of work around the IBM cloud private which is what
Watson runs on […] Red Hat is coming up and so this allows it to move anywhere out there.
This is a big piece […] of hybrid cloud which you've heard me say we think that's a trillion-
dollar market and we'll be number one in it so that gives you a good feeling” (CNBC
Television, 2019). A strong strategy requires substantial investments, even in times of
economic crisis.
For several years, IBM has invested large sums of money in AI and Cloud R&D. Both
semi-structured interviews, documentation, and publicly available interviews illustrate the
nature of IBM investments, thereby facilitating access to the Cloud and AI market through
acquisitions and partnerships. Besides IBM’s acquisition of Red Hat, Inc., IBM has been
investing more than $240 million in a joint effort with the Massachusetts Institute of
Technology (MIT) to set up a new AI laboratory in which leading academic and industry
experts work together to facilitate knowledge acquisition (IBM Corporation, 2017). The
mission for this ambitious project is “to improve speed by orders of magnitude without
42
sacrificing accuracy” as IBM Strategy & Operations Lead, Mark Weber claims in a public
domain interview (RE-WORK, 2018). In addition, John E. Kelly III, Senior Vice President
and Director of IBM Research argued: “AI systems […] will require new innovations to tackle
increasingly difficult real-world problems to improve our work and lives” (IBM Corporation,
2017). Through this ambitious strategic partnership with MIT, IBM aims to explore the
economic and social benefits of AI in advancing knowledge acquisition to tackle societal
problems and improve the human condition.
PROPOSITION 2: AI implementation orients companies towards digital
transformation, changing business processes, accelerating knowledge dissemination and
sharing, thus benefiting from better use of intellectual capital.
Figure 4.3: Step 2 - Drive Company Transformation
When a company believes in its vision, it projects the entire organization towards the
practical application of its strategies. Technological tools cannot be implemented without the
adaptation of business processes. Several SMEs highlighted both IBM’s drive towards the
digitalization of enterprises and the pervasiveness of the AI system within a single platform,
integrated into all business processes and, for this reason, significantly more efficient. The
advantages of integrating AI into business processes are highlighted in an interview with an
IBM Project Manager: “The business process workflows become more intelligent because
43
IBM Watson integrates into workflows by adding AI where it is needed.” In order to upgrade
and improve business processes, organizations must change their internal processes, by
identifying focus areas where to implement AI technologies and take action in terms of
efficiency, speed, and accuracy. Organizations need to understand which workflows,
resources and, above all, the skills they need to be able to use AI tools effectively and trigger
business optimization.
Digitalizing and automating workflows enable to redesign business processes through
new information management, thus providing a different way of conceiving day-to-day
operations. IBM Client Executive describes the IBM Cloud environment as “an infrastructure
enabling the company to make a transformation to the digital world.” At the same time,
digitalization is a trend that is affecting gradually many organizations. An IBM White Paper
(2019) highlights that over the years, "firms are embracing more data sources on the cloud,
combining it with existing data on-premises, and applying analytics and AI on the Cloud to
drive new insights.” Companies oriented towards digital transformation understand that data
digitalization and structuring add value to their core business because people can take directly
the information needed, as IBM AI Cognitive Delivery Manager explains. Digital
transformation also helps organizations speed up document management processes by
focusing only on the most important data (IBM AI Cognitive & Analytics Consultant) and
“perform[ing] analytics on […] large datasets to understand which dataset is corresponding to
another, adding more insights” (IBM Europe Automation Practice & Delivery Leader).
Cloud and hybrid cloud strategies significantly facilitate access and analyze large
amounts of information by data scientists and reduce costs. A Forrester study commissioned
by IBM (2019) highlighted the valuable role of data scientists in providing insights to
organizations that use them in their strategic processes. Besides, data scientists can benefit
from using dashboards available in IBM Watson environments to communicate and share
insights more effectively with company decision-makers. IBM Technical Solution Architect
44
Cloud & AI Cognitive described the role of the data scientist in business process optimization
by minimizing costs and ripping the benefits of efficiency by exploiting the platform’s
algorithm. Therefore, data scientists “can find those innovations and those steps that allow us
to make certain business processes more effective and more efficient”, and by analyzing
various users’ behavior, they can “understand how to improve and predict further action.” As
data scientists can now access, use, and analyze larger data sets, their contribution to
companies’ strategic processes improves remarkably.
PROPOSITION 3: AI applied to KM helps people make better and more objective
decisions, allows them to solve complex problems better, and improves their work
performance.
Figure 4.4: Step 3 – Reinforce People Transformation
In order to make organizational change processes more efficient and effective, companies
must involve all stakeholders who are interested in using the system at different levels, such
as managers, employees, customers, suppliers, and business partners. An IBM Client
Executive, speaking about the application of AI within business processes, argues that IBM’s
“strategy is always to be able to support the human being in his decisions." Tools and
processes, even if augmented by AI, can only be implemented correctly if people adapt their
behavior to the new context. In order to make the most of AI, it is crucial to understand how
45
the system “learns” and “improves” over time, overcoming challenging business problems,
and transforming them into opportunities. An IBM Senior Managing Consultant and Research
Scientists argues that when collecting a large amount of data, "in encoding its relevance to the
specific decision-making domain and in allowing also a human understanding […] a greater
decision-making capacity is allowed, because […] [data is processed] according to
classifications that are then screened by the experience of managers and [SMEs]."
IBM Watson allows people to make more sound and effective decisions to complex
everyday challenges, which leads to improved work performance. An IBM Senior Watson AI
Consultant argues that "the decisions of the professional [are] more facilitated by more
information." KM processes improve, because the AI system allows people to “access to
unstructured data, and can learn from small data sets, […] and helps to increase its value by
analyzing it more deeply,” says an IBM Project Manager. The research exposed these
advantages not only at the corporate level but also in other domains, such as the medical field.
David Cole, IBM Watson Health Innovation Lead for Europe, in a conference at the Oxford
Union, discussed the significant contribution of AI to medicine (OxfordUnion, 2016). Rob
High, IBM’s Vice President, argued that “by showing where the information and
recommendations are coming from, Watson expands what human doctors can do and provides
them with resources to make the best decisions for their patients” (Morgan, 2017). AI thus
enriches the wealth of knowledge of doctors, helping them to make more accurate decisions
in a shorter time frame.
Strategies selected to adapt business processes must take into account people’s
motivations and expectations. Management strategies and actions must support and reinforce
cultural and behavioral change in order to achieve tangible benefits. The research has shown
that one of the reasons that promote people's acceptance of AI systems is the ease of use of
IBM’s cognitive systems. In several direct semi-structured interviews and a public domain
talk, SMEs agreed on the user-friendliness of IBM Watson and argued that end-users require
46
only basic IT knowledge and brief training. An IBM Watson Explorer Architect argued that
“it [is] a question of getting comfortable with them and the greater challenge is giving accurate
and effective data to train them." Some SMEs argued that the AI platform’s simplicity of use
depends on the type of application. An IBM Senior Watson AI Consultant argued that “some
[applications] can be used even if you do not have specific knowledge, and then it is enough,
others instead require the technical knowledge.” Nevertheless, the simplicity with which
people can access and use of AI systems in cloud networks eliminates any psychological
barriers and drives people to accept change, experience it positively, and change their habits.
PROPOSITION 4: AI-augmented business processes and people's behaviors transform
and improve KM in terms of information collection and dissemination, data processing, and
insight generation while ensuring data privacy and protection.
Figure 4.5: Step 4 – KM improvements
MNEs have understood that the information that can be collected and processed today
is much extensive, complex, and articulated than ever before. The evolution of IT technology
in terms of computing capacity and data storage has allowed companies to develop systems
to manage knowledge in a much more advanced way. AI allows collecting every stimulus
from the environment, explicit forms, latent forms, insights, sentiments, and to develop new
knowledge. In a semi-structured interview, an IBM AI Cognitive Delivery Manager explained
47
that AI techniques help people to seek more-in-depth knowledge and insights, thus helping
people to extract concepts “at 360 degrees.” An IBM Senior Managing Consultant and
Research Scientist deepened this concept by clarifying that the AI system recognizes “insights
that the same human being accomplishes, but that then struggles to put together in correlation
between […] thousands and thousands of records of data." KM has changed dramatically over
the years. People interact with AI systems, organizing the information, and disseminating it
quickly and pervasively. MNEs that have understood the importance of these processes have
equipped themselves with cutting-edge tools to respond to new challenges in order to achieve
new and more ambitious goals.
IBM is implementing AI tools and capabilities systematically in the evolution of KM
processes, unleashing new ideas, and opening up new business opportunities. However, the
quality of the processed data may not always be adequate, and the training processes of the
learning machines may not provide clear, correct, or updated guidelines. Besides, the
processing of emotional signals, feelings, and perceptions may not lead to objective evidence
but may be conditioned by contingent factors. This is one of the issues addressed with the
SMEs surveyed during the second-level interviews. When asked about any adverse effects
deriving from incorrect or obsolete information that may arise when teaching IBM Watson,
several interviewees confirmed the presence of these potential risks. For instance, an IBM
Watson Explorer Architect argued that an effective governance system must be in place and
that end-users must decide which approach (e.g., machine learning, linguistic rules) is the
most appropriate for the training depending on the situation. On the contrary, IBM Europe
Automation Practice & Delivery Leader did not highlight any possible dangers when training
AI systems: “Watson is constantly learning. So even if the information is incorrectly input and
coded into Watson, it will be quickly rejected.”
AI processes large amounts of data, and the second level semi-structured interviews have
touched on the topic of data privacy protection. IBM Europe Automation Practice and
48
Delivery Leader ensured that "personal data can be controlled completely.” He also added that
“Watson looks at who needs information, then if the person has it in excess, then what is the
level of content that the person has, and what is the time frame for which that information
needs to be provided […] all of those things can be deployed to effectively make sure of
compliance to all regulatory bodies." Besides, an IBM Watson Explorer Architect added that
“if [a person does not] wish, IBM will not learn from that data." The direct interviewees, as
well as public domain documentation, expressed optimism and trust towards IBM’s data
protection and privacy, and the company’s full transparency towards data processing.
Francesca Rossi, IBM AI Ethics Global Leader, argued in a public interview that an AI system
is trustworthy when it “is not biased, is fair, is explainable, and the way uses the data of the
user is transparent” (ITU, 2018). Also, she guaranteed that IBM does “not reuse the data for
other clients or other tasks” (ITU, 2018).
Semi-structured interviews and public domain documentation demonstrate that correct
and responsible use of AI enhances business processes and people’s performance, leading to
improved KM processes. An IBM report (2012) mentions that Watson solutions can provide
significant support to data-intensive industries, by examining “high volumes of structured and
unstructured data”, providing “speed and accuracy of a response to a question or input
provided”, helping them “learn with every outcome or action taken”, and responding to
“critical questions that require confidence-weighted recommendations and supporting
evidence.” In a semi-structured interview, an IBM Information Technology Architect argued
that IBM Watson “allows [employees] to decipher all the non-traditional inputs that arrive,
[…] it allows [them] to schematize, classify and make accessible […] all the information that
is typically managed by people", leading organizations to maximize the intellectual capital
brought by each individual. Furthermore, an IBM Senior Managing Consultant and Research
Scientist has argued that IBM AI technology allows end-users to achieve deeper meaning by
49
navigating through concepts, making the search more aggregated and finer-grained, so that
people can attain the intrinsic value of that particular type of information.
PROPOSITION 5: Business transformation and new ways of managing knowledge
through AI capabilities lead companies to achieve positive results in terms of generation of
innovative insights, resulting in increased revenue, cost reduction, and resource optimization.
Figure 4.6: Step 5 – Improved business performance
The most evident business results deriving from the use of AI tools mainly concern
cost reduction deriving from a better allocation of HR, the increase of market shares in an ever
more digital and globalized world, and growth of operating margins that allow for new
investments in the context of continuous technological evolution. In a study commissioned by
IBM, Forrester (2019) emphasizes the greater effectiveness of data science projects, which
generate millions of dollars in revenue and savings for organizations. Enhanced access to
information allows data scientists to deliver higher profits to organizations’ projects: “With
an average operating margin of 10%, this equates to an incremental $750,000 in operating
margin per project" (Forrester, 2019). The research points out cost reductions in infrastructure
and administrative costs, considerable time savings, and improved employee’ productivity.
Besides, new ideas generation allows organizations to seize new business opportunities and
new markets where multinationals can leverage their competitive advantage. In a public
50
domain talk, Manoj Saxena discussed the benefits of the AI platform in terms of empowering
the way people think, act, and learn (TEDx Talks, 2013).
Some SMEs and publicly available documentation have highlighted the benefits of AI
in HR processes, which provides faster and more accurate information analysis, thus speeding
up personnel management systems. IBM Technical Solution Architects posited that AI “helps
to find the right match between people's skills and the job”, and contended that automatic
systems help people to “trace [employees’] professional evolution”, suggesting them “what
are the most appropriate things […] to put in [their] curriculum […] on the basis of real
evidence." In a public domain interview for the University of California, Berkeley, former
IBM Global Cognitive Visioning and Strategy Leader Bjorn Austraat draws a link between
cognitive transformation and HR transformation, arguing that AI allows for “a transformation
of functions, individual functions, but then also of the overall enterprise […] from the
complete employee and engagement lifecycle” (Berkeley Haas, 2017). An IBM document
illustrates employee cost savings of AUD 10 million by an Australian energy company. These
savings stemmed from the use of IBM Watson, which allows "faster access and more intuitive
analysis of [......] records" leading to "a 75% reduction in team time spent reading and
searching data sources" (Banerjee, 2017). By looking at the organization holistically, AI
allows to improve business processes and models and reduce costs, allowing organizations to
gain a competitive advantage.
The systematic use of cognitive systems within business processes brings greater
efficiency and unleashes positive effects on business and people. When observing a computer
that responds to a call center and provides practical and appropriate responses, people may
fear that automation will lead to downsizing and layoffs. Many interviewees surveyed on this
topic and IBM documentation related to HR believe that the use of AI frees the human being
from repetitive and mechanical activities and shifts her skills towards more valuable tasks. An
IBM Senior Managing Consultant & Research Scientist says that IBM Watson helps HR
51
practitioners to free up resources for that type of work and move professionals to other areas
where they are most useful for the organization. A document by the IBM Smarter Workforce
Institute reports that HR savings from AI enable organizations to invest in further AI
advancements. As a result, HR management will be able to "develop strategic skills, create
positive work experiences, and provide outstanding decision support for employees,” says an
IBM report (Guenole and Feinzig, 2018). The positive effects on business results and people's
motivation significantly influence corporate strategies, thus triggering an iterative process that
stimulates new investments in AI, thereby promoting the organization’s vision at multiple
levels within the company.
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5. DISCUSSION
The case study has explored critical aspects related to how MNEs can gain a
competitive advantage in the implementation of KM practices through AI. Through the
deepening of a real and practical experience of an MNE that regularly uses AI techniques and
capabilities to manage its intellectual capital, the research highlighted the positive impacts of
these best practices on people and business results. AI is transforming the way information is
collected, processed, and distributed. This has led to the generation of new levels of
knowledge that foster new ideas and new business opportunities. The discussion section
relates the results obtained with the current literature on AI applied to KM.
The first proposition argues that organizations undergo an ongoing and sustained
transformation process by implementing AI on cloud systems, leading to concrete and
significant positive results to their core business and KM processes. The literature recognized
that having robust AI strategies is a fundamental starting point for any organization. Previous
research by Fowler (2000) and Tsui et al. (2000) has highlighted that AI strategies rely on
tools that require substantial investment in R&D. A recent McKinsey Global Institute survey
of 3,000 executives in 10 countries and 14 industry sectors identified five critical strategies
for maximizing the benefits of AI: plan growth, invest in technologies and people's talent, be
ready to review strategic goals, build a robust digital base, and create an AI ecosystem (Bughin
and Hazan, 2017). Mangelsen and Alexander (2019) argue that in the United States, more and
more companies are investing in AI's digital transformation strategies. However, a study by
MIT reports that more than 95% of firms are still not adopting AI technology to reinvent
business processes (Bughin, 2018). Unlike IBM, many companies have not yet defined robust
strategies. Moldoveanu (2019) illustrates that organizations must bridge the skills and
communication gap between non-technical decision-makers and AI teams in order to solve
pressing business challenges.
53
The second proposition highlights how AI application in KM processes drives
companies towards digitalization, improves the information dissemination, and yields clear
benefits to organizations' intellectual capital. Existing research agrees that companies are
making a transition to digitalizing KM processes by applying AI capabilities when processing
large amounts of data (Paschek et al., 2017). As confirmed during the interviews and public
domain documentation, the literature also illustrates how digital transformation drives
companies towards digitalization and new forms of knowledge processing (Avdeenko et al.,
2016). A report by Capgemini (2017) on the digital revolution illustrates how this aspect
radically changes every business process.
The literature agrees that in order to obtain the best AI-driven business results based
on AI, the action of data scientists is not enough. Digital transformation must take place
throughout the whole organizational environment. Kruhse-Lehtonen (2019) discusses how
digitalization can create greater efficiency and productivity: business leaders must set
ambitious but realistic goals, look after people and give them adequate training, identify the
most appropriate digital investments, and implement operational models to organize data and
AI effectively. Managers, and companies more in general, benefit from their digital
transformation not only through increased revenues and savings, made possible through
production improvements but also through more effective use of data and knowledge (Botha,
2019). The IBM Watson case study illustrates how an MNE has responded to the need, already
expressed in the literature, to integrate AI tools and techniques in its organizational processes.
The exploratory research has confirmed that digitalization allows for greater dissemination of
knowledge and the optimal use of intellectual capital.
The third proposition highlights how the AI’s implementation processes involve people’s
way of working, how the organization manages its human resources, and how managers make
decisions and solve problems. Trends in the literature show a broad consensus on HR
processes improvements, better KM processes, and better document organization and in-depth
54
analysis thanks to AI. Liebowitz (2001) discusses the importance of AI in knowledge
discovery and data mining approaches, which could be implemented inductively to find
relationships in repositories for new knowledge creation. Indeed, many large companies have
made investments in AI R&D a priority for their core business. Mortensen (2019) stressed the
importance of developing emotional thinking and higher-order expertise to cope with the most
sought-after skills of the future. Botha (2019) highlights how knowledge workers will need to
re-skill their jobs, personalizing, and sharing contextualized knowledge in support of digital
transformation. As a result, a more careful collection, selection, and enrichment of knowledge
will allow them to make better decisions.
The fourth proposition describes how the implementation of AI can transform the way of
managing business communication by processing vast amounts of information and generating
new knowledge while preserving users’ data privacy. Gandon (2002) has focused on how AI
can exploit the breadth of human knowledge, starting with information shared via the web.
The world changes, and the amount of data being shared and analyzed increases. The
application of AI in business processes enables large amounts of data to be managed
effectively (Strauß, 2018; Mangelsen and Alexander, 2019). The change brought about by the
use of AI in KM is addressed in the literature mainly in terms of opportunities: document
management optimization, information sharing and research (Bizirniece, 2011), and support
for people to make more informed decisions more quickly (Saravanakumar, 2019). Semi-
structured interviews from IBM AI experts, as well as public domain videos and
documentation, illustrate how the practical application of AI in KM helps people make better
decisions and solve complex problems.
The data processed by AI are many and also cover unconventional sources, such as audio,
images, and movies that provide additional information, but that may touch aspects of data
privacy. IBM's strong focus on data protection and confidentiality prompted the SMEs
interviewed to stress that this is an issue under control and does not seem to raise any concerns.
55
Although they claim IBM has equipped itself with all the necessary tools to ensure its
stakeholders data protection and privacy, the literature on these issues is inclined to highlight
scenarios that are not always positive and highlights the need to find tools and techniques that
promote transparency and accountability in data-based decision-making (“Big Data Senior
Steering Group”, 2016). Some articles consider the protection of personal data as a weak point
that calls into question the security aspect of AI systems, as these may not apply the necessary
control measures to protect customers and employees (Cate and Dockery, 2019; Shaw, 2019).
Although they recognize the significant benefits of AI at the systemic, corporate, and
individual levels, the authors point to severe gaps limiting data protection frameworks, which
are inadequate to protect people's privacy and promote innovation in the data-based economy.
The fifth proposition indicates how AI applied to KM can lead to business innovation,
triggering new ideas and insights, which will enable organizations to obtain a competitive
advantage in revenues, cost savings, and resource optimization. The literature agrees that AI
opens up the generation of new ideas, allowing companies to leverage the hidden value of
their data. The U.S. Government, in its “Federal Big Data Research and Development
Strategic Plan” (Big Data Senior Steering Group, 2016), recognized the potential of advanced
computing and data analytics, and increasing investments in data collection and management
processes to create new products, services, and capabilities. AI innovations will allow
establishing knowledge bases of information deriving from the interrelations between
structured and unstructured data. Besides, by supporting inter-organizational knowledge and
learning, managers can tap into the knowledge and insights of a similar firm from the same
industry to solve a particular business problem (Venugopal and Beats, 1995). There is also a
broad consensus in considering AI as a road to innovation and the identification of new
business opportunities. On the path towards sustainability, AI will assist organizations to
innovate in the way they create, maintain, and manage human capital (Mercier-Laurent, 2014).
56
To sum up, the IBM Watson case study has applied the theoretical arguments found in the
literature to the analysis of a leading MNE that implements AI in its business processes. The
five propositions developed from the interview findings and company documentation,
depicting a five-step iterative diagram in which each component acts on the other as a stimulus
and activates a positive spiral in which change takes place and extends to every business
process. Robust strategies act on the operational implementation of AI, justify its evolution,
and strengthen its pervasiveness. Strategies and process changes affect people, their beliefs,
and their behavior. When the organization and its employees implement critical digital
transformation strategies, the implementation of new AI tools provides new ways to collect,
disseminate, and generate new knowledge. By putting these actions into practice, the positive
results are evident, both in terms of economic returns and in terms of improved work
performance.
57
6. CONCLUSION
The study has deepened the experience gained by an MNE in the field of AI applied to
KM. The research has shown that AI systems can improve considerably the way people
collect, analyze, and share information. By applying innovative forms of human-machine
interaction, companies can achieve positive results for their core business and their
stakeholders’ quality of life. The case study allowed us to understand how KM has changed
in an MNE that implements AI tools in its business processes: people fully exploit their
intellectual capital, and AI systems generate new knowledge and enrich corporations'
knowledge base. Computers do not replace people but integrate them and develop their
potential so much so that today, companies refer to AI as "Augmented Intelligence" (Jablokov,
2019) that renovates decision-making processes and facilitates complex problems resolution.
Through the IBM Watson case study, this dissertation offers a virtuous path that MNEs can
follow. The model starts from the setting up of a robust strategy, a vision that can trace the
desired future in which increasingly sophisticated cognitive systems improve the capture and
management of knowledge within MNEs. The study also stressed that aspects of personal data
protection cannot be underestimated and must follow particularly strict rules and forms of
control.
The most critical element that emerges from the study is the application of AI tools in the
daily practices of IBM employees and managers. Much of the current research on AI is limited
to describing its potential and outlining possible future scenarios. Interviews with IBM SMEs
illustrated how IBM Watson makes it easier to find information, share knowledge, and
develop new ideas and opportunities. From a technological point of view, the case study
stressed the ability of intelligent systems to capture meaningful and hidden information, such
as sentiments, emotions, and insights. From an organizational point of view, one of the most
important aspects is the mental approach of the people interviewed who regularly use these
58
systems, consider them an integral part of their work, and are not surprised to talk with an
automatic chatbot to get the information they need.
This dissertation did not consider specific technical aspects of IT solutions that would
have allowed the researchers to go into the operation of AI tools, advanced methods of
machine learning, and possible future technological developments. Besides, this thesis did not
consider quantitative aspects related to research, such as the amount of investment in R&D,
the increase in revenue from new business opportunities, savings on personnel costs, return
on investment. An area of possible future work relates to the measurement of quantitative
aspects of positive business results stemming from AI implementation in KM, such as by
calculating the ROI of AI (Return of Investment of Artificial Intelligence), as proposed by an
Accenture research report (Mannar, 2019). Another point not covered by this manuscript
concerns the analytical comparison with the experiences of other MNEs that have undergone
similar transformations in the field of AI and KM. The comparison between MNEs could
highlight significant differences, analogies, and consequent results on companies’ corporate
strategy and human capital.
Quantitative and comparative studies will have the opportunity to take inspiration from
this qualitative study and build a robust model of AI-best practices applied to KM. The areas
in which it would be possible to develop future studies can concern the deepening of
technological potential, with the possibility of extending the discussion to research in the field
of robotics and automation. This dissertation has identified a model that can be of inspiration
and emulation for managers and business leaders of all companies that have not yet
experienced the implementation of AI in their business processes and KM practices.
59
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APPENDIX A: Semi-Structured Interview Questionnaires
• First level interviews:
Question 1: What is your role at IBM?
Question 2: Artificial Intelligence at IBM has always been one of the strengths of its
commercial offering. In particular, IBM Watson has always been at the forefront of
computer systems by answering questions posed in natural language. What is IBM's
strategy in this critical sector today?
Question 3: Using Artificial Intelligence with IBM Watson is a great business opportunity.
How is IBM organized to respond to different market needs? What is the primary role
of IBM Research Centers and IBM Competence Information Centers in the world?
What are the principal investments by IBM and the future scenarios?
Question 4: Let us dive a little deeper into the components that characterize IBM Watson.
What are the particular elements of different solutions such as IBM Watson Discovery,
IBM Watson Knowledge Catalog, IBM Watson Machine Learning? Which tools fall
within the scope of the cognitive approach, analytics, and data mining?
Question 5: Now let us explore the relationship between Artificial Intelligence and Knowledge
Management. Since we have to manage large amounts of data, Big Data, how can IBM
Watson be used? What are the main benefits that a company can derive from it?
Question 6: At IBM, the knowledge, experience, documentation, processes, skills gained by
each person can become a valuable corporate asset. How can the use of Watson
facilitate the sharing and use of relevant information for a company's strategy?
Question 7: With computer systems increasingly distributed, how do IBM Cloud and
Multicloud service strategies develop with the management and sharing of knowledge
and the use of Artificial Intelligence?
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Question 8: Many companies use their corporate intranet for many activities like research,
presentations, complex problem solutions, and decision support. How is it possible
that IBM Watson can be used to optimize and make these processes more effective?
Question 9: IBM's international dimension may be a burden from the point of view of the
flexibility of marketing actions and the rigidity of bureaucratic processes. At the same
time, it could also be an advantage in terms of knowledge of the different markets in
the world, the specific needs of customers in particular geographical areas and business
opportunities that can hardly be seized. How does Artificial Intelligence help the
organization and sharing of this information?
Question 10: Could you tell me about a real, concrete project of IBM Watson, using artificial
Intelligence applied to knowledge management, in which IBM has applied its acquired
"best practices" and developed a state-of-the-art solution?
Question 11: On the subject of Artificial Intelligence and its application in the field of
Knowledge Management, who are IBM's main competitors? What are their solutions?
Which other famous multinationals have similar experiences to those of IBM?
Question 12: In conclusion, are there any other topics, areas, or information you consider
relevant to the research that was not covered in this interview?
• Second-level interviews:
Question 1: What is your role inside IBM?
Question 2: The use of IBM Watson applied to Knowledge Management allows users to
analyze large amounts of structured and unstructured data and easily reach the
information they need. Could you tell me about your experience at IBM?
Question 3: IBM uses Watson’s cloud system to easily and immediately reach large
populations of employees and customers all over the world, therefore spreading large
amounts of information. Can you tell me what the main advantages of this strategy
are?
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Question 4: Corporate communication is increasingly interactive and unstructured: it uses
chat, images, audio and video. How does IBM Watson allow users to generate new
levels of knowledge and share information easily and effectively?
Question 5: IBM Watson uses processes that simulate the human mind through neuronal
networks. So, it uses a learning system, the so-called machine learning. Isn't there a
risk that in the teaching phase, even in an unconscious way, incorrect or obsolete
instructions are inserted? What could be the negative effects?
Question 6: What are the implications of personal data protection in the use of Watson and
what are the possible impacts in knowledge management?
Question 7: Is the use of IBM Watson by employees or customers complex or easy to use?
Does it require special technological skills?
Question 8: IBM Watson has transformed the way we manage the knowledge of employees
and managers. How was knowledge management handled in IBM before Watson was
used? So how was the corporate intranet used and how did the way to reach and share
information change after the introduction of IBM Watson?
Question 9: Did the use of IBM Watson optimize the use of human resources? And has it led
to "cost savings" on IBM’s part and has it been quantified?
Question 10: IBM Watson is a system that helps organize large amounts of data and find
complex answers. How is this system used to help employees and managers make
more informed decisions?
Question 11: How can people apply IBM Watson’s experience gained in some countries to
other geographical areas?
Question 12: Are there any other topics, areas, or information you consider relevant to the
research that was not considered in this interview?
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APPENDIX B: Semi-Structured Interview Transcripts
First-level interviews
1. IBM Client Executive AI SME
Interviewer: What is your role inside IBM?
Interviewee: I work in the area of the railways group and I have the commercial responsibility
for the client. I work in a team within IBM that oversees from a commercial point of view all
the offers that IBM has, among which there is certainly also the cognitive that is one of the
offers on which IBM is supporting so much, that is, the two offers on which IBM is supporting
are definitely the Cloud as an infrastructure enabling the company to make a transformation
to the digital world, but in particular the cognitive, and then the cognitive, as you know we
started well in advance of all the others including Google, Microsoft which are now a bit our
competitors. When IBM announced its strategy on Watson we were so early that no one
believed that our CEO Ginny Rometty, everyone thought that she had mistaken the strategy,
and then on the contrary we verified over the years that eventually everyone had to follow,
and at this time many areas are lagging behind us, because we are I think a couple of years
ahead of the others.
Interviewer: You have had that competitive advantage…
Interviewee: Yes, I mean, the fact of having believed in advance in the transformation that
was able to bring cognitive artificial intelligence to the time of machine learning gave us a
very good advantage in competitive terms. I'll tell you two important things from my point of
view. So the first is that IBM entered this world with a famous American quiz called
“Jeopardy!”, where there are people who answer questions, and IBM that had started many
years before with, with Deep Blue that was that supercomputer that was able to play chess
against the best champions of chess and then beat them and, by the way IBM has always had
this thing to get into these, let's say, challenges, in which demonstrated the skills of the
computer that could say, assist, help people to improve their lifestyles, let's say. In this race
“Jeopardy!” IBM participated with its cognitive supercomputer and showed that it was
possible to beat, compete let’s say, against people at the same level and answer, in a way,
clearly to the questions we asked in natural language, open, on topics of general knowledge.
I'll tell you something else, great in my opinion, that Watson is called this way because it was
made, thought up by the son of Watson, who is the founder of IBM, started from his son. This
idea of doing, of competing against, say, in questions of general knowledge is an idea that
came from an Italian engineer named Sicconi, who worked at IBM Vimercate, and Watson,
Watson's son has understood the potential of both the idea that this engineer, Sicconi, and his
idea, that he came to Italy to meet him, he went to Vimercate, met him, spoke with him, took
him to the United States with him and it was this man who then gave birth to this, this project
with which he participated in “Jeopardy!”. I had the opportunity to hear some interviews with
Sicconi, and I saw him once at a communication meeting in IBM where he talked about the
experience that IBM had to explain, for example he said when someone talks to you, ask, and
general knowledge that is already difficult in itself, because a machine is answering you, not
a human being, and then he asks you questions in which there is no possibility within the
question to understand, or hooks to understand what kind of answer you needed. And he said
that this was one of, let's say, of the greatest challenges that he had, he made the example, who
was the first to circumnavigate Africa right? He said there are thousands of these examples to
make, that is, it is not that you can go and answer every single question, you have to instruct
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the computer so that it understands how to use a, let's say, it seems that they had taken it from
Wikipedia, a Wikipedia to find within such an endless data base of information the correct
answer in a very short time to compete in a race, which of course it was made for purposes,
let's say, both promotional and for IBM's image.
Interviewer: Artificial Intelligence at IBM has always been one of the strengths of its
commercial offering. In particular, IBM Watson has always been at the forefront of computer
systems by answering questions posed in natural language. What is IBM's strategy in this
critical sector today?
Interviewee: Our strategy is always to be able to support the human being in his decisions. All
the things we have done in the medical field, is the idea that it is a tool that helps people, that
is, our idea that can, in some way, help to improve the world, and we think that the adoption
of artificial intelligence is the tool with which this world can try to address its main problems
such as, in medicine, I'll just give an example, we have a history of, was one of the areas where
IBM was first committed, our idea is basically to help doctors to have the best possible
information available to make a diagnosis but based on an analysis of both the examinations
and what there is in the world as knowledge, that it is as simplified as possible, and that they
can give their patients tools, but in general this is the strategy of IBM, of the tools with which
this world can improve.
Interviewer: Using Artificial Intelligence with IBM Watson is, therefore, a great business
opportunity. How is IBM organized to respond to different market needs? What are the roles
of IBM Research Centers and IBM Competence Information Centers in the world? What are
the principal investments by IBM, and what are the future scenarios?
Interviewee: Clearly at the beginning the competence centers were in the United States, now
of course IBM has made a major investment fundamentally in two areas, the first area was to
create a platform that is accessible to everyone that is IBM Cloud, within this platform one
has all the tools needed to do artificial intelligence. Our platform has all this payment policy
basically, all those who want to try to use the tools have the possibility to use them for free up
to a certain level of use, but they can test exactly if their idea put into a startup in the case of
a company that wants to innovate, can count on value. This platform is open with great
documentation, everyone can use it, and there are all the tools with which we put on the market
of artificial intelligence, and this was the first major investment of IBM. The second
investment is in people, we have, let's say teams, then in Italy, among other things, we are
particularly good because we have adopted, we have done projects of artificial intelligence,
we are operational not only in Italy but also worldwide, of which I was certainly one of the
promoters because we have done important things both on the railways group and Telco
operator and we are certainly the union of a platform that has all the tools to do artificial
intelligence but above all our people who know the business logic of our customers, we
believe that this is the winning combination, people first and then our Watson platform. We
have many centers, right now it is also difficult to tell you where they are, they are of course
in the United States, I will go in September to this center in Yorktown which is a research
center near New York and I go with this customer of ours and certainly we have them, we will
meet people that with IBM workers will show us what is the, let’s say, not only current but
also future strategy. The current one you can see, as I told you, in the Watson platform that is
now available in the Cloud, of course what IBM is preparing for the next few years is right
now in our laboratories that are around the world. IBM has laboratories around the world on
artificial intelligence. For example, IBM has decided that for the whole help sector, Milan will
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be the cognitive technology hub for Europe, an agreement has been signed between the Italian
government and Ginni Rometty so that Italy can be the hub for all the whole help sector.
Interviewer: Let us dive a little deeper into the components that characterize IBM Watson.
What are the particular elements of different solutions such as IBM Watson Discovery, IBM
Watson Knowledge Catalog, and IBM Watson Machine Learning, and which tools fall within
the scope of the cognitive approach, analytics, and data mining?
Interviewee: The machine learning algorithms that are the basis of everything, are open source
algorithms, that everyone has at their disposal, that you practically go on any, thirty seconds
you install the R platform or Jupiter that is a notebook and you can start using these algorithms,
right? Then of course there is all the data and what you want to do with them, so what is it
that differentiates IBM from another company, what it has built on these algorithms, right?
So, what is it that we make available on our platforms? I'll give you an example, we're working
now, from the client I work with, our idea is that you can create thematic areas where you can
talk to people by providing support services, almost always, obviously using neural networks,
but the basic element of reasoning and experience that we have made for our customers, the
neural network its work knows how to do it, a well-made platform must be able to attract the
problem of the neural network to the person who has the business problem and wants to solve
it. Even in these days we have made design thinking with people of our customer who have a
deep knowledge of the customer's behavior and his, let's say, customer journey. So basically
we are able on the one hand with our people to understand exactly how the customer behaves
in the various stages of his customer journey and our platform that is based on neural networks
but has built on it a whole model of use and simplification of the complexity of the network,
you are able to instruct, to pull your network, to be able to bear a dialogue with your customer
in natural language without you having to have the knowledge, this gives you basically two
advantages. That you can bring to the table people who are experts in the domain you are
dealing with, right? So, in our case we talk to people who know everything about the topic of
travel, the behavior he has in the customer when he has to make purchases, right? But on the
other hand no one goes to put their hands directly into the neural network, there is an interface
of level understandable to the human being of business that knows the dynamics of behavior
of the customer and easily instructs, but above all does the training because you do some
testing and within a few weeks you are able to reproduce within a platform of artificial
intelligence the behavior and expectations that your customer has, because you are able to
build easily the model of interaction with your customer. Therefore, our idea is to know how
to build on the concept of machine learning and artificial intelligence easy tools for our
customers that are able, with ease, to create areas where they, with their knowledge of business
and not technological, not of artificial intelligence and not cognitive, are able to produce value.
Interviewer: At IBM, the knowledge, experience, documentation, processes, skills gained by
each person can become a valuable corporate asset. How can the use of Watson facilitate the
sharing and use of relevant information for the company's business?
Interviewee: We believe in what we're doing as IBM. Our way of sharing our experience of
projects around the world is certainly one of the elements of differentiation of IBM, because
when I do a project I make it available to IBM for information purposes, so let's say, and all
the others do the same thing, so when you have requests, we have cognitive engines in which
we formulate requests to have, say, a support of information, experience, project, business
driver, of customer problems, and this platform that I use regularly, through a cognitive engine
pulls me out around the world all the experiences similar to mine or all that I need to build my
experience so I have a worldwide knowledge but I do not have to go look through all the
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documents, I am exposed in a kind of cognitive search engine to only that which is relevant,
extremely relevant, for those that are my needs.
Interviewer: You mentioned IBM Cloud earlier, with computer systems increasingly
distributed, how do IBM Cloud and Multicloud service strategies develop with the
management and sharing of knowledge and the use of Artificial Intelligence?
Interviewee: Our strategy in this field is that of the hybrid Cloud, we think that it is the best
way to make this transition to this digital world that obviously embraces cognitive a lot
because now to say digital in short also means, many things in the world of cognitive. We
basically try to put our poles, for example in Italy we have one, there is one of these data
centers, which is the one in Milan basically, where there are all the services offered by IBM
Cloud and the idea of IBM is to be as close as possible to its customers say in areas where
there is definitely a relevance, so in Milan we have our center, of course then when I do my
own configuration I can decide to give services in a distributed way but our idea is the hybrid
Cloud, where the customer has certain information and will continue to have them for a variety
of reasons, and other things are instead in the Cloud and so a kind of application, of business
platform is formed in order to leverage both the capabilities, let’s say, in the Cloud and the
on-premises capabilities, as you know we acquired Red Hat which has in it its Open Shift
platform, and we think that Open Shift can become the operating system of the Cloud, and
when we mean Cloud we mean a platform that is both distributed to our customers and to our
centers, then our idea is that if someone has the peculiarities even of those of our competitors,
we think that they should be used to create the best possible service for our customers.
Interviewer: Many companies use their corporate intranet for many activities like research,
presentations, complex problem solutions, and decision support. How is it possible that IBM
Watson can be used to optimize and make these processes more effective?
Interviewee: As I told you before, our Intranet has basically two big types of use, one is to use
its own cognitive search engine, so you have a sort of interface in which you have cognitive
access to all the information that is on our intranet, so the complexity that you can imagine
with IBM that has so much internal data is absolutely simplified because you have a single
interface in front of you and then it is the cognitive engine that takes care of finding in the
various positions of IBM the correct material of the intranet for you. The second thing we
have, that we use a lot, is the chat, because you have the possibility of, every time you access
the intranet to use a chat that is composed of both recurring questions that are automatically
resolved by a cognitive system that in our case is Watson Assistant, which is one of the most
important pieces of our platform, which is able to answer many questions. Of course if then
there are specific questions, of a very high complexity related to a very distinctive process,
you are automatically able to scale always in chat on a natural person who, in turn, using a
cognitive engine is able to give you all the answers you need, so now IBM has embraced the
cognitive within its intranet, of course IBM is very large and in transformation, some things
are already supported by the cognitive while others are being transformed.
Interviewer: Let’s move on to IBM’s international dimension, that can be a burden from the
point of view of the flexibility of marketing actions and the rigidity of bureaucratic processes.
At the same time, it could also be an advantage in terms of knowledge of the different markets
in the world, the specific needs of customers in particular geographical areas and business
opportunities that can hardly be seized. How does Artificial Intelligence help the organization
and sharing of this information?
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Interviewee: I think we're talking about people here, not cognitive systems. IBM is a very
large company and has clear and very visible processes. I've never had a problem in one of
my business negotiations both cognitive and non-cognitive caused by IBM processes, I've
always had people who sometimes seemed to me, they asked questions but I have to say that
IBM processes are made because the business of IBM is a healthy business and has positive
features so let's say IBM processes are made by people and every time you, on that
opportunity, you have a discussion both at the Italian level but especially at the international
level, but I've never had a problem even by the international business lines I've always had
some help.
Interviewer: Watson is multilingual…
Interviewee: Yes, but what I was telling you about the management of opportunities, there is
not Watson inside, there are people. Then Watson is multilingual. It is able to understand
Italian, English, it has many languages. And so you, once you have done your cognitive
application then you are able to easily export it in many languages, obviously in the world
where there is a dialogue with people you must be sure that the translation is adequate because
when you speak English there must obviously be an adequate translation. We are doing,
among other things, in the railways, a very interesting project in which we go to analyze all
the data of competitors and of our client's socials and when we have situations or hybrids or
names in Italian we have one of the components of Watson which is called Watson Translator
and therefore for us the issue of the language is easily overcome in the sense that we are able
with ease to complete the text and to enrich it without any difficulty.
Interviewer: Speaking of actual projects, could you tell me about a real, concrete project of
IBM Watson, using artificial Intelligence applied to knowledge management, in which IBM
has applied its acquired best practices and developed a state-of-the-art solution?
Interviewee: I personally sold, realized, followed I think the two most important projects that
there are in IBM, I say certainly in Italy but certainly relevant. I can talk about it because we
have public references of both projects, one is the rebuilding of the call center of Wind with
an artificial intelligence platform, so we can assist Wind customers through Watson in their
dialogue, in receiving automatic services on some of their requests, we are in production and
we receive thousands of calls. I think it is perhaps the first call center in the world that uses
Watson, artificial intelligence, to help its customers, not in chat, the constituents speak, they
speak normally and receive their answer. Here we say, in the idea that we sold and then we
have realized and it worked perfectly, that Watson quickly understands what your problem is
and compared to a traditional call center where you first talk to an IVR, one x, two x, three x,
four x, then end up in a line, then with an agent, Watson immediately, in a very short time,
less than a minute, gives you your answer and meets your request. The second big application
of artificial intelligence that we did, as we say from the client we work with, is the fact that
we can buy tickets for public transport in Rome, Milan, Florence, and many other things in
voice, I went to my client this morning, while walking to the subway I asked it to buy a ticket,
the ticket arrived to me automatically while I was walking without touching the keyboard I
received my ticket that I asked it vocally and I entered the subway and I arrived to my client
so, so it is also that the citizen can easily buy tickets for public transport without any problem,
without any effort, at a time exactly while he is on the move, and is able to travel without any
difficulty.
Interviewer: This is excellent.
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Interviewee: I really like this idea of IBM being, we want to help the world improve for what
is possible for us.
Interviewer: On the subject of Artificial Intelligence and its application in the field of
Knowledge Management, who are IBM's main competitors and what are their solutions? You
mentioned Microsoft. Which other famous multinationals have had similar experiences to
those of IBM?
Interviewee: I answer you with two considerations. The first is that in IBM there is a wonderful
rule, that you cannot speak badly of your competitors, you can only talk about your offer, and
then I, what I say, in my opinion, we have an advantage at this time, ours is a very, very
interesting offer, of course we have competitors but we are almost always able to demonstrate
that our ability to combine the platform, so Watson on our IBM Cloud, and the ability of our
people to know how to work with the customer, we believe that it is something that is very
differentiating, that only IBM has, so surely there is room for all, the world is big, many
artificial intelligence projects, but ours are wonderful projects and I do not see all this
competition at this time, no. We have made several software selection competitions and let's
say it was our customers who recognized that our platform in POC, in experimentations, and
then in projects was definitely the best of all. So, I feel like saying that right now we're playing
a game let's say in a privileged position, not that there are no competitors, there are
competitors, but we certainly have a very valuable platform.
Interviewer: Well, that’s good. In conclusion, are there any other topics, areas, or information
you consider relevant to the research that was not considered in this interview? Concerning
IBM Watson, knowledge management…
Interviewee: Well, I can give you some advice. These days I am seeing, because there is a
European directive on how artificial intelligence should be, and we say Europe, in my opinion
that is always very attentive to the rights of the citizen, in the last GDPR, which is certainly
something that helps the citizen because it protects their data, and therefore if you want to
build a service also based on artificial intelligence, you have to be respectful of people like
we always do in our projects, but this, let's say, role that artificial intelligence has to play as
an aid in the life of the citizen is the most important and relevant thing of all and it is the way
in which IBM is presenting itself on the market. I feel like giving you advice, try to look at, it
seems to me that there are thirty, thirty rules that are the directives of the European Community
on how to apply artificial intelligence so that it really helps people.
2. IBM Technical Solution Architect Cloud & AI Cognitive
Interviewer: What is your role inside IBM?
Interviewee: So, I work as an architect in a team that works on Watson technology, artificial
intelligence applied to our customers.
Interviewer: Artificial Intelligence at IBM has always been one of the strengths of its
commercial offering. In particular, IBM Watson has always been at the forefront of computer
systems by answering questions posed in natural language. What is IBM's strategy in this
critical sector today?
Interviewee: Well it's definitely one of the elements on which the whole IBM offer is based.
IBM is characterized by being a company founded on two main principles of information
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technology, one is the principle of the cloud and the other is the principle of artificial
intelligence. So they are basically two core businesses of IBM.
Interviewer: Using Artificial Intelligence with IBM Watson is, therefore, a great business
opportunity. How is IBM organized to respond to different market needs and what is the role
of IBM Research centers and IBM Competence Information Centers in the world?
Interviewee: So, we basically have the research centers that are located around the world that
specialize in creating new functionalities and new technologies through artificial intelligence,
so all new algorithms and modes of interaction, of application of artificial intelligence, that
basically arise in IBM research laboratories. The competence centers are those centers that are
used to bring this innovation into the products and solutions that are offered and sold to
customers. So, they are more, let's say that point of connection between what is researched,
tested and created in the research laboratories and what is applied in the implementation phase
of the actual solutions.
Interviewer: What are the principal investments by IBM, and what are the future scenarios?
Interviewee: Well certainly artificial intelligence is an element on which IBM has invested for
several years now and will continue to invest. Alongside these there will also be issues such
as cloud computing, issues related to Blockchain, and these are then linked in the emergence,
so to speak, to the concept of digitalization of the enterprise of companies.
Interviewer: Let us dive a little deeper into the components that characterize IBM Watson.
What are the particular elements of different solutions such as IBM Watson Discovery, IBM
Watson Knowledge Catalog, IBM Watson Machine Learning? Which tools fall within the
scope of the cognitive approach, analytics, and data mining?
Interviewee: So, first of all we have to say that we have an offer of artificial intelligence at
360 degrees. What does that mean? It means that we can range from what is defined as a fully
customizable artificial intelligence, therefore created through an appropriate implementation
typical of a data scientist. So, a set of tools that allow you to create your own deep learning
networks, in a totally custom or free mode, or maybe using open source frameworks, the most
famous are Tensorflow, or Caffe, or Keras, and so on. And all this is implemented in the
Watson platform through the tools that as you mentioned which are the Knowledge Catalog,
in order to manage the set of data sources in a coordinated, aggregated way, being able to also
go to implement the concepts of accessibility to various sources, and so on. There is the tool
of Watson Studio that allows us instead to go to implement, to build, our artificial intelligence
algorithm that is the typical tool of the data scientist. The machine learning, which is instead
the run time, which allows us to run in the form of API what was built by the data scientist,
therefore the algorithm created by our data scientist. But on top of this there is a whole series
of offers and services, you mentioned one, Watson Discovery, which are basically part of what
is called pre-built artificial intelligence, pre-built, pre-packaged, that is built in a laboratory
but then specialized on customer data, which are basically a set of services that allows us to
make a quick startup of our solution. There is no need to invent the wheel to be able to
implement an artificial intelligence algorithm related to a specific technological
implementation. They are known algorithms, they are now consolidated, and through IBM
Watson, Watson Assistant or Discovery or Natural Language Understanding, they allow us to
easily and immediately, through artificial intelligence, to integrate it into our complete
business solution. So, we have basically all the possibility, the flexibility of being able to build
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either from scratch or taking advantage of what has already been done with our artificial
intelligence platform.
Interviewer: Now let us explore the relationship between Artificial Intelligence and
Knowledge Management. Since we have to manage large amounts of data, Big Data, how can
IBM Watson be used? What are the main benefits that a company can derive from it?
Interviewee: So, we have two levels of understanding and use of artificial intelligence. On the
one hand we have to understand what the real contents are, the hidden ones, we were talking
about unstructured data, therefore the whole set of hidden contents that are the proprietary of
our company, that must be interpreted according to a specific language, technical for the
company, and that therefore must be interpreted correctly. From this point of view, we are
talking about document analysis, therefore an understanding of the documental language that
allows us to take out all the data and then be able to search for them effectively. On the other
hand, there is the concept of 'accessibility to the data, that is, to allow anyone to easily access,
speaking in natural language, the data we analyzed in the previous phase, and then use a
mechanism in which the system can interact directly and understand correctly what the user
is saying to then be able to perform the various operations of post processing, data retrieval,
execution of a series of algorithms that can be somehow useful to those who must then produce
business information.
Interviewer: At IBM, the knowledge, experience, documentation, processes, skills gained by
each person can become a valuable corporate asset. How can the use of Watson facilitate the
sharing and use of relevant information for a company's strategy?
Interviewee: Well, I'm going back to what I was saying before, that is the ability to give access
to anyone, in a unified and completely natural way, a series of information that can also be
very technical and even very sectoral, in everyday life, allows you to easily spread knowledge
at all levels of our company. So being able to provide access with the same tool both the Top
Line Manager and also the operational on shift that obviously goes to access the various
portions of data can use the language that is best suited to their role and their knowledge,
precisely because we can make sure that the system knows how to interpret the question and
knows how to respond accordingly.
Interviewer: With increasingly distributed computer systems, how do IBM Cloud and
Multicloud service strategies develop with the management and sharing of knowledge and the
use of Artificial Intelligence?
Interviewee: Surely the Cloud platform allows us to go and manage information exactly where
we are, without having to worry about the correct computerization of the information. This is
because it allows us to access anywhere information that may be located in a certain
geographical area of the business. Obviously then we have a whole series of constraints to
which we must respect, for example the question of the GDPR, which basically says that the
information that arises, the management of personal information of particular importance,
must remain and be managed in Europe. Well, a cloud platform like the one by IBM allows
us to be able to both meet this prerequisite, but then also make the information accessible
throughout the world, where we need information.
Interviewer: Many companies use their corporate intranet for many activities like research,
presentations, complex problem solutions, and decision support. How is it possible that IBM
Watson can be used to optimize and make these processes more effective?
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Interviewee: Surely both from the use of facilitated access points, many companies use them,
IBM itself is using the so-called chatbots or virtual assistants that allow us to easily get
additional information in 24-hours mode, so at any time of day and night, wherever we are,
now I was waiting for important calls that then maybe are not properly handled. This is the
simplest way but also other ways are the concepts of process optimization, where through
optimization algorithms that exploit the platform as I said before, for example, typical of data
scientists, can find those innovations and those steps that allow us to make certain business
processes more effective and more efficient, from an analysis, for example, of the behavior of
various users, and so an analysis of the historical level of what you do and then understand
how to improve and predict further action.
Interviewer: IBM's international dimension may be a burden from the point of view of the
flexibility of marketing actions and the rigidity of bureaucratic processes. At the same time, it
could also be an advantage in terms of knowledge of the different markets in the world, the
specific needs of customers in particular geographical areas and business opportunities that
can hardly be seized. How does Artificial Intelligence help the organization and sharing of
this information?
Interviewee: Surely it goes back a bit to what we said before, the possibility of being able to
access large data information, analyzed, understood and sectorized, for example, for the
business market, allows us to certainly facilitate this type of activity. I will give a quick
example, for me personally, 2 years ago I was hired on a knowledge management activity at
a company, and I used for the occasion all the know-how developed by our Australian
colleagues at a similar Australian company that a few months earlier, a year earlier, had made
a type of, let's say, very similar requests and solutions. So, having been able to take advantage
of their knowledge building, let's say, their expertise in this type of solution has benefited us
in being able to propose something more innovative and efficient in our case. So, this talk of
knowledge sharing, of node sharing, which is hanging on the whole area of the worldwide,
put together becomes a point of advantage and also differentiates our type of offer.
Interviewer: Could you tell me about a real, concrete project of IBM Watson, using artificial
Intelligence applied to knowledge management, in which IBM has applied its acquired "best
practices" and developed a state-of-the-art solution?
Interviewee: Yes, then one of the most recent, in which I worked personally, is a solution
developed by ‘Sole 24 Ore’ in which a virtual assistant is basically developed. This virtual
assistant is able to interact with tax experts and has become a support tool in the field of
taxation, think about 600,000 documents relating to all Italian tax legislation which is
obviously immense and varied. It has become an expert that is able to respond appropriately
to questions from tax experts relating to the percentage of VAT applied to a certain asset or a
certain share or activity, in a very accurate and precise manner. All this with the ability to
interact in natural language and have, substantially, digested, understood and analyzed
precisely this documentation of about 600,000 documents, all related to Italian tax regulations.
Interviewer: On the subject of Artificial Intelligence and its application in the field of
Knowledge Management, who are IBM's main competitors? What are their solutions? Which
other famous multinationals have similar experiences to those of IBM?
Interviewee: Well certainly at the level of artificial intelligence our classic competitors are,
but you can also cite them, Apple more than anything else is our partner, surely Microsoft,
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Amazon and Google are our first competitors with whom we compare day by day. In reality
then in the field of Knowledge Management we have a further competitor that maybe applies
more traditional technologies but that in the end do basically the same things, that is called
Expert System, and then it gets more and more popular from this point of view. These are the
strongest main players from a corporate point of view. And then we have a whole series of
niche players, so to speak, that try in one way or another to address the same issue always
with the concept that artificial intelligence should be developed in a way perhaps less of an
enterprise type and more of a quick-and-dirty type, so to speak. So, they are true startups
trying to cover very marginal and niche situations.
Interviewer: In conclusion, are there any other topics, areas, or information you consider
relevant to the research that was not considered in this interview?
Interviewee: Well, then surely an aspect that could be detected, could also be that relating to
the degree of maturity a platform of artificial intelligence brings with respect to the use that a
business user must make of it. Here we also enter a bit in how the platform Watson has
distinguished itself over time from the platforms of our competitors, right? Where typically in
our competitors what is required is a specific knowledge of the tools and a specific knowledge
of IT and often also requires us to build the algorithms for the analysis of artificial intelligence.
We often limit ourselves to simply creating a support to be able to develop a lot of business.
In IBM with the Watson platform we tried instead to go a little further and provide tools, so
to speak, of refining and traction of artificial intelligence that are used by real business users,
to be able to go substantially to apply artificial intelligence directly in the various business
processes, without having to require a specific development of an algorithm by a data scientist,
so we also say concepts of accessibility could be a concept that could be interesting in this
type of research.
3. Emanuela Picardi: IBM AI Cognitive Delivery Manager
Interviewer: What is your role inside IBM?
Interviewee: I am Cognitive Analytics Manager at IBM, I deal with the delivery of cognitive
projects, mainly based on Watson technology.
Interviewer: Using Artificial Intelligence with IBM Watson is a great business opportunity.
How is IBM organized to respond to different market needs? What are the roles of IBM
Research Centers and IBM Competence Information Centers in the world?
Interviewee: Okay so, IBM, in order to respond to different market needs actually takes
advantage of the fact that it can realize any type of data at 360 degrees, not only focusing on
structured data, but also analyzing non structured data, both with machine learning techniques
and so to go and search text insights but also those that deal with tone, the sentiment, and then
thanks to this whole series of information you can extract the concept at 360 degrees. And
how it is organized, in reality and, fundamentally is based on, a whole series of, there’s from
small to large businesses so it doesn’t just operate on the national level but most importantly
on the international level and then on the basis of the demand it tries to adapt and also
understand what is the most appropriate Watson technology to the current needs for both
society and the market. Mainly the software is developed by the research centers, so all the
machine learning engines that we see is delegated to IBM Research, while all developments,
so what is instead the side of the, let's say consulting that deal with employees, IBM
employees, is related instead to the adaptation of this machine learning, so Watson products,
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to what is then the individual customer. So, it is seen a bit as, consultant side as a black box,
the side instead of what is the work behind is delegated to the research side.
Interviewer: And what are the principal investments by IBM, and what are the future
scenarios?
Interviewee: So, the investments by IBM, as all the multinational companies that deal with
the artificial intelligence of data, so trying to have a large amount of data to refine the machine
learning models behind, both related to structured and not structured data, so textual but also
those of visual recognition. And future scenarios, now a lot of reference is made to Watson
Healthcare, therefore an aspect that will be in the health sector.
Interviewer: Let us dive a little deeper into the components that characterize IBM Watson.
What are the particular elements of different solutions such as IBM Watson Discovery, IBM
Watson Knowledge Catalog, IBM Watson Machine Learning? Which tools fall within the
scope of the cognitive approach, analytics, and data mining?
Interviewee: Essentially, Watson's services are divided into various categories. It starts from
sentiment recognition, so there are services such as tone analyzer, natural language
understanding that allow you to extract between texts the information related to the sentiment
and related to the tone, so if a given text or post is written in an ironic way, written in a bad
way.
Interviewer: So emotions…
Interviewee: Yes. The actual tone. Then we have a whole section dedicated instead to
discovery, then, language and allows through machine learning engines, but also rule based,
to go and find all that information in the texts, and, in this case, an important thing of
Discovery, Watson Discovery, is that it can be setup with these custom models, so both
machine learning and rule based, which are developed with the Watson Knowledge Studio
and, and allows you to extract all the enrichments and therefore is a kind of database, and...
obviously is cognitive intelligent. And then there is all the visual part, of the visual
recognition, which allows instead, machine learning related to the images, therefore the
analysis of the images, so to be able to classify images. And then there is the part of
classification, natural language classifier, for example, which allows instead to go to classify
documents, so, to know what they are, what kind of documents they are and what they tell
about and then give a classification, a classification of the documents. And then there is the
whole part related to the vocal, text-to-speech, speech-to-text, always related to Watson
services that allows instead to analyze the audio and then to transcribe it, or vice versa the
written part to make it into audio.
Interviewer: Now let us explore the relationship between Artificial Intelligence and
Knowledge Management. Since we have to manage large amounts of data, Big Data, how can
IBM Watson be used and what are the main benefits that a company can derive from it?
Interviewee: So, the benefits so, how it is structured, in reality is very tied, initially to any kind
of project of structured data, so trying anyway to have a source, even more than one, and go
gradually to have a process of analysis of these documents depending of course on what is the
final result, and depending on the insights that you want to extract. So maybe this source is
structured, unstructured, whatever it is, because it passes through the various Watson
channels, so perhaps it passes through the section of the Natural Language Classifier to be
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able to classify a document, then it must be analyzed with a custom machine learning model
in order to go and analyze those specific insights related to the customer, then it can pass
through the part of the tone analyzer to go and define the tone and so on, and then all this
process makes the data structured. The advantage for the customer is basically related to being
able to analyze any type of data and have it immediately available, because where there is an
unstructured data the difficult thing is to have to read everything and then go to filter for what
are the main information. One thing that happens especially in the public sector, in ministries,
is that most of the data is mainly still paper, so the digitized part is not there, so the work
becomes double, because you have to go and read any type of document and we don't talk
about a hundred documents but we talk about thousands of millions of documents, so the thing
that, let's say the added value comes from the digitalization of this data and structuring of this
data, so that as if you were analyzing any DB, any structured data, I can take directly the
information I need.
Interviewer: At IBM, the knowledge, experience, documentation, processes, skills gained by
each person can become a valuable corporate asset. How can the use of Watson facilitate the
sharing and use of relevant information for the company's business?
Interviewee: So an example, I don't know if it's appropriate, but then maybe I'll do another
one, an important thing that I forgot to mention before, among the various Watson services is
that there is the part of the language instead, so Watson's ability to recognize and be able to
respond in natural language as well.
Interviewer: In natural language...
Interviewee: Yes, and also Watson Assistant, and these are the two systems that make it
possible to recognize the user's intention and thus succeed in giving an in-line answer. And
what does that allow? Even at the company level, all processes, for example HR processes,
are all linked to the cognitive part, whatever it is, the performance of a person with the analysis
of all, maybe the feedback he or she received, the whole part of the knowledge that has
acquired and a whole process that basically are perhaps certifications, data, even unstructured
data, feedback data, are analyzed and when they give the alert to managers to say look, for a
pay raise or for additional information, so this also helps a lot and speeds up the HR system.
Interviewer: Compensation and benefits...
Interviewee: Yes.
Interviewer: With computer systems increasingly distributed, how do IBM Cloud and
Multicloud service strategies develop with the management and sharing of knowledge and the
use of Artificial Intelligence?
Interviewee: Watson is Cloud, so it's just IBM Cloud. So, there are basically all Watson's
cloud services, all those I've mentioned and in addition there are also services that are on-
premises. And obviously those that are currently exploited to this day are those in the cloud,
and for those in multicloud, all Watson services even in an environment that is not mainly
IBM Cloud but also just Amazon's Cloud for example, you can go and integrate the Watson
systems.
Interviewer: I have seen on the IBM Watson website that there were several stories about IBM
Watson applied to other companies such as Amazon, KPMG...
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Interviewee: Yes.
Interviewer: Now let's move on to the international dimension because, as I said before, this
dissertation is focused on the field of international business. IBM's international dimension
may be a burden from the point of view of the flexibility of marketing actions and the rigidity
of bureaucratic processes. At the same time, it could also be an advantage in terms of
knowledge of the different markets in the world, the specific needs of customers in particular
geographical areas and business opportunities that can hardly be seized. How does Artificial
Intelligence help the organization and sharing of this information?
Interviewee: If this could be the example, I'll tell you already that Watson for many services
supports ten, fifteen languages so...
Interviewer: Italian as well?
Interviewee: Italian as well, so Italian, English, French, Spanish, Portuguese, Russian,
Japanese, Chinese. Of course, there is a road map, so, for some services, English of course, is
above all and also has a very high level. For some services, such as text-to-speech, speech-to-
text, there is perhaps a level of Italian that is at a certain point in the road map, so maybe it is
basic, but gradually there is a whole road map drawn over the years to have then when it is
that is supported one hundred percent, but most of Watson's services already support most
languages one hundred percent so…
Interviewer: Could you tell me about a real, concrete project of IBM Watson, using Artificial
Intelligence applied to knowledge management, in which IBM has applied its acquired "best
practices" and developed a state-of-the-art solution?
Interviewee: So, I can give you an example of two customers, who are also let's say public,
and, two references, we have one that is one of our customers, Enel, for the procurement part
of Enel, so it is a kind of cognitive dashboard that allows you to analyze the reputational part,
the documentary part of its suppliers, so when Enel needs to know if a supplier is in line with
what are its internal standards, uses this dashboard. To do this technically and so cognitive
side, what was used? For the reputation part of the suppliers, so to see if he committed crimes,
was used a machine learning model trained on news, and therefore every day a whole series
of newspapers are analyzed in such a way as to give reality to an information if a stakeholder,
or the company itself, the supplier itself, is at the center of some scandal. On the documentary
side on the other hand, all the legal documents of the suppliers were analyzed, so when the
supplier comes to tender from, for example, the document of fiscal regularity, and also here,
according to Enel's internal standards, in this case, perhaps had a limit on what were the
pending charges, defined whether that supplier was in-line or not. And these legal documents
have a validity of six months, a year and therefore even when it was due they were requested
automatically by the system, and whenever an alert was presented both for the documental
part and for the reputational part, the representatives were obviously warned and therefore it
was a real time system, so at any time even if it is not before, if a scandal had happened, two
minutes after, Enel succeeded, it was aware of it. And, another, and this is a project that is
already going on for two years, while a new project is, also here very interesting is Wind,
where they were, in this case a vocal assistant was used, and this unlike Enel, which is internal,
it is used to the public. So when people need to have clarifications from Wind call the call
center, Watson answers the call with two engines, the first is the predictive, so when the user
calls depending on, all the information that Wind has at the customer's disposal and potential
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problems, maybe they have seen a higher bill, it can already predict the reason for the call,
and then this is the first engine. The second instead is that of natural language understanding,
so if the reason for the call is among those that are the information of interest of the, let's say
to Watson's knowledge, it will be Watson directly to answer, otherwise Watson turns the
questions to the team of competence, and then the team of competence will then answer the
call instead, and this is a project that let's say started a year ago and continues with its
developments.
Interviewer: An ambitious project…
Interviewee: Yeah.
Interviewer: On the subject of Artificial Intelligence and its application in the field of
Knowledge Management, who are IBM's main competitors, what are their solutions and which
other famous multinationals have similar experiences to those of IBM
Interviewee: Uhm well mainly Amazon, Microsoft and Google because it has the power of
data so...
Interviewer: Google Analytics...
Interviewee: Yes.
Interviewer: Are there any other topics, areas, or information you consider relevant to the
research that was not considered in this interview?
Interviewee: Well, I think we've more or less touched all the points.
Second-level interviews
1. Giovanni Triunfo – IBM Project Manager Application Automation
Interviewer: What is your role inside IBM?
Interviewee: The Blue Pages internal network definition of my role is Project Manager
Application Automation, but basically, I am Test Manager for System Integration projects,
but I am also a certified Scrum Master for Agile Projects.
Interviewer: The use of IBM Watson applied to Knowledge Management allows users to
analyze large amounts of structured and unstructured data and easily reach the information
they need. Could you tell me about your experience at IBM?
Interviewee: I don't have a technical background to fully use all the tools Watson provides,
but I have used the IGNITE Cognitive Test Quality Platform.
Interviewer: IBM uses Watson’s cloud system to easily and immediately reach large
populations of employees and customers all over the world, therefore spreading large amounts
of information. Can you tell me what the main advantages of this strategy are?
Interviewee: From production to call centers, Watson, and therefore the AI, is applicable to
all industrial sectors up to call centers, HD, offers data protection, a very important issue, and
can interface with tools already in use. Watson's IBM Cloud Strategy is powered by the latest
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innovations in natural language processing, visual recognition, and automatic learning, and it
is thanks to its recommendations, intuitions, and insights that Watson can predict and model
business forecasts for companies, so that it can improve critical decisions by reasoning in real
time with its integrated Machine Learning processes. Business workflows become smarter
because Watson integrates into workflows to add AI where it is needed.
Interviewer: Corporate communication is increasingly interactive and unstructured: it uses
chat, images, audio and video. How does IBM Watson allow users to generate new levels of
knowledge and share information easily and effectively?
Interviewee: Watson on IBM Cloud allows access to unstructured data, and can learn from
small data sets, that is the quality of the data that makes the difference, not the quantity, and
helps to increase its value by analyzing it more deeply, the Deep Learning mechanism.
Fundamental models and reports are processed and produced through images, emails, social
media and much more, and Insights are shared on the Cloud. Data Science and Artificial
Intelligence have evolved to the point that organizations of all sizes are actively experiencing
the inclusion of predictive insights. IBM Watson Machine Learning helps data scientists and
developers collaborate to accelerate the process of moving to distribution and sharing and
integrate AI into their applications. By simplifying, accelerating and regulating deployments,
AI enables organizations to produce business value.
Interviewer: IBM Watson uses processes that simulate the human mind through neuronal
networks. So, it uses a learning system, the so-called machine learning. Isn't there a risk that
in the teaching phase, even in an unconscious way, incorrect or obsolete instructions are
inserted? What could be the negative effects?
Interviewee: In fact, the AI is based on the acquisition and storage of data from different
industries, so the more Watson ingests quality data, the more accurate the forecasts and
insights can be. Obviously, the dirty data is also taken into account, but a minimum
percentage, even if loaded continuously, does not affect the forecasts or the percentages of
effective applicability for which you identify the suggestions.
Interviewer: What are the implications of personal data protection in the use of Watson and
what are the possible impacts in knowledge management?
Interviewee: The information is absolutely protected, and a correct diffusion and diffusion of
the data cannot prescind from accurate policies of security. IBM is committed to providing
customers and partners with innovative solutions for privacy, security and data governance.
IBM is also aware of the crucial importance of data protection for a business, not only for
business data, but also for personal information. If the privacy of your company's data is
compromised, it can cause irreparable damage to the company's reputation and loss of
competitive advantage.
Interviewer: Is the use of IBM Watson by employees or customers complex or easy to use?
Does it require special technological skills?
Interviewee: Absolutely not, just basic IT application knowledge is required; Watson Machine
Learning is an integrable solution and allows an inter-functional team to deploy, monitor and
optimize models quickly and easily. APIs are automatically generated to help developers
incorporate AI into their applications, in a few minutes. Watson Machine Learning's intuitive
dashboards make it simple for teams to manage models in production, and its uninterrupted
workflows enable new, ongoing training to maintain and improve model accuracy.
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Interviewer: IBM Watson has transformed the way we manage the knowledge of employees
and managers. How was knowledge management handled in IBM before Watson was used?
Interviewee: Essentially through communities and posts within them, and topics were
searchable by keyword in a search box within the corporate network through a search engine;
obviously search results were generic and by keyword.
Interviewer: Are there any other topics, areas, or information you consider relevant to the
research that was not considered in this interview?
Interviewee: IBM Research has been exploring artificial intelligence and machine learning
technologies and techniques for decades. Artificial intelligence will transform the world in
dramatic ways in the coming years, and IBM is advancing in the field through its portfolio of
research focused on three areas, advancement of AI, rescaling AI, and confidence in AI. IBM
is also working to accelerate artificial intelligence research through collaboration with
institutions and related individuals to push the boundaries of AI faster, for the benefit of
industry and society.
2. Amit Puri: IBM Europe Automation Practice & Delivery Leader – AI SME
Interviewer: What is your role inside IBM?
Interviewee: Automation Practice and Delivery Leader for Europe.
Interviewer: The use of IBM Watson applied to Knowledge Management allows users to
analyze large amounts of structured and unstructured data and easily reach the information
they need. Could you tell me about your experience at IBM?
Interviewee: Watson is not one thing, right? There’s no such thing called Watson per se.
Watson is a set of products and capabilities that IBM has developed. Each unit uses these
products and capabilities to create new products or software. We use it for automation, we use
it for analyzing weather data, so it has multiple uses, using the same set of algorithms. For
example, multiple language processing, artificial intelligence and so on. When it comes to
automation and knowledge management, forget automation for a minute. When we say
automation, we capitalize automation in a broad sense. We call anything that will help
represent a repetition of work for a human, we will capitalize that as automation, okay? So,
anything that represents a repetition of work. What kind of work are we talking about? Let’s
say you are a master’s student, right? So you often get some Bachelor’ students coming to you
and asking you some questions, right? The set of students is changing but the question remains
the same. If the same set of question that one Bachelor student will come ask you, and maybe
next year and a year after that another set of students come and ask you, and so on and so
forth, right? In this sense, while the student is changing, the human explains the same answer
again and again, okay? What you can do now is to teach this answer to Watson, okay? And
then, Watson teaches that to other students. You curate the answer and once the curation has
happened then Watson gives the answers out to those students. It is so simple. This is layer
one. Now let us go to layer two. Layer two would be that the answer that you give comes from
many sources, right? It would be a combination of something that is written in a paper, so you
would ask that student to read a particular paper or a text from a particular doc, something
like that. It could be a picture, seeing videos, so there are multiple type of content that you
will have. So now Watson has a way for you to curate all this content. You can take the answer
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directly from the content. If you give Watson a PDF, for example, or a textbook, it will read
automatically through that entire book, and create an answer out of it, okay? And then
someone like you, a knowledge expert, will look at the answer that has been created and you
can rectify it, you can change it, you can modify it so that it makes complete sense. This is
layer two. Now let’s talk about layer three. Once you have curated the content, when the
learner looks at the content, he can define the content useful or define the content not useful.
Like on Facebook, if the feedback is positive, you will give a thumbs up, if the feedback is
not positive you will give a thumbs down. Every time you do that, Watson learns. If you give
thumbs down, Watson will roll the curated content back to the Knowledge Manager, like you,
saying this is not correct anymore. And then you will have a chance to correct the content. If
there are multiple answers for the same question, what Watson will do is when you ask the
question, how do I go to Point A, right? And then Watson will give multiple answers, you can
go to Point A by car, by train, by bus, right? But based from the feedback from previous users,
the highest probability of answer being right is going by bus, so it will give a probability score
of the answer, and every time a user gives a feedback, the probability scores will change, so
Watson continues to learn.
Interviewer: IBM uses Watson’s cloud system to easily and immediately reach large
populations of employees and customers all over the world, therefore spreading large amounts
of information. Can you tell me what the main advantages of this strategy are?
Interviewee: The main advantage of information dissemination is that I can reach to large sets
of people the information, as soon as I see this information, it is available to all those users,
right? I can segregate the users and I can look at the amount of information that should be
available to a particular user, and I can make sure only the relevant user gets that relevant
information, so I can control GDPR, any kind of government regulations that apply, particular
users aim to get particular information, and I can do it in a cost-effective manner.
Interviewer: Corporate communication is increasingly interactive and unstructured: it uses
chat, images, audio and video. How does IBM Watson allow users to generate new levels of
knowledge and share information easily and effectively?
Interviewee: In terms of use of information, when Watson is set with a lot of these large
datasets of information, you can also now perform analytics on these large datasets, to
understand which dataset is corresponding to another, adding more insights coming out of it,
giving more insights coming out of it, who are the most frequent users of the data, what is the
way in which they are using the data, all of those things can be monitored.
Interviewer: IBM Watson uses processing processes that simulate the human mind through
neuronal networks. So, it uses a learning system, the so-called machine learning. Isn't there a
risk that in the teaching phase, even in an unconscious way, incorrect or obsolete instructions
are inserted? What could be the negative effects?
Interviewee: Not really, because as I said, Watson is constantly learning. So even if the
information is incorrectly input and coded into Watson, it will be quickly rejected by the user
without using it. As soon as they find that that information is not relevant or it is not making
sense, we are going to get that feedback that they are not happy with that information, and so
it gets rejected in the system.
Interviewer: What are the implications of personal data protection in the use of Watson and
what are the possible impacts in knowledge management?
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Interviewee: As I said before, personal data can be controlled completely. Watson looks at
who needs information, then if the person has it in excess then what is the level of content that
the person has, and what is the time frame for which that information needs to be provided.
So, all of those things can be deployed to effectively make sure of compliance to all regulatory
bodies. And unlike a human, Watson will not make a mistake, and the compliance is one
hundred percent.
Interviewer: Is the use of IBM Watson by employees or customers complex or easy to use?
Does it require special technological skills?
Interviewee: Well, it takes about five minutes to learn, so it is not complex at all.
Interviewer: Now let’s dig into the history of how IBM managed Knowledge Management
before Watson, so how was the corporate intranet used and how did the way to reach and share
information change after the introduction of IBM Watson?
Interviewee: Internally, we used to save a lot of information through wikis. We had a lot of
internal wikis and an IBM connection tool. These used to be the most common tools for storing
the knowledge. But other than that, we would also use a lot of commercial tools, that could be
SharePoint, it was also dependent on what the client required us to use.
Interviewer: Did the use of IBM Watson optimize the use of human resources and has it led
to "cost savings" on IBM’s part and has it been quantified?
Interviewee: Yes, it has been optimized, definitely optimized, but optimized in the sense that
the Knowledge Management was never a rule per se, every one of us, as part of our jobs, we
would make use of Knowledge Management tools and also sharing that knowledge with other
IBMers. And that would take some amount of time, let’s say I was spending one hour per
week on Knowledge Management, someone else might spend ten minutes on Knowledge
Management, someone else might be spending four hours on Knowledge Management, so
now that has been reduced. So, it was a replicative task that was being done and that is no
longer the case now. In terms of savings, we don’t have any definite quantification but we
definitely between thousands of minutes have been saved.
Interviewer: IBM Watson is a system that helps organize large amounts of data and find
complex answers. How is this system used to help employees and managers make more
informed decisions?
Interviewee: Absolutely, so we use these systems to make more time decisions on our work
on a daily basis.
Interviewer: How can people apply IBM Watson’s experience gained in some countries to
other geographical areas?
Interviewee: So, Watson does not have the physical boundaries, once you apply Watson it can
be used by anyone, that person might for instance be based in India, Japan or America. So, it
does not matter at all from a Watson perspective. What matters is the language in which the
content has to be curated, right? So, Watson, I think the last time I saw it supported 8
languages, I have to check how many languages it is supporting now. But it really depends on
the curation of the content, so if my colleague cannot understand English, they will need to
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use Watson, they will need to curate content on their own. But from an excess point of view,
they will have access to the English content, and then we also have some Watson resources
that help translate the content as well, so if it has content in English, Watson will translate into
another language if required. But also that translation will be somewhat limited depending on
how complex the content is. So, sometimes the Google Translate will be effective, sometimes
it will not be effective.
Interviewer: Are there any other topics, areas, or information you consider relevant to the
research that was not considered in this interview?
Interviewee: No, I think we covered most of it. If there is one of the key things I think that I
might want to add is that the use of Watson, is that IBM is moving towards open cloud and
making sure that these technologies are available to the general public as well, so they can
come up with their own usage of these Watson technologies, sharing our knowledge so that,
you know, the manager can make more use of this knowledge and create more content.
3. Marco Monti: IBM Senior Managing Consultant & Research Scientist IBM Watson
AI & Advanced Analytics
Interviewer: The use of IBM Watson applied to Knowledge Management allows users to
analyze large amounts of structured and unstructured data and easily reach the information
they need. Could you tell me about your experience at IBM?
Interviewee: Watson is a set of technologies ranging from content analytics, standard, to
knowledge representation, with knowledge graphs, with semantic technologies and with the
opportunity to represent concepts in an abstract way at different levels depending on the
granularity of knowledge that is intended to be formalized. The experience I have had has
been in the healthcare sector, but also in the insurance sector, and now the understanding of
new domains of knowledge is always important, let's think of the insurance companies that
have to build new financial and insurance products on areas that were not in their previous
experience and therefore have to move in an exploratory way and to do so they have to take
advantage of many documents that are often with unstructured data content, such as texts and
so on, and therefore to represent these concepts, first identify them and then represent them.
It is very important so that then they can develop hypotheses of scenarios or business or also
hypotheses of causal link, correlation, with cause, between different elements that must be
dealt with. In this sense, IBM helps companies to process this unstructured information, to
extract concepts and conceptual entities so that they can then be formalized and support
conscious decision-making powers that reverberate the authentic empirical evidence, not only
on memories but also on unstructured data that until recently was a difficult process. Because
without the possibility of extracting knowledge from unstructured data it is difficult to produce
statistical or even logical inferences.
Interviewer: IBM uses Watson’s cloud system to easily and immediately reach large
populations of employees and customers all over the world, therefore spreading large amounts
of information. Can you tell me what the main advantages of this strategy are?
Interviewee: It's not necessarily true that the cloud shares information. The cloud is used to
amplify and distribute worldwide computational resources that are used to implement AI, but
it does not necessarily convey information, indeed it is just the opposite. Think of an insurance
company that wants to do an analysis of its data and elaborate a representative statistical model
and it is not that IBM then produces this model for an XY insurance and then shares it in the
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cloud for everyone else otherwise it would violate the privacy of this company and spread a
knowledge that is intrinsic of that company to others. This part may need to be reworked as a
question. I understand the meaning a little bit. And the meaning is that, to develop
technologies on a global scale, so-called upscale, very expensive computing resources are
used, which were once the mainframes monitored within customers, now they are the cloud
that can be both public, private and hybrid. So, they are solutions with very intense and very
extensive computational force that are then made smart through statistical models developed
by leading research groups around the world. Thus, both the computing capabilities of the
hardware on the one hand, and the possibility of sharing them on the network through the
Internet and then the cloud itself, and also the richness of statistical models and artificial
intelligence that IBM develops for each individual case of application, are combined.
Interviewer: Corporate communication is increasingly interactive and unstructured: it uses
chat, images, audio and video. How does IBM Watson allow users to generate new levels of
knowledge and share information easily and effectively?
Interviewee: The cloud technology that we have in IBM Watson, in this case are multiple
technologies, allow you to process multiple types of data, those structured, present in
databases, with clear classification, and those unstructured that can be text, audio and video.
So, it goes to process and cover a great heterogeneity of data. This allows therefore to valorize
a lot the informative asset of the companies, even the most silent one, that is the asset that
until a few years ago could not be handled with IT tools. The availability of these tools, we
think, I do not know, of a hospital that can relate the information it has on the success of some
treatments benefiting from both written text reports and images, let's say, under the
microscope of some histological examinations or whatever, really can then enrich this chain
of documentation that is produced in the decision, but also in understanding, in the complexity
that there is both in the business but also in other contexts and therefore can allow to valorize
all the insight or all the knowledge that is available in each type of data putting then all together
in a harmonic chain, I can also find new information that arise from correlations that until
recently could not be observed. This is a holistic approach to knowledge, the fact that with
these new technologies you can attach more layers of phenomenal reality and therefore more
evidence, more types of data, put them together, process them each with those specific
algorithms because for the part of audio you use some algorithms, for the part of video others,
for the conceptualization part I would use the knowledge graph, and we go to simulate what
is not so much the neural network that is what you said that is in the brain, but instead we go
to recognize some insights that the same human being accomplishes but that then struggles to
put together in correlation between thousands and thousands of entities and thousands and
thousands of records of data, because our mind is limited, it is called bounded rationality,
while the mind of machines can theoretically become super-rationality, that is a rationality
that really allows to collect infinite amounts of data, to process statistics in a correct and
undistorted way, to then represent all this complexity in a neutral way, these three dimensions
characterize the knowledge that can be obtained from machines as, say, stronger than what
can be collected by a human computation.
Interviewer: IBM Watson uses processes that simulate the human mind through neuronal
networks. So, it uses a learning system, the so-called machine learning. Isn't there a risk that
in the teaching phase, even in an unconscious way, incorrect or obsolete instructions are
inserted? What could be the negative effects?
It's not clear what you mean in the end. Yes, I think so, this is important because it concerns
the relationship between the construction and a model of reality and the data used to build it,
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this is also the part that descriptive statistics teach us, that is, the data you use must be
representative of the phenomenon that you want to describe and represent. Otherwise, if the
data are not representative, there is a risk of building a poor and unrealistic statistical model.
In the same way the artificial intelligence feeds on data, therefore the artificial intelligence is
a series of algorithms that are born on various statistics, therefore that elaborate statistically
the data, and that develop on data whose quality must be also weighted, as for the statistic but
also for a human learning, if you provide an information, a set of data that is not representative,
also the model will not be so. This is a risk that statisticians and those who do machine learning
should consider, but there are further risks that go beyond the unrepresentativeness or bias of
the machine learning model, there is the fact that some algorithms of machine learning allow
to make many of those different conjectures and elaborations that can lead to the discovery of
new information that before were not compared with one another and then there may also be
a risk in the use of this knowledge, and then what kind of effects it may have on society as
you indicated in the introduction of your thesis. But it is no longer simply connected to the
technical and statistical theme itself, of how representative is the set of data with respect to
the quality of the model to be elaborated but there are also implications of an ethical nature.
Interviewer: What are the implications of personal data protection in the use of Watson and
what are the possible impacts in knowledge management?
Interviewee: Personal data, in the context of Watson and IBM Cloud, belongs exclusively to
the customer who makes it available for his statistical models and artificial intelligence. IBM
does not benefit in any way, does not appropriate in any way the content of customer data and
does not even generalize them for later use. This is very important. So data protection is full,
unlike other players or vendors on the market, which instead create opportunities to collect
new data even without customer knowledge. Let's also think of Facebook, some apps that are
created to collect new and even sensitive data and develop patterns, perhaps behavioral, of
consumption, urban journeys and so on. IBM does not behave like that, on the contrary, in its
collaboration environment with international, universities, scientific entities, with which to
define the ethical values on which to move and evolve AI, there is privacy in the strict sense.
There is also the ability to explain how data is used and also to learn or try to imagine the
implications of some algorithms compared to others. In this sense, your question gets across.
IBM proposes itself as a fiduciary of our customers and as an absolute respectable entity
towards them.
Interviewer: Is the use of IBM Watson by employees or customers complex or easy to use?
Does it require special technological skills?
Interviewee: There are several modules that make up Watson technology as you have already
had the opportunity to explore. Depending on the type of instrument we have a different
approach in terms of ease and also immediacy. Surely, IBM takes great care of user
friendliness, that is the ease of the interface and also of the approach to data. It's still true that
we deal with complexity and some complexity still remains in the hands of the human being
who is then called to be an effective actor in the construction of the attitude of that technology
and how to finalize it to a cognitive purpose, because there is no IBM Watson for everyone.
There are many modules, which have capabilities a bit like in the human brain that specializes
in some areas, and each area offers an application and even computational contribution to that
specific cognitive goal. So, in the same way, IBM Watson's cognitive services also have
different goals, different maturity, and different attitudes. There are some, as you said, that do
not require any programming skills. On the contrary, they are very user friendly and that with
a simple WYSIWYG training of a few minutes the person can be already operational, such
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as, for example, the one concerning knowledge management and content analytics, or Watson
Knowledge Studio, which allows to extract knowledge from texts and where the subject
manages to qualify which conceptual category some texts belong to and therefore the
labelling, or tagging of those documents is done with great ease and then the machine
automatically manages to generalize what the human being has done and tries to continue this
categorization of the text and then asks the human being to validate it to learn if it did well or
did poorly.
Interviewer: That’s definitely a strength of IBM Watson. Let's talk about knowledge
management in IBM before Watson. IBM Watson has transformed the way of managing the
knowledge of their employees and managers. How was knowledge management handled in
IBM before Watson was used?
Interviewee: As all technologies, they have their own progression, both of development in
laboratories and then of applications in real contexts. If we want to focus on the area of content
analytics and knowledge management, the technology that also animates our intranet in IBM
started from a research for initial keywords on all the documentation that IBM offered to our
employees to then arrive now at a semantic search based on concepts, not only on keywords,
which has further facilitated the exploration of the great knowledge available in IBM, so,
therefore, a person who perhaps at the beginning was looking for a topic can, thanks to this
technology, get to other nuances on the subject thanks to the fact that cognitive technology
allows to navigate not only in documents but also through concepts and thus aggregate and
make more and more fine-grained the research so that the employee can really reach the value
in that particular type of information.
Interviewer: Did the use of IBM Watson optimize the use of human resources? And has it led
to "cost savings" on IBM’s part and has it been quantified?
Interviewee: Internally I am not aware of how efficiency is measured in our company but I
can tell you how our customers measure it, and as a consultant when we offer cognitive and
advanced technology to our customers and partners we certainly propose a review for the use
of human resources, not with a view to reduction, therefore layoffs, but with a view to
retraining on other levels. As if to say that what the Watson system will do in the future will
free up resources that will in turn be able to grow in education and information, and develop
themselves as people, further services and additional added value for the company. I think it
is measured in this sense, in a perspective of changeover, of positions that are remodeled as
professions and as roles in a company after the inclusion of an artificial intelligence system
that also evolves over time as human skills evolve. These are measures that quantify, therefore,
not only the percentage of full time equivalent or FTE, that is, professional figures that are
moved to other dimensions thus freeing up resources for that type of task, but new capabilities
that Watson's cognitive systems offer are also proposed because of the intrinsic capabilities
that they can express, correlation analysis between multiple sources of data, which were not
previously seen, or even their conceptualization, it is possible that a company discovers,
thanks to those cognitive technologies, to have so much wealth in the data to be able to almost
open new forms of business or launch new start-ups internally. And this is also happening a
lot in the financial and banking sector, where banks discover that they are almost capable of
generating new business thanks to the amount of information that informs about the behavior
of individual customers.
Interviewer: IBM Watson is a system that helps organize large amounts of data and find
complex answers. How is this system used to help employees and managers make more
informed decisions?
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Interviewee: As we said before, in collecting a greater extension of data, in encoding its
relevance to the specific decision-making domain and in allowing also a human
understanding, therefore a summarization, a more transparent visualization of data to the
human user, an empowerment is made, that is an enhancement of the capacities of the
interlocutor or decision-maker, and therefore, a greater decision-making capacity is allowed
because it is based on data that really people have at hand, because they have processed them
according to classifications that are then screened by the experience of managers and subject
matter experts, and that therefore this technology allows to be much closer to the empirical
evidence of the business.
Interviewer: Also the fact that being less time consuming, for example, a doctor who has to
make a decision about a patient through IBM Watson that provides him with the data he needs
instantly, he or she can make better decisions because he has less time to spend to reach this
information.
Interviewee: Surely summarization in the field of content analytics is very important, but in
this case it can also be very important for the physician, given its functionality, to understand
how this summary of information happens, and once the technology is considered reliable and
there is trust in the solution, then yes the physician can quickly take advantage and process
the information and indeed, also find diagnostic hypotheses that can then be refuted or
validated and together with the patient proceed to a further collection of evidence and maybe
even a better dialogue and better involvement of the patient. This observation is very important
because if we think about the patient medical relationship, it certainly frees up new resources,
increases awareness and also allows for a better relationship. We must say, however, that this
has, say, a push that could be excessive when, for example, by virtue of these technologies we
ask the doctor to examine ten patients per hour against maybe four that should be considered
at most as fair to meet, there are therefore deformations, as say, the technology can help, can
make an empowerment, the important thing is that you do not go too far because then you
would then go to lose in effectiveness, in grip, without paying attention to other dynamics.
"Est modus in rebus", as the Latins used to say, that is, there is a way in things, so it is the
push towards the automation of certain processes, but also the attention that at least some
levels of quality must also be offered among human beings because the dynamics of
interaction, in this case patient-doctor, also requires processing times that are the result of
human capabilities, not Watson's automatic ones.
Interviewer: How can people apply IBM Watson’s experience gained in some countries to
other geographical areas? I think of a project in North America using Watson, applied in Japan
or in Italy...
Interviewee: This question is very interesting and also enhances the international and
multicultural nature of IBM, as you say in your thesis in a very interesting way. Surely
humanity is crossed by common needs. In the Maslow scale you have a first representation
from the simplest to the most evolved needs. But even science has questions that unite all
humanity, for example, recently the media exposure is about how we are managing our planet,
if we are already really at the end of it and therefore to our extinction or we are still in time to
do something. So the ability to recognize that some questions are common to all humanity
both on a scientific level but also on a business level because all companies that want to do
business want to make money and get resources from the supply chain of products and services
and want to do so in a respectful and hopefully sustainable way. So, in the face of common
questions, cognitive technologies are proposed as tools to reach these common answers to
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humanity that populates all different geographies and latitudes, and then Watson technologies
present themselves as a point of unification and intelligence with respect to questions that are
common to the whole world. A subtle aspect is that if we all use the same way of processing
information and responding, there is a risk that if an approach is erroneous, it will have global,
international repercussions on a global scale. So even in this sense, the cloud must be seen as
an element of enhancement of the available computing resources and also of statistical models,
but also always in the ethical aspect, to make humanity robust with respect to any model
errors, we must also try to cultivate a heterogeneity of interpretation so that there can also be
a variety of understanding of complexity, not only understand what is right or wrong in time,
but it is also right to cultivate different ideas. But the important thing is to make human
intelligence evolved too, that is, not only to destine our future to artificial intelligence but also
to be able to outline our future and not only to believe that AI, like other technologies, can
solve problems that instead require a polyvalent effort.
Interviewer: Are there any other topics, areas, or information you consider relevant to the
research that was not considered in this interview?
Interviewee: In my opinion a very important element are the ethical aspects that you raised at
some point in your proposal and that find in IBM a very strong answer ranging from data
privacy to the construction of models as realistic as possible and whose purpose is in favor of
human and not against human, a bit like the Asimov principle of robotics, but also an increase
in the sensitivity of what it means to allocate much of our decision-making capacity to
algorithms. Think even only of speculative bubbles on the stock exchange, many of these
speculative bubbles are generated by algorithms, basically, whose parametric functioning has
not been designed for some contexts, and when different contexts arise, the scenarios of these
algorithms have not worked anymore, that is, the decisions taken by these algorithms were no
longer consistent with the main objectives. So I think that one element of your thesis could
be to cultivate a strong sensitivity to these issues, have a greater culture and technical
knowledge not only of marketing, what AI means, what implications it has and in what
context, and also to enhance the cultural and multicultural element and therefore how now
globalization is pushing not only in the use of common currency and currencies that
financially bring countries closer, but is also moving to representational levels, or rather the
knowledge that these technologies are also sharing in a somewhat hyper-dynamic way with
respect to our human capabilities, is also a bit levelling or reducing the distances between
countries, and this can be an advantage on some dimensions but not on others, because if all
of us become very equal in the way of thinking and in the way of acting, the possibility that
an element that affects our behavior as a person can then spread to all others because we are
all equal, while it is important to have differences.
4. IBM AI IBM Watson Explorer Architect - IBM Analytics Europe
Interviewer: What is your role inside IBM?
Interviewee: I am what is known as a Technical Seller, that means I instruct clients on how to
use our software, most effectively, so in most cases what happens is a salesperson, they find
an opportunity for sales, of our software and I then help support our sales team by explaining
to the customer what our software does, how to use them and how can be used for them to
generate business value.
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Interviewer: The use of IBM Watson applied to Knowledge Management allows users to
analyze large amounts of structured and unstructured data and easily reach the information
they need. Could you tell me about your experience at IBM?
Interviewee: So, I have worked with Watson solutions since Watson became aggrouped within
IBM, that’s 7 years ago. My work with Watson solutions actually pre-dates the formation of
Watson or creation of Watson, these technologies have been around longer, some of them,
and I have been working with those for about 10 years and I have not been using these for
IBM, just to be clear, I have only used them for our customers. I don’t know to what extent
IBM uses these tools.
Interviewer: IBM uses Watson’s cloud system to easily and immediately reach large
populations of employees and customers all over the world, therefore spreading large amounts
of information. Can you tell me what the main advantages of this strategy are?
Interviewee: Right, so once again I am going to tell you what the advantages of this strategy
are for the client, for the customers I work with. Not for IBM, because I am not part of the
decision-making in IBM, so I can’t really think of some application, I have not really been
involved with applying these tools within IBM, but I have been using them for the customers,
okay? So just to be clear about it. Okay, so the area as I work with is unstructured data, or
textual data, or what we call content and the main part of the content that work with this textual
nature so think about documents, think about word documents, PDFs, e-mails or tweets,
SMSs, anything where there’s written text, or human beings, has to read the text and decide
for the course of action or getting some information from that text, I work with tools that help
those organizations extract meaning from that text and therefore not just one meaning from
just one document, e-mail or whatever but from thousands, tens of thousands, up to tens of
millions of documents, okay? So the mere sign that we can reach documents, that volume, and
interpret them and understand them in a way that a business person would interpret and
understand them, means that we can understand and analyze orders of magnitude, more data,
unstructured data, that any human being could, and we can do more accurately and
consistently. Human beings are actually quite poor at understanding and interpreting textual
data, so a system like Watson can do it more consistently and more accurately and to way
more, you know, at a much higher volume, speed, that a human being could. So this can have
profound value across an organization, any process where a human being is relied upon to
reach something, and understand what it means to then take the next step or to start a new
process or to initiate any kind of action can now be, textually be, semi or fully-automated,
with a Watson solution.
Interviewer: Corporate communication is increasingly interactive and unstructured: it uses
chat, images, audio and video. How does IBM Watson allow users to generate new levels of
knowledge and share information easily and effectively?
Interviewee: Right, so again I’m focusing on textual benefits at the area that you live in, well
the answer that I gave applies to this as well, the volume of information shared with the
employees and gained from employees is far too vast for any one person or group of people
to effectively analyze, interpret and use in any way and Watson allows us to do this very very
quickly and also more actively and reliably than a human being could. So, with relations we
have with employees we can understand them at a macro level instead of just at an individual
employee level, which is what happens in most organizations.
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Interviewer: IBM Watson uses processes that simulate the human mind through neuronal
networks. So, it uses a learning system, the so-called machine learning. Isn't there a risk that
in the teaching phase, even in an unconscious way, incorrect or obsolete instructions are
inserted? What could be the negative effects?
Interviewee: Yes, that’s certainly a risk, but that’s not the only way that we teach Watson or
Watson solutions have to understand textual data. Machine learning is one approach, there are
other approaches we use as well, with equal effectiveness just depending on the situation we
use one approach or the other. An example is using linguistic rules, which are much more
determinate and in some cases much more effective, in other cases machine learning is more
effective, so if you’re just going to rely on machine learning without any kind of governance
around, who does the machine learning? Who gathers the training data? And how the training
data is used? And that is a real danger, in fact that will absolutely happen. But in the techniques
we use, first of all, we provide also the tooling for governance, and for data quality analysis,
and then, as I said, we don’t just use machine learning methods, that’s just one of the
techniques. Another major technique we use, linguistic rules, helps to mitigate that risk.
Interviewer: What are the implications of personal data protection in the use of Watson and
what are the possible impacts in knowledge management?
Interviewee: That’s a rather broad question, I am not familiar with all of the personal data
protection, legislation in Europe to give you a comprehensive answer. Certainly, the principle
that personal data belongs to the person and not to the organization that speaks to leverage it
has a profound effect on anything not just unstructured data but also structured data, any data
we have from, any organization has from customers. So requires an extra level of vigilance,
and care in dealing with the data so, for example, a chat session with an employee or a
customer is just as sensitive as that person’s birth date and address and needs to be handled
that way. So the repercussions or the effects are quite substantial insofar that we must take
care to understand that this data does not belong to, our customers must take care, that
understand that the data does not belong to them it belongs to the individual person. But, you
know, that’s for any bit of data so that answer applies to any data we collect about the customer
involved. I will say one thing. I can say about the policy and that is any customer that uses
IBM services, cloud services, can usually opt out to of having IBM do anything with the data
they work with. So, what I mean by that is, when you use a Watson service, you are sending
data or using your own data to train the service. Unlike our competitors, IBM will not, if you
do not wish, IBM will not learn from that data, but if you do that in Google Cloud or Microsoft
Azure, they will learn from your data, and incorporate that into their algorithms.
Interviewer: Is the use of IBM Watson by employees or customers complex or easy to use?
Does it require special technological skills?
Interviewee: Ok, so there are two different groups, end-users, which is complex or easy as
you make the solution, Watson solutions are just a way of building an application for an end-
user, to do something. The tools themselves to configuring use are generally very straight
forward, and easy to use. It’s a question of getting comfortable with them and the greater
challenge is giving accurate and effective data with which to train them.
Interviewer: IBM Watson has transformed the way we manage the knowledge of employees
and managers. How was knowledge management handled in IBM before Watson was used?
So how was the corporate intranet used and how did the way to reach and share information
change after the introduction of IBM Watson?
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Interviewee: I can’t really answer that question effectively for you, sorry, I don’t have insight
on how this was done before and after.
Interviewer: Did the use of IBM Watson optimize the use of human resources? And has it led
to "cost savings" on IBM’s part and has it been quantified?
Interviewee: Once again, if we’re talking about IBM as the user of Watson, I have no insight
about any cost savings we or we have not realized using Watson solutions.
Interviewer: IBM Watson is a system that helps organize large amounts of data and find
complex answers. How is this system used to help employees and managers make more
informed decisions?
Interviewee: Well, once again I am not speaking for IBM, I am speaking for the customers I
work with, okay? Just to be clear. Well, the main problem with trying to use these services
that are based on a certain level of unstructured data is if human beings are just very poor at
understanding of processing any data, any amount of data really, so the first thing Watson
does by simulating how we interpret and understand textual data, Watson allows us to get to
a much larger amount and process more consistently and reliably, so that cuts across almost
any interaction between managers and employees you think of, in an organization where
textual data is being used, it’s more accurate, it’s more reliable and consistent. So that can be
any process, that can be HRM process, we’re trying to figure out who’s performing best based
on reports of their work, that’s based on, that could be employees’ surveys, trying to figure
what employees think of their organization, customers surveys, anywhere where textual data
is at the heart of the process, Watson provides a way of analyzing that much more effectively.
Interviewer: How can people apply IBM Watson’s experience gained in some countries to
other geographical areas?
Interviewee: Well, Watson solutions are available in two general flavors. The IBM Cloud
solutions are online and accessible to anyone who has internet access, so anyone can get it
online, go to IBM Cloud and start, and provision a service, and use it. You can also buy a
license for some forms, almost all of them now IBM, download it and run on a private cloud
behind your firewall. Watson solutions in general support 11 languages and can score up to
20 languages depending on what you’re trying to do. So we also have a comprehensive list of
language support so if you’re working in German or French, it can translate the data, so it is
actually quite easy from anyone outside the United States, in the Netherlands, in Western
Europe or Asia anywhere really as long as you have internet access you can use Watson.
Interviewer: Are there any other topics, areas, or information you consider relevant to the
research that was not considered in this interview?
Interviewee: No, not really. I hope some of the information I offered was useful.
5. Stefano Maffezzoli Felis: IBM AI Cognitive & Analytics Consultant
Interviewer: What is your role inside IBM?
Interviewee: I'm an IT consultant, let's say, broad-spectrum, but specifically in the last period
I've mainly dealt with configuring chatbots with Watson Assistant which is one of the services
that IBM has at its disposal related to IBM Watson, then let's say about the Assistant is the
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one on which I'm most focused, the others, I know some others in a tangential way, I saw
them, I did a little bit with them, but about the Assistant is the one on which I'm most focused.
Interviewer: The use of IBM Watson applied to Knowledge Management allows users to
analyze large amounts of structured and unstructured data and easily reach the information
they need. Could you tell me about your experience at IBM?
Interviewee: I had direct experience with this aspect, for a project in which they were
essentially fed to a Watson service that in this case was Discovery, the documents that were
passed were essentially supply contracts that the customer had with its suppliers. So within a
data structure, let's say more or less predefined, because in the end the structure of the contract
was more or less always the same, what varied was the content, then the services provided
varied, the supplies involved etcetera, because Watson was able to find this information in a
document of 20, 30 pages, in this way it was easy for the end user, in this specific case through
a dashboard, so that not having to read all the documents to understand which was the
document that interested in the specific, what were the supplies of a particular document, this
is one of the strongest aspects in general, is often used. Another example quite similar, this
instead went on a wider document spectrum, in this case what I just told you were well-defined
documents. This other case here instead is the customer who needed to understand, let's put it
in a more trivial way, whether or not to trust their suppliers, so if there were managers who
had a pending suit or if companies had been involved in trials, if they had passed them off as
guilty or as innocent, so understand if the supplier with whom he or she was interested in
making a particular agreement was trusted, and even here Watson did a whole first part of
information retrieval through sites, newspaper articles, specialized articles, specialized sites
that collect precisely this information. So the first part of the documentation was collected and
this was done in a non-cognitive way, the cognitive part was then to process a series of
information collected in these documents depending on a model and then to present to the user
who wanted to know about a particular provider if it was a supplier to trust or not, also there
through a dashboard, so one just needed to find out that instead of having to be on the Internet
or through various sites an internal documentation, so that was the interesting data of that
provider in a platform the Watson model automatically tells you good, bad or intermediate in
short. I don't want to make it simple, but this is the concept, that is, it is able to find the
information already and process it in order to give you a first evaluation.
Interviewer: IBM uses Watson’s cloud system to easily and immediately reach large
populations of employees and customers all over the world, therefore spreading large amounts
of information. Can you tell me what the main advantages of this strategy are?
Interviewee: First of all, it's not necessary to have to create something that runs on the
customer's hardware systems, so there's no need to use their systems but the fact that it's cloud
can then be reached by, that is, it's developed directly in the cloud so it can be reached from
anywhere in the world you want, let me say it in a really bad way. Mainly this is the advantage.
Moreover, living all Watson services, living in the same cloud environment, they are also
easily integrated with each other because many times more services are used, for example
what I told you before, which analyzes the documents, takes out the insight is used very often
along with the one to do the chatbots, so then the insights are returned in the form of chat. In
this sense, it is also quite easy to integrate them.
Interviewer: Corporate communication is increasingly interactive and unstructured: it uses
chat, images, audio and video. How does IBM Watson allow users to generate new levels of
knowledge and share information easily and effectively?
Interviewee: In practice, putting together what I have said so far, in the sense that new levels
of information can be found by analyzing large data, the large amount of data does not allow
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an analysis made by a single person, so it is easy there to take the information for her
knowledge, and this is what I told you before the documental part or otherwise information of
the large data. Through the chatbot, to interact with the user is used a graphical interface, that
is programmed in a standard way, without anything cognitive, but thanks to that and also the
information that the chatbot retrieves, everything that the cognitive system retrieves, you can
provide the user with a view of the information varied, therefore in the form of chat, in the
form of video, in the form of images. Other ways at the moment do not come to my mind, I
would say that Watson Assistant, so the chatbot side, is what I think is the most innovative
way and that allows more variety in sharing information and knowledge.
Interviewer: IBM Watson uses processes that simulate the human mind through neuronal
networks. So, it uses a learning system, the so-called machine learning. Isn't there a risk that
in the teaching phase, even in an unconscious way, incorrect or obsolete instructions are
inserted? What could be the negative effects?
Interviewee: Yes, the risk obviously exists, and that is why it is a very delicate phase of a
cognitive project, the training phase. First, let's say that a perimeter of knowledge is created
within which Watson will be trained, and this already allows, let me say, to limit possible
external influences. Secondly a job is done with those who are experts in the field to identify
how to create these cognitive models, of course a mistake is always possible and that is why
I said that is a complicated phrase, is one of the longest phases and where more attention is
taken in the period of creation of a cognitive system, just for what you said. It is quite mitigated
by the skills and experience of our consultants, I'll put it this way. However, I don't know if
you were there, it happened some time ago, that Google had made available an artificial
intelligence that self-learned and that after a while had to close for the disastrous. It was
something that came out some time ago, because they used an automatic learning process,
while what we do is all supervised learning, mainly, precisely to avoid these errors and
falsifications.
Interviewer: What are the implications of personal data protection in the use of Watson and
what are the possible impacts in knowledge management?
Interviewee: The management of personal data is a fairly delicate issue, in fact it is usually
integrated with, from personal experience, whenever it was necessary to use personal
information of users, to make you understand I make an example, when it came to dealing
with banking users, so it was necessary to identify the user who was asking about his cards
information, let me give this example, of course it was necessary to go to retrieve personal
and also sensitive data, and this has always been done an integration with the internal services
of the customer and anyway the security aspect of the cloud in the case of IBM is something
that is constantly monitored, is one of the most important aspects of course. What impact can
this have on the spread of knowledge well of course always to give you the example of credit
cards, you can retrieve interesting information for a user who interacts with Watson without
having to necessarily having it give it to you, for example I log in to the chatbot and
automatically retrieves a whole series of related information. The chatbot intended to answer
questions about a bank account, I log in and as a human operator on the phone can enter and
tell you the information, even the chatbots, even Watson in general is able to retrieve it and
give you precise answers indicating your data without you necessarily have to first give
specific information, simply knowing who you are, Watson is able to know what are your
cards and so on, information of this type. So, it's a much more human interaction, as if you
were talking, interacting with a person.
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Interviewer: Is the use of IBM Watson by employees or customers complex or easy to use?
Does it require special technological skills?
Interviewee: No, it depends, but on the part of the end user of a Watson service it does not
require any competence, because in the end as I told you, if it is a chatbot, it is like interacting
with a person, if a service is not a chatbot anyway the user just does a search, and then all the
cognitive aspect takes place in a hidden level from the user so he does not need to enter directly
into the cognitive sphere. As for the employees of a customer who wanted to interact there
too, we say there are different aspects depending on whether they are the holders of knowledge
then they want to be the first person to feed the cognitive knowledge of the Watson system
that was implemented then there is a minimum of complications in addition but it is still a
matter of using services that have already been developed and then the configuration is quite
simple, there is no need for great skills even from the point of view of configuration because
usually we as IBM provide customers who want to have this autonomy in knowledge
management a minimum of training, but technical skills no, just minimum knowledge of how
to use a computer and little else.
Interviewer: IBM Watson has transformed the way we manage the knowledge of employees
and managers. How was knowledge management handled in IBM before Watson was used?
So how was the corporate intranet used and how did the way to reach and share information
change after the introduction of IBM Watson.
Interviewee: Here, too, I give an example from my own experience, and as I told you I mainly
did chatbots. I did a project for a client who wanted to minimize the interaction between their
employees with the internal help desk and so what happened, that users call the help desk
saying to connect from the remote to their computer solves the problem, so there was this
direct interaction to solve the problem. With our chatbot we made it so that a whole series of
problems, let me say, the obviously most common ones, the difficult ones actually need to
have the intervention of a technician, but all those that were easily solved by the help desk by
connecting have moved, have moved the solution from the user who at this point asks how it
is done, the chatbot is able to give him a simple guide, and the user is able to solve it by
herself. So, let's say the change was in this step, so now the help desk only receives those
requests for help that are really difficult to resolve on their own. So, there has been a bit of a
transfer of knowledge and resolution from the help desk to the end user, the end user is now
able, thanks to the chatbots, to solve certain problems on their own that before did not even
consider the possibility of doing.
Interviewer: Did the use of IBM Watson optimize the use of human resources? And has it led
to "cost savings" on IBM’s part and has it been quantified?
Interviewee: From IBM I can't tell you for sure from the customer who implemented the
system because obviously every call to the help desk in this case or every call to the various
services of the desk has a cost and even simply a cost of an hour, of time used by the desk
operator who is now able to focus only on those more serious issues and therefore also provide
a better service, more punctual to that user who really needs it. So, saving time for those that
are minor issues in favor of major issues. So let's say there is a broad spectrum gain.
Interviewer: IBM Watson is a system that helps organize large amounts of data and find
complex answers. How is this system used to help employees and managers make more
informed decisions?
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Interviewee: Generally speaking, the simple fact that you don't have to read several documents
by yourself means that, in the meantime, it is time saving. I have to read 10 documents to find
insights, obviously it takes a while. In this way, however, the fact that the documents have
been pre-processed by Watson allows the user to focus only on what is important data, or
rather to have more important data. Let me give you an example for what medical research
could be, which is based from the point of view of the end user, so let's say the doctor, on
reading academic articles, which in addition to being complex are also many and varied, so
staying up to date on what are the news and improvements is obviously a considerable waste
of time. In this way, thanks to the use of IBM Watson it is possible for a doctor to feed, so to
speak, the system a whole series of documents and get that information. So to be able to have
a first processing and then of course you have to look at the human component to go and
analyze in detail but, let's say, there is a component of considerable time gain.
Interviewer: How can people apply IBM Watson’s experience gained in some countries to
other geographical areas?
Interviewee: The models behind it, although similar, obviously vary depending on the
situation, let’s say, both the climate and that from state to state. But having matured, having
developed a certain project in an area of the world allows us to have a background, a starting
point, that is, not to create something new from nothing. It allows from the point of view of
the system of course but also from the point of view of the ability and knowledge that have
been used to create a particular project. A project, however similar, brings with it variations
from what is the information that is being used. So, for example, a project in North America
that takes into account the climate and the political structure of North America brings with it
a whole series of knowledge that, however, must be revised taking into account the situation,
for example, in Italy. However, it is essentially in the internal knowledge, so they have
developed the knowledge on a project that is then proposed on another project with the
appropriate updates.
Interviewer: Are there any other topics, areas, or information you consider relevant to the
research that was not considered in this interview?
Interviewee: Actually, no, it seems to me that the spectrum has been quite adequate.
6. IBM Senior Watson AI Consultant
Interviewer: What is your role inside IBM?
Interviewee: I am in the Watson AI area, so the team of consultants dedicated to Artificial
Intelligence projects. It's a cross-industry team, so we're not specialized by industry, but we're
our own Artificial Intelligence product specialists, so we focus on the methodology of
applying these projects regardless of the field.
Interviewer: The use of IBM Watson applied to Knowledge Management allows users to
analyze large amounts of structured and unstructured data and easily reach the information
they need. Could you tell me about your experience at IBM?
Interviewee: I can tell you a lot of things, in particular, unstructured data research is a
revolution. There is no longer a limit to the sources, new problems have arisen, problems
concerning which information is relevant, which are true, which are the most correct. I speak
especially with regard to companies, the figure, which does not have to be necessarily the last,
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that is the most correct. For example, if I search the Internet for a trivial Google search, which
is news, I look at what was last published and generally I consider it correct. This is not valid
in large companies where knowledge on the various portals has different certification times.
Let me give you an example a little more in detail, when a law changes the first knowledge
base that is impacted will be a single credit text for banks so all those that are the rules that
then regulate the processes, procedures, so it takes for example 2 months to update this
legislation and in the meantime the underlying changes, procedures, and processes remain
misaligned or have already recognized them.
Interviewer: What are the implications of personal data protection in the use of Watson and
what are the possible impacts in knowledge management?
Interviewee: The protection of personal data varies from project to project, it is not so much
Watson but the type of project you are dealing with.
Interviewer: Is the use of IBM Watson by employees or customers complex or easy to use?
Does it require special technological skills?
Interviewee: It depends on the type of application, some can be used even if you do not have
specific knowledge and then it is enough, others instead require the technical knowledge of
experts.
Interviewer: IBM Watson has transformed the way we manage the knowledge of employees
and managers. How was knowledge management handled in IBM before Watson was used?
Interviewee: Before IBM Watson, knowledge was managed through repositories of
information and it could happen that the information entered and shared could be contradictory
to each other. IBM Watson has transformed the way information is managed, making this
process more transparent and effective.
Interviewer: Did the use of IBM Watson optimize the use of human resources? And has it led
to "cost savings" on IBM’s part and has it been quantified?
Interviewee: I don't know about IBM data because I work with external customers.
Interviewer: IBM Watson is a system that helps organize large amounts of data and find
complex answers. How is this system used to help employees and managers make more
informed decisions?
Interviewee: A single knowledge management on the one hand, then in some contexts such as
research, the ability to process a quantity of information that has no precedent, can only be
able to read so much information and propose it to a specialized professional already a sort of
summary on papers that would have taken at least months to search for, certainly provides
much more information than before, makes the decisions of the professional also more
facilitated by more information, then the decisions can be made by the professional, but
enabled by more information.
Interviewer: How can people apply IBM Watson’s experience gained in some countries to
other geographical areas?
Interviewee: Internally we have, precisely because it is such a cutting-edge area, every single
experience in the world is important, because it is a competitive advantage, every experience
brings a lesson and must be shared within us. Often, however, these experiences are linked to
the language, because these tools are often linked to the language. So we try to make the most
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of the experiences of others, some things can be deepened more easily, others less, however,
as a methodology, as project structuring this facilitates a lot, therefore in the management
timescales, in the most effective ways of managing this data, because it was one of the major
challenges, and in the management after the project, so the tools, monitoring, security
considerations and so on.
Interviewer: Are there any other topics, areas, or information you consider relevant to the
research that was not considered in this interview?
Interviewee: Truth management has been one of the main topics of discussion for a long time,
it is one of the dilemmas because the training part is one of the most difficult of these systems
and there is the shortcut of self-learning. In some cases it works well, in other cases it does
not work so well. Famous are the cases of a bot on Twitter who had been self-trained, so he
continued to take as examples other situations on Twitter and became sexist, racist in a short
time, and so they had to close it. Another case was that of Instagram that used hashtags to do
self-learning and that as well had become racist tagging people of color as gorillas, so there
are a number of situations when these systems are not controlled in which what is generated
is not exactly what was desired, that is why we go to check the training base that we provide.
We avoid giving, at least in situations, it is a design choice so then based on the situations you
can choose to continue to give examples to the machine in an unsupervised way but in general
you try to give, especially to private bodies, the ability to control what are then the examples
and associations on which the machine then relies to behave.
7. IBM Information Technology Architect – AI IBM Watson Development Squad Team
Interviewer: What is your role inside IBM?
Interviewee: I work in a team named Watson's Code, and my job is mainly to meet customers
and develop propositions for customers of mainly, but not necessarily, cloud-based solutions
that use artificial intelligence for all those scenarios where artificial intelligence can help or
improve the effectiveness of certain processes.
Interviewer: The use of IBM Watson applied to Knowledge Management allows users to
analyze large amounts of structured and unstructured data and easily reach the information
they need. Could you tell me about your experience at IBM?
Interviewee: IBM has been working on artificial intelligence for many years. The problem
with artificial intelligence is that in order to have systems that can be applied in a commercial
environment a large computing capacity is needed, precisely because artificial intelligence
uses unconventional algorithms based on technologies that have been well known for years
but that require considerable computational power, we are talking about technologies such as
neural networks, machine learning and so on. The advent of the cloud, which is precisely the
ability to delegate the computation of processes not to the laptop in front of you on your desk
but to much more complex systems that are geographically distributed and optimized for
performance, has allowed IBM, as clearly to other actors on the scene, to release solutions for
artificial intelligence that are no longer simply confined to research laboratories but also
within the reach of anyone, we can say, because then today even the individual citizen,
registering on the IBM cloud, can develop their own artificial intelligence solution, simple, as
you like, but fully functional, at no cost, because many of Watson's artificial intelligence
services are free for a limited use. So what happened is that starting from the solutions that
have been developed in the medical field that were in part the first developments of artificial
intelligence applied to current business areas, so systems to help doctors to make diagnoses,
to find evidence and to cross-reference patients' data, we then moved on to solutions in the
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financial and banking areas, and then solutions for call centers, for telephone companies, up
to now where it can be said that almost all areas of business can take advantage of the
usefulness of artificial intelligence precisely because compared to the past it is much easier to
build solutions.
Interviewer: IBM uses Watson’s cloud system to easily and immediately reach large
populations of employees and customers all over the world, therefore spreading large amounts
of information. Can you tell me what the main advantages of this strategy are?
Interviewee: The advantage is certainly to be able to include in the technologies used but
especially in the business processes used, a whole series of data that were previously present
but could not be used, think for example of all the unstructured data such as texts. A telephone
company has hundreds and hundreds of thousands of complaints sent by e-mail, recordings
made by the call center about complaints or requests, it also has all the opinions that are poured
every day on social channels and so on. In the same way, perhaps the bank has all the
transcripts of the bank operations, all the requests that are made, so also the insurance and so
on. Therefore, there is a whole amount of input that companies receive and that cannot be
dealt with traditional technologies. Traditional technologies grind numbers, do the processing
on structured data, so obviously if I give them the text of a customer complaint they don't do
much. Let's say on the other side, there is all the knowledge in terms of internal knowledge of
each company, so all the experience, for example the experience of a repair technician, on
what are then the options or best practices to implement to repair a certain problem, to redress
a certain situation, all the knowledge of those who, for example, knows the insurance policies
of a company and then is able to answer even strange questions, whether they are the duration
of a contract or the cost. I give you a very simple example, if I phone an insurance company,
animal policies, and ask "I have a pit bull dog, can I insure it?", It is not a simple question
because you need to know all aspects of the policy because the pit bull could be a breed
considered dangerous and therefore perhaps is not covered by all types of policies and so on.
So, what does artificial intelligence allow us to do? On the one hand it allows us to decipher
all the non-traditional inputs that arrive, on the other hand, it allows us to schematize, classify
and make accessible, even from the automatic flows, all the information that is typically
managed by people. From what I have said one could imagine that the human being is
completely excluded. Obviously, this is not the case, because these systems are not, let's say,
an alternative to human intelligence, but simply serve to help people, hence humans, human
operators, to do their work more effectively. In the previous case, if I ask the question to an
operator about the insurance policy, while in the past the operator had to go and get the
contract, read it, interpret what was written, now he can turn my question immediately to an
automatic system which, if it does not already give her the answer nice and ready, it highlights
all those parts of the documentation where there are the answers, and this saves a lot of time
because perhaps there could be a general clause on the policy, but maybe there could be a
database of real cases in which perhaps an insurer wrote "We had to do a review to a certain
contract because there was this situation with a particular breed dog" and so on. So I have the
opportunity to access various databases with various points of view and artificial intelligence
allows me to have an eye on all this information and then to make the best decision in a faster
time, because then in the end the decision is always made by a human, you will not have, at
least for the next few years, an artificial intelligence system that makes decisions, especially
in areas such as medicine or finance, that is, the final decisions are definitely up to the person.
The artificial intelligence system unlocks a whole series of potentialities that were not
accessible in the past.
Interviewer: Corporate communication is increasingly interactive and unstructured: it uses
chat, images, audio and video. How does IBM Watson allow users to generate new levels of
knowledge and share information easily and effectively?
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Interviewee: I have already answered this question in part with the previous answer. I will add
a few comments. Having the possibility now to manage unstructured data and process them,
what is important is to create a sort of simplification even for those who then have to
implement these solutions, and therefore IBM, on its Watson platform that we can imagine as
a fairly centralized platform with precise strategies implemented centrally, has implemented
a whole series of services that are divided by capabilities, that is by focused capabilities, and
that can then be put together to build solutions that expose precisely intelligent capabilities.
For example, we have a service called Watson Assistant that specializes in interacting with
human sources, and therefore is able to sustain a chat conversation, so it can handle things
like understanding the purpose for which someone is writing something to you, or managing
the recognition of concepts and entities within user sentences, and can also manage the levels
of dialogue, if my interaction is not a yes-no question but is based on a dialogue, the Assistant
system is able to retrieve pieces of information maybe that had been said 3 or 4 interactions
ago, and combine them in a consistent context, for example to help, I don’t know, a person
who can no longer enter a site, to understand what may be the causes. So understanding where
the person is calling from, what kind of user he has, what technological tool he is trying to
access, to give the most useful answer, then to put it in communication with someone who can
help her, simply if it is possible to point out to her to be able to remedy herself. For example,
we have the system of speech-to-text and text-to-speech, which is then able to interpret the
natural language and transform it into digital text. You can already understand that by putting
these two pieces together I can build a virtual assistant that is able to talk to people, and listen
to what people say, because if the person speaks into a microphone, as I am doing, the speech-
to-text intercepts the voice, turns it into text, and sends the text to Assistant who processes the
answer or the next question, returns it to the text-to-speech service that in turn responds
vocally to the operator so in the end you have combined two elementary services and built a
wider capability than it was the previous capability. If you then add, for example, a service
that makes the analysis of the text in terms of finding concepts, relationships, entities within
the speech, for example, you have added a further piece. If you add a piece that manages for
example the management of internal knowledge, and therefore is able to associate to a certain
question that has been asked, the answers that come from what is my wealth of knowledge,
mine as a company, you added yet another capability, which is to give sensible answers to
questions. In this way, by assembling together cognitive services with clearly also traditional
services, I can build complex applications and solutions as you like, that expose even more
human capabilities at the service of people. We have not talked for example about Visual
Recognition, I can also put in all this the service of visual recognition that for example can
recognize in an image that I show, particular situations. I’ll give you a practical example.
Suppose I have a problem that I can't connect my router to the internet and then I call the
assistance of my phone company. If I have an artificial intelligence solution, the solution could
say "take a picture of the router", and I take a picture of the router, the system examines the
photo and from the pattern of red, orange and green lights that are lit, immediately reconstructs
what the problem is, while instead it would have been more complicated to say it by voice.
Interviewer: IBM Watson uses processes that simulate the human mind through neuronal
networks. So, it uses a learning system, the so-called machine learning. Isn't there a risk that
in the teaching phase, even in an unconscious way, incorrect or obsolete instructions are
inserted? What could be the negative effects?
Interviewee: The risk is there, and it is a very real risk. If you want, in the second phase of the
adoption of artificial intelligence this awareness has just taken over. The first phase was the
pioneering one of the great enthusiasm, everyone launched themselves a bit, at discovering
these things also because precisely the objective of being able to make people' work efficient
and to be able to delegate to the machines more repetitive, longer tasks, is obviously
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interesting for everyone, for all the companies. At a later stage, doubts began to arise. Can we
trust an automatic system? Can we be sure that it does not make mistakes? How do I get the
thermometer that what you are saying is correct? And it has been highlighted the problem that
technically is called the problem of bias, or prejudice, because these systems must be trained
and the training is done by human beings so if I take a person who maybe does a training just
like you said, or based on old notions or even based on incorrect notions, or spoiled by
prejudices, it is clear that the automatic system will learn the old story or otherwise spoiled by
prejudices. In this regard I quote the case of a company of our competitor who did an
experiment, left a chatbot open to all suggestions from users, a few years ago, practically in
24 hours the chatbot began to praise Nazism, just to say, because it is clear, maybe there is
someone who had a little fun with it and then has...
Interviewer: I had heard a similar thing with a chatbot story, but it was linked to racism.
Interviewee: It is not very easy, even beyond these extreme examples, for example, consider
the case of a system trained to recognize perhaps the person from the face to be able to
determine for example the age or sex, which is something that could be convenient in
surveillance systems that, without invading the privacy, clearly give a picture of how many
people there are inside a room, a shop and so on. Now what happens, that if, as it happened,
this training is done for example only and mainly with white people, the system when it sees
a person who is not white, but is, perhaps, of darker skin, does not recognize her, and this was
because, precisely because there was a bias, so an excessive polarization in the training.
Another example, if we consider an automatic system that for example must help not the
insurance in this case but the bank to understand if there is sufficient reliability to be able to
grant a mortgage or a loan, if those who do the training perhaps use, so to speak, data that are
mainly perhaps of young people, the system could consider as unsuitable, perhaps, the job
application of a person of a certain age, for the simple fact that it does not have sufficient data,
but that in fact it is something that goes to violate a very specific rule, namely that you should
not set limits related to age, sex, or geographical origin, but only objective limits related to
solvency. To avoid these situations IBM was, I think, one of the first companies to equip itself
with a layer that goes, let's say, to add itself to what is the operational chain of artificial
intelligence, and that is basically summarized in our solution called IBM OpenScale, which
has precisely the task of supervising the machine learning models, although the model seems
to us a black box in the sense that we do not understand how it works, OpenScale is able to
go and see the model while working and to pull out the criteria for which the model has taken
a certain precision rather than another. So for example in the previous case, if the machine
learning model says this person does not have the ideal characteristics to be able to receive a
loan, OpenScale can extract the key criteria for which those decisions were made, and then
the instant it tells you "the key criterion is age", I can realize that there has been a polarization
and then I can remedy. In the same way this thing is done on models for example of visual
recognition so it is a system that, let's say, is a 'guardian of artificial intelligence' and warns
me when there is something wrong, when there are areas in which decisions are made taking
into account certain variables and not others.
Interviewer: What are the implications of personal data protection in the use of Watson and
what are the possible impacts in knowledge management?
Interviewee: Impacts on personal data are clearly important. Artificial intelligence by itself
does not complicate and does not simplify the aspect of data protection because in fact the
presence of my personal data inside a server and the use that can be made of that data does
not depend on whether this use is made by a cognitive system or not. Even my phone number,
if it is used, if I have provided it to, for example, have a health care service, and it is used to
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make commercial offers, it is a use that violates the GDPR, regardless of whether it is an
artificial intelligence system or not to use it. From this point of view, however, IBM has a
very clear policy of use. First of all, the data of customers or users are never extrapolated from
what is the context of the solution, so, to give an example, if I do a training of a system for a
certain service and I go to a certain company, IBM ensures that both the training data and the
data that are then managed by the solution will never be used for different purposes, so that
training will not be used to build another similar solution, maybe for a competing company,
nor the data that this system then grinds will be used maybe to produce databases that can be
of help in other situations. So our cloud and in general our artificial intelligence solutions have
a very strict aspect of control over them, in our service level agreement it is clearly stated that
personal data belongs to customers, so to speak, to users, and are used simply for the purposes
that are declared to be used. However, it is obvious that this is an area in which we must be
very careful.
Interviewer: Is the use of IBM Watson by employees or customers complex or easy to use?
Does it require special technological skills?
Interviewee: The adoption of machine learning systems, contrary to what happened with
traditional information technology, has made solutions and especially the construction of
solutions much more accessible. Today we can say that services such as the Assistant or the
Discovery or the Natural Language Understanding, can be used immediately by anyone who
is able to work with the basic functions of a computer, in their general, universal version, but
also specialize one of these services on a domain knowledge, so for example on a specific call
center or specific insurance domain, is easily accessible to business users, that is, people who
do not have programming skills, but who are able, for example, to highlight what are the key
concepts to be extracted from a document, what are the typical questions that an artificial
intelligence system can receive, what are the answers to be given and so on. IBM Watson
systems are supplied with tools that are then user interfaces that greatly simplify the training
phase so that, almost without the need for any guidance from technical staff, even a business
user such as an insurance agent, the owner of a store or chain of stores and so on, is able to
build a working artificial intelligence solution. Obviously when it is then required the
intervention of a figure such as a data scientist or a programmer, when for example I have to
make integrations between these solutions or for example my databases. So if I have my data
on a certain system and I want the artificial intelligence system to use that information it is
clear that I will have to do a minimum of integration and typically this is a job that is done by
IT specialists or system engineers and so on, it will take an architect who designs a minimum
of solutions but like all other IT systems. The data scientist, that is, the expert of the
management of data processing, may be required when the complexity of the model is such
as to require a very high degree of customization. If, for example, I want to do something, I
don't know about a very articulated predictive model that takes into account many factors to
build medium-term forecasts, I don't know about certain market trends to understand which is
the product and so on, maybe a data scientist can help me and can use, together with the
business user, Watson solutions to build these very particular models that can then be inserted
into the solutions and then realize the functionality that is needed.
Interviewer: IBM Watson has transformed the way we manage the knowledge of employees
and managers. How was knowledge management handled in IBM before Watson was used?
So how was the corporate intranet used and how did the way to reach and share information
change after the introduction of IBM Watson?
Interviewee: Watson is currently used within IBM and is used in a variety of business
functions especially in personnel management, because artificial intelligence helps to find the
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right match between people's skills and the job. So, while before, for example, the selection
of the most recommended figures for a certain job was done manually by people, now there
are systems that allow you to better match candidates with jobs. And this is important not only
within the company but also, for example, for those who enter the world of work, therefore
graduates, of people who apply with their resume. IBM, but also other companies, now have
systems that by analyzing the curriculum, are already able, for example, to classify the skills
of people and direct them more wisely to a certain type of job than another. This is important
in my opinion because it allows you to overcome what was originally a more simplified
classification of the work roles that existed in the past, that is, in the past, I don't know, there
was the programmer, which was a very broad notion but we know that there are many
programmers depending on the knowledge, language, ability to deal with general algorithms,
maybe to deal with more specific skills that could be intended for very different and more or
less complex jobs. In an IBM division, until recently, there was a group of programmers highly
specialized in certain tasks and therefore had skills that if they were not then valued, there was
the risk they would remain a bit neglected. As a real example, I can do that of skill
management, that is I find myself today automatically all my skills added to the internal
system of human resources that traces my professional evolution, the system suggests to me
what are the most appropriate things that I have to put in my curriculum and does so in a very
careful way, that is, it does so on the basis of what is then the real evidence, that is, on the fact
that I may have intervened in a forum talking about certain topics, that I don't know, perhaps
released solutions for certain things, artificial intelligence processes these data and builds in a
congruous and concrete paths to help you then to develop your curriculum and your skills in
a more organic way. As well as the help in research, the fact of having Watson behind the
research helps you to find more relevant resources in terms of documents, people, than before.
So even in this sense, artificial intelligence has greatly changed the way of being in the
company.
Interviewer: Did the use of IBM Watson optimize the use of human resources? And has it led
to "cost savings" on IBM’s part and has it been quantified?
Interviewee: This kind of questions definitely needs to be asked to a person from human
resources. I guess so, but I obviously don't have the data, I don't have visibility for this, but I
guess so.
Interviewer: How can people apply IBM Watson’s experience gained in some countries to
other geographical areas?
Interviewee: Compared to traditional projects, in artificial intelligence projects and in any case
in cognitive applications, you must still take greater account of the human factor, in the sense
that being systems that basically manage unstructured information that comes mainly from
people, cannot be carried by weight, perhaps from one part of the world to the other and
adapted without a minimum of adaptation precisely. For various reasons, obviously the
language is one of these reasons, that is, a conversational system trained on English to be
brought to Italy must clearly be localized and the localization does not mean translating point
by point the various things, but then bring that same system into the Italian reality. In Italy,
for example, we have the phenomenon of dialects, so if I use a system of English speech-to-
text I'm quite relaxed that it works anywhere for phone calls anywhere in America, maybe
with some small tuning. If I take it to Italy and give it in the hands of a large audience, I can
be sure that I will have to do a lot of work for retraining because in Veneto they speak in one
way, in Campania in another and in Liguria in another. In the same way, maybe in some parts
of the world there is obviously less or more attention to give than certain aspects, what for an
Italian is a simple joke for an American can be a very serious outrage in terms of harassment
116
or bullying and so on and vice versa. So even in this case it is necessary to work a lot on the
tone, on the analysis of the tone, on the fact that maybe certain images or certain phrases can
be considered inappropriate. So, this is certainly an aspect to keep in mind.
Interviewer: Are there any other topics, areas, or information you consider relevant to the
research that was not considered in this interview?
Interviewee: We have mainly talked about applications in business fields, I personally care
about the application of artificial intelligence for humanitarian solutions, and then go and lend
a hand in all those realities where basically, beyond business, profit, there is instead an ethical
issue at stake. Medicine is certainly one of these, that is, if an artificial intelligence system can
help me make diagnoses or at least guide the process of anamnesis and analysis in order to be
able, let's say, to arrive more quickly at conclusions and help doctors make diagnoses in time
to save lives, this is certainly something on which I think we must give great strength and
great emphasis. IBM is very committed in the medical field. Another field on which, in my
opinion, artificial intelligence can help a lot is, for example, all the management of collateral
situations where there are issues of crisis linked to wars or health emergencies or situations of
natural disasters and so on. Artificial intelligence solutions that can help medical staff or
humanitarian associations to better understand the needs of people who may be refugees or
people in need of help, perhaps in different parts of the world, this is also the subject of the
previous question, and therefore can speed up the help that one can give, I do not know, for
example, understand where it is better to distribute food, or where maybe food is useless
because I bring things to eat and people do not eat them because maybe they do not like that
type of food, maybe allow to unify families that have been dispersed through maybe the
analysis of what may also be, I do not know, the physical aspects or otherwise the stories of
people and so on, that is, all situations in which so far clearly we could not do much, but that
with the advent of artificial intelligence we could have, so to speak, a support. I personally as
an employee of a company that is IBM, but also IBM as a whole, I see that we believe in this
thing enough and in fact IBM sponsors several events involving also internal people. This
year I participated in one of these events, called Call for Code, in which we are called to make
proposals and also to try to make prototypes that can then be used to create solutions to help
in contexts of humanitarian aid or medical aid of people in need, this I think is one of the best
things we can do.
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Appendix C: Data Supporting Interpretations of IBM Watson for
Knowledge Management
STRATEGIES
Artificial Intelligence is the main
strategy of big companies that
invest a lot of money in R&D
"About AI and IBM Watson "Well certainly artificial
intelligence is an element on which IBM has invested for
several years now and will continue to invest." (interview
with IBM Technical Solution Architect Cloud & AI
Cognitive)
"The MIT IBM lab is a new initiative just started last year
IBM has committed 240 million dollars to joint research
and we think of it as the leading academic and industry
alliance in advanced AI research" (Public Interview with
IBM Strategy and Operations Lead, IBM-MIT Watson AI-
Lab Mark Weber).
“The fact of having believed in advance in the
transformation that was able to bring cognitive artificial
intelligence to the time of machine learning, gave us a
very good advantage in competitive terms.” (Interview
with IBM Client Executive AI SME)
"We actually had to do a lot of work around the IBM
cloud private which is what Watson runs on […] Red Hat
is coming up and so this allows it to move anywhere out
there. This is a big piece […] of hybrid cloud which
you've heard me say we think that's a trillion-dollar market
and we'll be number one in it so that gives you a good
feeling" (Public Interview with IBM Chairman, President
and Chief Executive Officer Ginni Rometty)
"I work in a team within IBM that oversees from a
commercial point of view all the offers that IBM has,
among which there is certainly also the cognitive (IBM
Watson) that is one of the offers of IBM on which IBM is
supporting so much" (Interview with IBM Client
Executive AI SME)
Some companies use cloud
computing as their main strategy
"The two offers on which IBM is supporting are definitely
the Cloud as an infrastructure" (Interview with IBM Client
Executive AI SME)
"IBM is characterized by being a company founded on
two main principles of information technology, one is the
principle of the cloud and the other is the principle of
artificial intelligence" (Interview with IBM Client
Executive AI SME)
"When we mean Cloud we mean a platform that is both
distributed to our customers and to our centers, then our
idea is that if someone has the peculiarities even of those
of our competitors, we think that they should be used to
create the best possible service for our customers"
(Interview with IBM Client Executive AI SME)
"To create a platform that is accessible to everyone that is
IBM Cloud, within this platform one has at their disposal
all the tools with which to do artificial intelligence"
(Interview with IBM Client Executive AI SME)
118
"Our strategy in this field is that of the hybrid Cloud, we
think that it is the best way to make this transition to this
digital world that obviously embraces cognitive"
(Interview with IBM Client Executive AI SME)
FEATURES – COMPANY PERSPECTIVES
Artificial Intelligence can be a
centralized platform of integrated
services to manage all business
processes
"Living all Watson services, living in the same cloud
environment, they are also easily integrated with each
other because many times more services are used, for
example what I told you before, which analyzes the
documents, takes out the insight is used very often along
with the one to do the chatbots, so then the insights are
returned in the form of chat. In this sense, it is also quite
easy to integrate them." (Interview with IBM AI Cognitive & Analytics Consultant)
“So, the integration platform that you just mentioned that's
being announced is to allow you to manage data and
services and apps moving between these places and
communicating between them.” (Public Interview with
IBM Chairman, President, and Chief Executive Officer
Ginni Rometty)
"With an integrated platform, organizations can reduce the
cost of analytical toolsets, administration overhead, and
external consulting. Organizations can replace some
existing analytics tools and integrate data and modeling
tools in one platform, creating a “one stop shop” for data
analysis." (IBM documentation: Forrester (2019))
"IBM, on its Watson platform that we can imagine as a
fairly centralized platform with precise strategies
implemented centrally, has implemented a whole series of
services that are divided by capabilities, that is by focused
capabilities, and that can then be put together to build
solutions that expose precisely intelligent capabilities"
(Interview with IBM Information Technology Architect –
AI IBM Watson Dev Squad Team)
Artificial Intelligence can move
companies to digitalization
About AI and IBM Watson "Well certainly artificial
intelligence is an element on which IBM has invested for
several years now and will continue to invest. Alongside
these there will also be issues such as cloud computing,
issues related to Blockchain, and these are then linked in
the emergence, so to speak, in the concept of digitalization
of the enterprise of companies." (Interview with IBM
Technical Solution Architect Cloud & AI Cognitive)
"The business workflows become smarter because Watson
integrates into workflows to add AI where it is needed"
(Interview with IBM Project Manager Application
Automation)
AI and IBM Watson "“Enabling the company to make a
transformation to the digital world” (Interview with IBM
Client Executive AI SME)
Artificial Intelligence can be an
opportunity for business
innovation and foster the progress
of humanity
"The competence centers are those centers that are used to
bring this innovation [new AI technologies] into the
products and solutions that are offered and sold to
customers. So, they are more, let's say that point of
connection between what is researched, tested and created
119
in the research laboratories" (Interview with IBM
Technical Solution Architect Cloud & AI Cognitive)
"Concepts of process optimization, where through
optimization algorithms that exploit the platform as I said
before, for example, typical of data scientists, can find
those innovations and those steps that allow us to make
certain business processes more effective and more
efficient, from an analysis, for example, of the behavior of
various users, and so an analysis of the historical level of
what you do and then understand how to improve and
predict further action." (Interview with IBM Technical
Solution Architect Cloud & AI Cognitive)
"Improving productivity across AI teams creates
substantial business value: By increasing access to data,
improving collaboration between roles, and increasing the
speed at which data scientists can build models, data
scientists can spend more time generating and delivering
valuable insights." (IBM Documentation: Forrester
(2019))
"Firms are embracing more data sources on the cloud,
combining it with existing data on premises, and applying
analytics and AI on cloud to drive new insights” (IBM
Documentation: IBM Corporation (2019))
FEATURES – PEOPLE PERSPECTIVES
Artificial Intelligence helps the
human make better decisions
"Our strategy is always to be able to support the human
being in his decisions" (Interview with IBM Client
Executive AI SME)
"Doctors can’t possibly keep up with all of the data and
new studies being created every day, but Watson can scan
through millions of records for new data and treatment
suggestions. By showing where the information and
recommendations are coming from, Watson expands what
human doctors can do and provides them with resources to
make the best decisions for their patients." (IBM
Documentation (Morgan, 2017))
IBM Watson "provides much more information than
before, makes, even the decisions of the professional more
facilitated by more information, then the decisions can be
made by the professional, but enabled by more
information" (Interview with IBM Senior Watson AI Consultant).
"In collecting a greater extension of data, in encoding its
relevance to the specific decision-making domain and in
allowing also a human understanding, therefore a
summarization, a more transparent visualization of data to
the human user, an empowerment is made, that is an
enhancement of the capacities of the interlocutor or
decision-maker and therefore a greater decision-making
capacity is allowed because it is based on data that really
people have at hand" (Interview with IBM Senior
Managing Consultant & Research Scientist IBM Watson
AI & Advanced Analytics)
"The client who needed to understand [...] whether or not
to trust their suppliers, so if there were managers who had
120
a pending suit or if companies had been involved in trials
[...], so understand if the supplier with whom he or she
was interested in making a particular agreement was
trusted, and even here Watson did a whole first part of
information retrieval through sites, newspaper articles,
specialized articles, specialized sites that collect precisely
this information." (Interview with IBM AI Cognitive &
Analytics Consultant)
With IBM Watson "I have the opportunity to access
various databases with various points of view and artificial
intelligence allows me to have an eye on all this
information and then to make the best decision in a faster
time" (Interview with IBM Information Technology
Architect AI IBM Watson – AI IBM Watson Dev Squad
Team)
"So, we use [IBM Watson] systems to make more time
decisions on our work on a daily basis" (Interview with
IBM Europe Automation Practice & Delivery Leader – AI
SME)
IBM Watson "provides much more information than
before, makes, even the decisions of the professional more
facilitated by more information, then the decisions can be
made by the professional, but enabled by more
information" (Interview with IBM Information
Technology Architect – AI IBM Watson Dev Squad
Team)
Artificial Intelligence helps the
human solve complex problems
"And we think that the adoption of artificial intelligence is
the tool with which this world can try to address its main
problems such as, in medicine"… "Because when I do a
project I make it available to IBM for information
purposes, so let's say, and all the others do the same thing,
so when you have requests, we have cognitive engines in
which we formulate requests to have, say, a support of
information, experience, project, business driver, of
customer problems" (Interview with IBM Client Executive
AI SME)
"We can assist Wind customers through Watson in their
dialogue, in receiving automatic services on some of their
requests, we are in production and we receive thousands
of calls. I think it is perhaps the first call center in the
world that uses Watson, artificial intelligence, to help its
customers, not in chat, the constituents speak, they speak
normally and receive their answer." (Interview with IBM
Client Executive AI SME)
"When people need to have clarifications from Wind call
the call center, Watson answers the call with two engines,
the first is the predictive, so when the user calls depending
on, all the information that Wind has at the customer's
disposal and potential problems, maybe he has seen a
higher bill, it can already predict the reason for the call,
and then this is the first engine. The second instead is that
of natural language understanding" (Interview with IBM
AI Cognitive Delivery Manager)
IBM Watson manages "all the knowledge in terms of
internal knowledge of each company, so all the
experience, for example the experience of a repair
121
technician, on what are then the options or best practices
to implement to repair a certain problem, to redress a
certain situation" (Interview with IBM Information
Technology Architect – AI Dev Squad Team)
Artificial Intelligence can find
valuable insights from texts and
extract valuable concepts
"Both with machine learning techniques and so to go and
search text insights but also those that deal with tone, the
sentiment, and then thanks to this whole series of
information you can extract the concept at 360 degrees"
(Interview with IBM AI Cognitive Delivery Manager)
"But instead we go to recognize some insights that the
same human being accomplishes but that then struggles to
put together in correlation between thousands and
thousands of entities and thousands and thousands of
records of data" (Interview with IBM Senior Managing
Consultant & Research Scientist IBM Watson AI &
Advanced Analytics)
"But on the other hand no one goes to put their hands
directly into the neural network, there is an interface of
level understandable to the human being of business that
knows the dynamics of behavior of the customer and
easily instructs, but above all does the training because
you do some testing and within a few weeks you are able
to reproduce within a platform of artificial intelligence the
behavior and expectations that your customer has, because
you are able to build easily the model of interaction with
your customer" (Interview with IBM Client Executive AI
SME)
"To solve pressing business challenges AI enables HR
organizations to deliver new insights and services at scale
without ballooning headcount or cost. Persistent
challenges, like having the people resources to deliver on
the business strategy and allocating financial resources
accordingly, can be addressed through the thoughtful
application of AI solutions." (IBM Documentation:
Guenole and Feinzig (2018))
"One of the most significant benefits for interviewed and
surveyed customers (using IBM Watson) is the ability to
efficiently generate and communicate important insights
to business decision makers." (IBM Documentation:
Forrester, (2019))
Artificial Intelligence is easy to
use and to learn.
"It takes about five minutes to learn [how to use IBM
Watson], so it is not complex at all" (Interview with IBM
Europe Automation Practice & Delivery Leader – AI SME)
IBM Watson's simplicity of use "depends on the type of
application, some can be used even if you do not have
specific knowledge and then it is enough, others instead
require the technical knowledge" (Interview with IBM
Senior Watson AI Consultant)
The tools themselves to configuring use are generally very
straight forward, and easy to use. It’s a question of getting
comfortable with them and the greater challenge is giving
accurate and effective data with which to train them"
(Interview with IBM AI IBM Watson Explorer Architect -
IBM Analytics Europe)
122
"IBM Watson is very easy to use and it is easy to learn it"
(Interview with IBM Project Manager Application
Automation)
Artificial Intelligence is a real aid
to the human and improves her
quality of life
"The skills of the computer that could say, assist, help
people to improve their lifestyles" (Interview with IBM
Client Executive AI SME)
"Artificial intelligence solutions […] can help medical staff
or humanitarian associations to better understand the needs
of people who may be refugees or people in need of help,
perhaps in different parts of the world" (Interview with IBM
Information Technology Architect – AI IBM Watson Dev
Squad Team)
"All the things we have done in the medical field, is the idea
that it is a tool that helps people, that is, our idea that can, in
some way, help to improve the world" (Interview with IBM
Client Executive AI SME)
OUTCOMES – BUSINESS PERSPECTIVES
Artificial Intelligence on the
Cloud adds new business
opportunities
"Both the computing capabilities of the hardware on the one
hand, and the possibility of sharing them on the network
through the Internet and then the cloud itself, and also the
richness of statistical models and artificial intelligence that
IBM develops for each individual case of application, are
combined" (Interview with IBM Senior Managing
Consultant & Research Scientist IBM Watson AI &
Advanced Analytics)
"IBM is moving towards open cloud and making sure that
these technologies are available to the general public as
well, so they can come up with their own usage of these
Watson technologies, sharing our knowledge so that, you
know, the manager can make more use of this knowledge
and create more content" (Interview with IBM Europe
Automation Practice & Delivery Leader – AI SME)
Artificial Intelligence facilitates
the generation of new ideas
"A government agency, you can start imagining but
particularly where the kind of recent stuff that we have been
through here also how they could start using technologies
like this so very promising areas where I see a massive shift
and a mass movement towards adoption of a new
technology (AI and IBM Watson) that will change the way
we think, act, and learn from." (Public interview with Former General Manager, IBM Watson Solutions, Manoj Saxena)
"Our platform has all this payment policy basically, all
those who want to try to use the tools have the possibility to
use them for free up to a certain level of use, but they can
test exactly if their idea put into a startup in the case of a
company that wants to innovate, can count on value"
(Interview with IBM Client Executive AI SME)
Artificial Intelligence systems can
increase revenues and save costs
"Major data science projects are more effective, generating
$2.5 million in incremental revenue or cost savings per
project. With improved access to data and modeling tools,
data scientists can drive more value on major projects. With
an average operating margin of 10%, this equates to an
incremental $750,000 in operating margin per project."
(IBM Documentation: Forrester, (2019))
123
"Woodside are realizing 10 million AUD savings in
employee costs because of faster access to and more
intuitive analysis of engineering records. The geoscience
team is realizing a 75% reduction in time spent by the team
reading and searching through data sources" (Banerjee,
(2019))
"To use HR budgets as efficiently as possible AI can enable
HR to become more efficient with its funding. HR spend
can shift to higher value and more complex problem
solving, without reducing levels of service for workers who
have more routine HR queries. HR savings made in this
way can be reinvested in further AI deployment, increasing
HR’s ability to solve business challenges, continuously
develop strategic skills, create positive work experiences,
and provide outstanding decision support for employee"
(IBM Documentation: Guenole and Feinzig (2018))
Artificial Intelligence can
improve HR processes for
information analysis, helping and
speeding up personnel
management systems.
"Even at the company level all processes, for example HR
processes, are all linked to the cognitive part, whatever it is,
the performance of a person with the analysis of all, maybe
the feedback he or she received, the whole part of the
knowledge that has acquired and a whole process that
basically are perhaps certifications, data, even unstructured
data, feedback data, are analyzed and when they give the
alert to managers to say look, for a pay raise or for
additional information, so this also helps a lot and speeds up
the HR system" (Interview with IBM AI Cognitive Delivery
Manager)
"Transformation with cognitive comes with obviously a
transformation of functions, individual functions, but then
also of the overall enterprise and that touches everything
from the complete employee and engagement lifecycle from
hiring, selecting, using this technology and then ultimately
then repurposing it for training as well. So, it really means
looking at your enterprise holistically, looking at
transformative power, not just as a speeds and feeds, but
also as a business process and business model, sometimes"
(Public interview with Former Global Leader - Cognitive
Visioning and Strategy - IBM Watson, Bjorn Austraat)
"These are measures that quantify, therefore, not only the
percentage of full time equivalent or FTE, that is,
professional figures that are moved to other dimensions thus freeing up resources for that type of task, but also new
capabilities that Watson's cognitive systems offer are
proposed" (Interview with IBM Senior Managing
Consultant & Research Scientist IBM Watson AI &
Advanced Analytics)
"So that can be any process, that can be HRM process, we’re
trying to figure out who’s performing best based on reports
of their work, that’s based on, that could be employees’
surveys, trying to figure what employees think of their
organization, customers surveys, anywhere where textual
data is at the heart of the process, Watson provides a way of
analyzing that much more effectively" (Interview with IBM
AI IBM Watson Explorer Architect - IBM Analytics Europe)
124
OUTCOMES – KNOWLEDGE PERSPECTIVES
Artificial Intelligence available
on cloud provides information
and knowledge dissemination
"The main advantage [in the use of Watson's cloud system]
of information dissemination is that I can reach to large sets
of people the information, as soon as I see this information,
it is available to all those users" (Interview with IBM
Europe Automation Practice & Delivery Leader – AI SME)
"With technologies like Watson humanity now, if you look
out 50 years 100 years, I believe is going to be a defending
of the knowledge for humanity because now you're not just
capturing the information about people capturing the
knowledge about those people that experiences world
people" ... "We are capturing his knowledge and putting this into a machine so generations from now on can benefit
from his knowledge and insight" (Public interview with
Former General Manager, IBM Watson Solutions, Manoj
Saxena)
"To then arrive now at a semantic search based on concepts,
not only on keywords, which has further facilitated the
exploration of the great knowledge available in IBM, so
therefore a person who perhaps at the beginning was
looking for a topic can, thanks to this technology, get to
other nuances on the subject thanks to the fact that
cognitive technology allows to navigate not only in
documents but also through concepts and thus aggregate
and make more and more fine-grained the research so that
the employee can really reach the value in that particular
type of information" (Interview with IBM Senior Managing
Consultant & Research Scientist IBM Watson AI &
Advanced Analytics)
Artificial Intelligence can
improve knowledge
management processes, better
document organization and
deep analysis
"And all this is implemented in the Watson platform
through the tools that as you mentioned are the Knowledge
Catalog, in order to manage the set of data sources in a
coordinated, aggregated way, being able to also go to
implement the concepts of accessibility to various sources,
and so on" (Interview with IBM Technical Solution
Architect Cloud & AI Cognitive)
IBM Watson allows to reach "new levels of information
[…] by analyzing large data, the large amount of data does
not allow an analysis made by a single person, so it is easy
there to take the information for her knowledge" (Interview
with IBM AI Cognitive & Analytics Consultant)
“IBM Watson allows us to decipher all the non-traditional
inputs that arrive, on the other hand, it allows us to
schematize, classify and make accessible, even from the
automatic flows, all the information that is typically
managed by people" (Interview with IBM Information
Technology Architect – AI IBM Watson Dev Squad Team)
"The management of internal knowledge (Watson) is able
to associate to a certain question that has been asked, the
answers that come from what is my wealth of knowledge,
mine as a company, you added yet another capability,
which is to give sensible answers to questions" (Interview
with IBM Information Technology Architect – AI IBM
Watson Dev Squad Team)
125
Layer two [of IBM Watson] would be that the answer that
you give comes from many sources, right? It would be a
combination of something that is written in a paper, so you
would ask that student to read a particular paper or a text
from a particular doc, something like that. It could be a
picture, seeing videos, so that are multiple type of content
that you will have. (Interview with IBM Europe
Automation Practice & Delivery Leader – AI SME)
Artificial Intelligence can allow
organizations to find
information by analyzing large
amounts of data and select
those related to useful
knowledge
"You have to instruct the computer so that it understands
how to use a, let's say, it seems that they had taken it from
Wikipedia, a Wikipedia to find within such an endless data
base of information the correct answer in a very short time"
(Interview with IBM Client Executive AI)
"And this platform that I use regularly, through a cognitive
engine pulls me out around the world all the experiences
similar to mine or all that I need to build my experience so I
have a worldwide knowledge but I do not have to go look
through all the documents, I am exposed in a kind of
cognitive search engine to only that which is relevant,
extremely relevant, for those that are my needs" (Interview
with IBM Client Executive AI)
"Personal data, in the context of Watson and IBM Cloud,
belongs exclusively to the customer who makes it available
for his statistical models and artificial intelligence. IBM
does not benefit in any way, does not appropriate in any
way the content of customer data and does not even
generalize for later use. This is very important. So data
protection is full" (Interview with IBM Senior Managing
Consultant & Research Scientist IBM Watson AI &
Advanced Analytics)
Artificial intelligence ensures
effective personal data
protection
"IBM does not behave like that, on the contrary, in its
collaboration environment with international, university,
scientific entities, with which to define the ethical values on
which to move and evolve AI, there is privacy in the strict
sense" (Interview with IBM Senior Managing Consultant &
Research Scientist IBM Watson AI & Advanced Analytics)
"Personal data can be controlled completely. Watson looks
at who needs information, then if the person has it in excess
then what is the level of content that the person has, and
what is the time frame for which that information needs to
be provided. So, all of those things can be deployed to
effectively make sure of compliance to all regulatory
bodies." (Interview with IBM Europe Automation Practice
& Delivery Leader – AI SME)
"The protection of personal data varies from project to
project, it is not so much Watson but the type of project you
are dealing with" (Interview with IBM Senior Watson AI
Consultant)
"IBM has a very clear policy of use. The data of customers
or users are never extrapolated from what is the context of
the solution [...] IBM ensures that both the training data and
the data that are then managed by the solution will never be
used for different purposes" (Interview with IBM
Information Technology Architect – AI IBM Watson Dev
Squad Team)
126
APPENDIX D: Coding of Personal Interviews
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Client Executive AI SME
"I work in a team within IBM that oversees from a commercial point of view all the offers that IBM has, among which there is certainly also the cognitive (IBM Watson) that is one of the offers of IBM on which IBM is supporting so much"
AI is a main offer for some companies
Main Strategy: Cognitive/AI AI strategy
IBM Client Executive AI SME
"The two offers on which IBM is supporting are definitely the Cloud as an infrastructure"
Some companies think the future is cloud computing Main Strategy: Cloud Cloud strategy
IBM Client Executive AI SME
"Enabling the company to make a transformation to the digital world"
AI helps companies make the transition to digitalization
AI innovation for digitalization AI advantages
IBM Client Executive AI SME
"The cognitive, as you know we started well in advance of all the others including Google, Microsoft which are now a bit our competitors"
AI initiatives ahead of its competitors
Initiators on the AI market AI strategy
IBM Client Executive AI SME
"On the contrary, we verified over the years that eventually everyone had to follow, and at this time many areas are lagging behind us, because we are I think a couple of years ahead of the others"
Big company is 2 years ahead on cognitive and AI over its competitors
Innovators on the AI market AI strategy
IBM Client Executive AI SME
"The skills of the computer that could say, assist, help people to improve their lifestyles"
AI helps the human to improve of the quality of life
AI improve quality of life AI advantages
IBM Client Executive AI SME
"You have to instruct the computer so that it understands how to use a, let's say, it seems that they had taken it from Wikipedia, a Wikipedia to find within such an endless data base of information the correct answer in a very short time"
AI can be used with individual respect and secure their personal data
AI effectively manages information AI advantages
IBM Client Executive AI SME
"Our strategy is always to be able to support the human being in his decisions"
AI helps the humans to make better decisions
AI improves human decisions AI advantages
IBM Client Executive AI SME
"All the things we have done in the medical field, is the idea that it is a tool that helps people, that is, our idea that can, in some way, help to improve the world"
AI helps humans to improve quality of life
AI helps to improve the world AI advantages
IBM Client Executive AI SME
"And we think that the adoption of artificial intelligence is the tool with which this world can try to address its main problems such as, in medicine"
AI helps the human to solve problems
AI for problem solving AI advantages
IBM Client Executive AI SME
"Our idea is basically to help doctors to have the best possible information available to make a diagnosis"
AI allows to have the best information available
AI manages information effectively AI advantages
IBM Client Executive AI SME
"To create a platform that is accessible to everyone that is IBM Cloud, within this platform one has at their disposal all the tools with which to do artificial intelligence"
Some companies think the future is cloud computing Main Strategy: Cloud Cloud strategy
IBM Client Executive AI SME
"A platform that is accessible to everyone that is IBM Cloud, within this platform one has at their disposal all the tools with which to do artificial intelligence"
Big companies invest a lot of money in AI as main strategy AI as main strategy AI strategy
IBM Client Executive AI SME
"Our platform has all this payment policy basically, all those who want to try to use the tools have the possibility to use them for free up to a certain level of use, but they can test exactly if their idea put into a startup in the case of a company that wants to innovate, can count on value"
AI experimentation on Cloud available to its customers
AI experimentation on Cloud
AI - on Cloud advantage
IBM Client Executive AI SME
"The current one you can see, as I told you, in the Watson platform that is now available in the Cloud, of course what IBM is preparing for the next few years is right now in our laboratories that are around the world"
Strong investments in R&D in laboratories worldwide
AI investments on R&D AI strategy
IBM Client Executive AI SME
"The machine learning algorithms that are the basis of everything, are open source algorithms, that everyone has at their disposal"
AI algorithms are open source, easy and available to all
Open Source AI algorithms AI advantages
127
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Client Executive AI SME
"Then of course there is all the data and what you want to do with them, so what is it that differentiates IBM from another company, what it has built on these algorithms, right?"
AI algorithm turns companies into a business advantage
AI algorithms used to create value for companies AI advantages
IBM Client Executive AI SME
"So basically we are able on the one hand with our people to understand exactly how the customer behaves in the various stages of his customer journey and our platform that is based on neural networks but has built on it a whole model of use and simplification of the complexity of the network"
AI helps the human to solve problems
AI applied to problem solving AI advantages
IBM Client Executive AI SME
"But on the other hand no one goes to put their hands directly into the neural network, there is an interface of level understandable to the human being of business that knows the dynamics of behavior of the customer and easily instructs, but above all does the training because you do some testing and within a few weeks you are able to reproduce within a platform of artificial intelligence the behavior and expectations that your customer has, because you are able to build easily the model of interaction with your customer"
AI allows to find the insights of the texts and to extract the concept
AI applied to business AI advantages
IBM Client Executive AI SME
"Our way of sharing our experience of projects around the world is certainly one of the elements of differentiation of IBM"
Big company leverages on experience sharing around the world
Dissemination of knowledge throughout the world KM
IBM Client Executive AI SME
"Because when I do a project I make it available to IBM for information purposes, so let's say, and all the others do the same thing, so when you have requests, we have cognitive engines in which we formulate requests to have, say, a support of information, experience, project, business driver, of customer problems"
AI helps the human to solve problems
AI for problem solving AI advantages
IBM Client Executive AI SME
"And this platform that I use regularly, through a cognitive engine pulls me out around the world all the experiences similar to mine or all that I need to build my experience so I have a worldwide knowledge but I do not have to go look through all the documents, I am exposed in a kind of cognitive search engine to only that which is relevant, extremely relevant, for those that are my needs"
AI can allow to find information by analyzing large amounts of data and take those related to the knowledge
Use of AI cognitive engines for KM AI applied to KM
IBM Client Executive AI SME
"Our strategy in this field is that of the hybrid Cloud, we think that it is the best way to make this transition to this digital world that obviously embraces cognitive"
Big company's strategy for the future is Big company Cloud and Hybrid Cloud
Big company Strategy: Hybrid Cloud AI - on Cloud
IBM Client Executive AI SME
"The idea of IBM is to be as close as possible to its customers say in areas where there is definitely a relevance"
AI on cloud is designed to support the customers needs
Attention to the needs of customers in the Cloud and AI
AI - on Cloud advantage
IBM Client Executive AI SME
"To use its own cognitive search engine, so you have a sort of interface in which you have cognitive access to all the information that is on our intranet"
Big company uses a cognitive engine for its research on the corporate intranet
Corporate intranet uses AI AI applied to KM
IBM Client Executive AI SME
"So the complexity that you can imagine with IBM that has so much internal data is absolutely simplified because you have a single interface in front of you and then it is the cognitive engine that takes care of finding in the various positions of IBM the correct material of the intranet for you"
Big company uses a cognitive engine to find the correct information within its corporate network
Corporate intranet uses AI AI applied to KM
128
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Client Executive AI SME
"The second thing we have, that we use a lot, is the chat, because you have the possibility of, every time you access the intranet to use a chat that is composed of both recurring questions that are automatically resolved by a cognitive system that in our case is the Watson Assistant, which is one of the most important pieces of our platform, which is able to answer many questions"
Big company uses a chat (cognitive engine) to answer questions within its corporate network
Corporate intranet uses AI AI applied to KM
IBM Client Executive AI SME
"We can assist Wind customers through Watson in their dialogue, in receiving automatic services on some of their requests, we are in production and we receive thousands of calls. I think it is perhaps the first call center in the world that uses Watson, artificial intelligence, to help its customers, not in chat, the constituents speak, they speak normally and receive their answer."
AI helps the human to solve problems
AI for problem solving AI advantages
IBM Client Executive AI SME
"The ability of our people to know how to work with the customer, we believe that it is something that is very differentiating, that only IBM has"
Use of AI is related to the ability to understand customers and their needs
AI is more successful if it is focused on the customer and his needs AI advantages
IBM Client Executive AI SME
"There is a European directive on how artificial intelligence should be, and we say Europe, in my opinion that is always very attentive to the rights of the citizen, in the last GDPR, which is certainly something that helps the citizen because it protects their data and therefore if you want to build a service also based on artificial intelligence, you have to be respectful of people like we always do in our projects, but this, let's say, role that artificial intelligence has to play as an aid in the life of the citizen is the most important and relevant thing of all and it is the way in which IBM is presenting itself on the market"
AI can be used with individual respect and secure their personal data
Respect for ethical values: AI must ensure personal data protection AI ethics
IBM AI Cognitive Delivery Manager
"IBM, in order to respond to different market needs actually takes advantage of the fact that it can realize any type of data at 360 degrees, not only focusing on structured data, but also analyzing non structured data"
AI can allow the management of structured and unstructured data
Cognitive: Data management of all types IBM Cognitive
IBM AI Cognitive Delivery Manager
"Both with machine learning techniques and so to go and search text insights but also those that deal with tone, the sentiment, and then thanks to this whole series of information you can extract the concept at 360 degrees"
AI allows to find the insights of the texts and to extract the concept
Cognitive: Data processing with concept extraction IBM Cognitive
IBM AI Cognitive Delivery Manager
"And how it is organized, in reality and, fundamentally is based on, a whole series of, there’s from small to large businesses so it doesn’t just operate on the national level but most importantly on the international level and then on the basis of the demand tries to adapt and also understand what is the most appropriate Watson technology to the current needs for both society and the market"
Big company uses Watson to tailor it to specific customer needs
AI is more successful if it is focused on the customer and his needs AI advantages
IBM AI Cognitive Delivery Manager
"And then there is all the visual part, of the visual recognition, which allows instead, machine learning related to the images, therefore the analysis of the images, so to be able to classify images"
AI manages the analysis and classification of images
Big company Watson: Images management and classification AI advantages
IBM AI Cognitive Delivery Manager
"And then there is the part of classification, natural language classifier, for example, which allows instead to go to classify documents, so, to know what they are, what kind of documents they are and what they tell about and then give a classification, a classification of the documents"
Big company manages the analysis and classification of documents
Big company Watson: Document management and classification AI advantages
129
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM AI Cognitive Delivery Manager
"And then there is the whole part related to the vocal, text-to-speech, speech-to-text, always related to Watson services that allows instead to analyze the audio and then to transcribe it, or vice versa the written part to make it into audio"
AI manages the audio component and content analysis
Big company Watson and audio management AI advantages
IBM AI Cognitive Delivery Manager
"Yes, and also Watson Assistant, and these are the two systems that make it possible to recognize the user's intention and thus succeed in giving an in-line answer"
AI allows to recognize the tone of the conversation and then give correct answer
Big company Watson allows to give correct answers AI advantages
IBM AI Cognitive Delivery Manager
"Even at the company level all processes, for example HR processes, are all linked to the cognitive part, whatever it is, the performance of a person with the analysis of all, maybe the the feedback he or she received, the whole part of the knowledge that has acquired and a whole process that basically are perhaps certifications, data, even unstructured data, feedback data, are analyzed and when they give the alert to managers to say look, for a pay raise or for additional information, so this also helps a lot and speeds up the HR system"
AI improves HR processes for information analysis, helping and speeding up personnel management systems.
Big company Watson applied to HR processes
AI in HR processes
IBM AI Cognitive Delivery Manager
"Watson is Cloud, so it's just IBM Cloud, so there are basically all Watson's Cloud"
AI is developed and available on the Cloud platform
Big company Strategy: AI on Cloud AI - on Cloud
IBM AI Cognitive Delivery Manager
"And obviously those that are currently exploited to this day are those in the cloud, and for those in multicloud, all Watson services even in an environment that is not mainly IBM Cloud but also just Amazon's Cloud for example, you can go and integrate the Watson systems"
AI is developed and available on the Cloud platform
Big company Strategy: AI on Cloud AI - on Cloud
IBM AI Cognitive Delivery Manager
"For the procurement part of Enel, so it is a kind of cognitive dashboard that allows you to analyze the reputational part, the documentary part of its suppliers, so when Enel needs to know if a supplier is in line with what are its internal standards uses this dashboard"
Use of AI for complex document management
AI applied to document management AI advantages
IBM AI Cognitive Delivery Manager
"So when people need to have clarifications from Wind call the call center, Watson answers the call with two engines, the first is the predictive, so when the user calls depending on, all the information that Wind has at the customer's disposal and potential problems, maybe he has seen a higher bill, it can already predict the reason for the call, and then this is the first engine. The second instead is that of natural language understanding, so if the reason for the call is among those that are the information of interest of the, let's say to Watson's knowledge, it will be Watson directly to answer, otherwise Watson turns the questions to the team of competence, and then the team of competence will then answer the call instead, and this is a project that let's say started a year ago and continues with its developments"
AI helps the human to solve problems
AI for problem solving AI advantages
IBM Technical Solution Architect Cloud & AI Cognitive
"Watson is a set of technologies ranging from content analytics, standard, to a part of knowledge representation, with knowledge graphs, with semantic technologies and with the opportunity to represent concepts in an abstract way at different levels depending on the granularity of knowledge that is intended to be formalized"
AI allows to represent concepts at different levels of granularity
Big company Watson applied to knowledge management AI and KM
130
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Technical Solution Architect Cloud & AI Cognitive
"The experience I have had has been in the healthcare sector, but also in the insurance sector, and now the understanding of new domains of knowledge is always important, let's think of the insurance companies that have to build new financial and insurance products on areas that were not in their previous experience and therefore have to move in an exploratory way and to do so they have to take advantage of many documents that are often with unstructured data content, such as texts and so on, and therefore to represent these concepts, first identify them and then represent them"
AI helps humans to make better decisions
AI improves human decisions AI advantages
IBM Technical Solution Architect Cloud & AI Cognitive
"Both the computing capabilities of the hardware on the one hand, and the possibility of sharing them on the network through the Internet and then the cloud itself, and also the richness of statistical models and artificial intelligence that IBM develops for each individual case of application, are combined"
Companies use the cloud computing to main strategy and drive the future vision in this direction
Main Strategy is Cloud Computing AI - on Cloud
IBM Technical Solution Architect Cloud & AI Cognitive
"The cloud technology that we have in IBM Watson, in this case are multiple technologies, allow you to process multiple types of data, those structured, present in databases, with clear classification, and those unstructured that can be text, audio and video. So it goes to process and cover a great heterogeneity of data. This allows therefore to valorize a lot the informative asset of the companies, even the most silent one, that is the asset that until a few years ago could not be handled with IT tools"
AI can process structured and unstructured data, making the most of the company's information assets
AI system acts on structured and unstructured data by broadening the information assets AI and KM
IBM Technical Solution Architect Cloud & AI Cognitive
"But instead we go to recognize some insights that the same human being accomplishes but that then struggles to put together in correlation between thousands and thousands of entities and thousands and thousands of records of data"
AI allows to find the insights of the texts and to extract the concept
Big company Watson finds non apparent information AI and KM
IBM Technical Solution Architect Cloud & AI Cognitive
"The data you use must be representative of the phenomenon that you want to describe and represent. Otherwise, if the data are not representative, there is a risk of building a poor and unrealistic statistical model. In the same way the artificial intelligence feeds on data, therefore the artificial intelligence is a series of algorithms that are born on various statistics, therefore that elaborate statistically the data, and that develop on data whose quality must be also weighted, as for the statistic but also for a human learning, if you provide an information, a set of data that is not representative, also the model will not be so. This is a risk that statisticians and those who do machine learning should consider"
Incorrect or unrepresentative data may result in unrealistic models
AI may process unrepresentative data but this is the risk of any statistical research AI risks
IBM Technical Solution Architect Cloud & AI Cognitive
"Personal data, in the context of Watson and IBM Cloud, belongs exclusively to the customer who makes it available for his statistical models and artificial intelligence. IBM does not benefit in any way, does not appropriate in any way the content of customer data and does not even generalize for later use. This is very important. So data protection is full"
AI can be used with individual respect and secure their personal data
Full data protection by AI system on the Cloud AI - on Cloud
131
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Technical Solution Architect Cloud & AI Cognitive
"IBM does not behave like that, on the contrary, in its collaboration environment with international, university, scientific entities, with which to define the ethical values on which to move and evolve AI, there is privacy in the strict sense"
AI can be used with individual respect and secure their personal data
Ethical aspects of the use of AI AI ethics
IBM Technical Solution Architect Cloud & AI Cognitive
"There are several modules that make up Watson technology as you have already had the opportunity to explore. Depending on the type of instrument we have a different approach in terms of ease and also immediacy. Surely IBM takes great care of the user friendliness, that is the ease of the interface and also of the approach to data" AI is easy to use AI is easy AI advantages
IBM Technical Solution Architect Cloud & AI Cognitive
"They are very user friendly and that with a simple WYSIWYG training of a few moments the person can be already operational, such as for example the one concerning knowledge management and content analytics, or Watson Knowledge Studio, which allows to extract knowledge from texts and where the subject manages to qualify which conceptual category some texts belong to and therefore the labelling, or tagging of those documents is done with great ease and then the machine automatically manages to generalize what the human being has done and tries to continue this categorization of the text and then asks the human being to validate it"
AI in KM helps the human being to organize texts in a simple way
Big company Watson makes knowledge organization (KM) easier AI and KM
IBM Technical Solution Architect Cloud & AI Cognitive
"If we want to focus on the area of content analytics and knowledge management, the technology that also animates our intranet in IBM started from a research for initial keywords on all the documentation that IBM offered to our employees"
In the past, Big company managed knowledge using keywords to find information
KM in the past, use of keywords KM in the Past
IBM Technical Solution Architect Cloud & AI Cognitive
"To then arrive now at a semantic search based on concepts, not only on keywords, which has further facilitated the exploration of the great knowledge available in IBM, so therefore a person who perhaps at the beginning was looking for a topic can, thanks to this technology, get to other nuances on the subject thanks to the fact that cognitive technology allows to navigate not only in documents but also through concepts and thus aggregate and make more and more fine-grained the research so that the employee can really reach the value in that particular type of information"
AI improves KM processes through better document organization and analysis
KM today use sematic research through cognitive technologies AI and KM
IBM Technical Solution Architect Cloud & AI Cognitive
"These are measures that quantify, therefore, not only the percentage of full time equivalent or FTE,
that is, professional figures that are moved to other dimensions thus freeing up resources for that type of task, but also new capabilities that Watson's cognitive systems offer are proposed"
AI improves HR processes
for information analysis, helping and speeding up personnel management systems.
Optimal use of knowledge and expertise of human resources AI and HR
IBM Technical Solution Architect Cloud & AI Cognitive
"Because of the intrinsic capabilities that they can express, correlation analysis between multiple sources of data, which were not previously seen, or even their conceptualization, it is possible that a company discovers thanks to those cognitive technologies to have so much wealth in the data to be able to almost open new forms of business or launch new start-ups internally"
AI can discover correlations and conceptualizations of data that allow to open up to new forms of business
Data management through AI system opens up business opportunities AI and KM
132
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Technical Solution Architect Cloud & AI Cognitive
"In collecting a greater extension of data, in encoding its relevance to the specific decision-making domain and in allowing also a human understanding, therefore a summarization, a more transparent visualization of data to the human user, an empowerment is made, that is an enhancement of the capacities of the interlocutor or decision-maker and therefore a greater decision-making capacity is allowed because it is based on data that really people have at hand"
AI helps humans to make better decisions
AI improves human decisions AI advantages
IBM Technical Solution Architect Cloud & AI Cognitive
"In the face of common questions, cognitive technologies are proposed as tools to reach these common answers to humanity that populates all different geographies and latitudes, and then Watson technologies present themselves as a point of unification and intelligence with respect to questions that are common to the whole world"
AI unifies common questions around the world and enables common answers to be reached
Big company Watson applied globally and internationally
AI and internationalization
IBM Technical Solution
Architect Cloud & AI Cognitive
"IBM is characterized by being a company founded on two main principles of information technology, one is
the principle of the cloud and the other is the principle of artificial intelligence"
AI and cloud are the
future of big company strategy
Big company
Strategy: AI and Cloud AI strategy
IBM Technical Solution Architect Cloud & AI Cognitive
"The competence centers are those centers that are used to bring this innovation [new AI technologies] into the products and solutions that are offered and sold to customers. So they are more, let's say that point of connection between what is researched, tested and created in the research laboratories"
AI as an opportunity to innovate and benefit humanity
Big company Watson allows to innovate
AI and innovation
IBM Technical Solution Architect Cloud & AI Cognitive
About AI and IBM Watson "Well certainly artificial intelligence is an element on which IBM has invested for several years now and will continue to invest. Alongside these there will also be issues such as cloud computing, issues related to Blockchain, and these are then linked in the emergence, so to speak, in the concept of digitalization of the enterprise of companies."
AI helps companies make the transition to digitalization
AI innovation for digitalization AI advantages
IBM Technical Solution Architect Cloud & AI Cognitive
In IBM "we have an offer of artificial intelligence at 360 degrees. What does that mean? It means that we can range from what is defined as a fully customizable artificial intelligence"
AI solutions are fully customizable
Big company Watson as fully customizable AI AI advantages
IBM Technical Solution Architect Cloud & AI Cognitive
"So [IBM Watson is] a set of tools that allow to create your own deep learning networks, in a totally custom or free mode, or maybe using open source frameworks
AI allows to create learning networks using free open source
Big company Watson helps to create learning networks AI advantages
IBM Technical Solution Architect Cloud & AI Cognitive
"And all this is implemented in the Watson platform through the tools that as you mentioned are the Knowledge Catalog, in order to manage the set of data sources in a coordinated, aggregated way, being able to also go to implement the concepts of accessibility to various sources, and so on"
AI improves KM processes through better document organization and analysis
Big company Watson Knowledge Catalog for optimal KM AI and KM
IBM Technical Solution Architect Cloud & AI Cognitive
"The machine learning which is instead the run time, which allows us to run in the form of an API what was built by the data scientist, therefore the algorithm created by our data scientist"
Automatic learning is made available in the form of programming interfaces as advanced AI algorithms
Big company Watson uses advanced AI algorithms for automatic learning AI advantages
IBM Technical Solution Architect Cloud & AI Cognitive
"There is a whole series of offers and services, you mentioned one, Watson Discovery, which are basically part of what is called pre-built artificial intelligence, pre-built, pre-packaged, that is built in a laboratory but then specialized on customer data, which are basically a set of services that allows us to make a quick startup of our solution"
Some AI products allow to customer to make better solution
Big company Watson Discovery offers pre-built forms of AI with rapid solution start-up AI advantages
133
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Technical Solution Architect Cloud & AI Cognitive
"Through IBM Watson, Watson Assistant rather or Discovery or Natural Language Understanding, they allow us to easily and immediately, through artificial intelligence, to integrate it into our complete business solution. So we have basically all the possibility, the flexibility of being able to build either from scratch or taking advantage of what has already been done with our artificial intelligence platform."
AI solutions allow to build a solution from scratch or leverage what it has already been done with AI platforms
Big company Watson solutions allow to build various forms of business solutions AI advantages
IBM AI Cognitive & Analytics Consultant
"Watson [is] able to find this information in a document of 20, 30 pages, in this way it was easy for the end user, in this specific case through a dashboard, so that not having to read all the documents to understand which was the document that interested in the specific, what were the supplies of a particular document"
AI facilitates the identification of information within complex documents
Big company Watson improves KM of complex documents AI and KM
IBM AI Cognitive & Analytics Consultant
"The client who needed to understand [...] whether or not to trust their suppliers, so if there were managers who had a pending suit or if companies had been involved in trials [...], so understand if the supplier with whom he or she was interested in making a particular agreement was trusted, and even here Watson did a whole first part of information retrieval through sites, newspaper articles, specialized articles, specialized sites that collect precisely this information."
AI helps humans to make better decisions
AI improves human decision AI advantages
IBM AI Cognitive & Analytics Consultant
To implement IBM Watson "It's not necessary to have to create something that runs on the customer's hardware systems, so there's no need to use their systems but the fact that it's cloud can then be reached by, that is, it's developed directly in the cloud so it can be reached from anywhere in the world you want"
AI solutions developed in the Cloud can be easily reached via Internet and do not have to adapt to the client's systems
Big company Watson solutions on cloud easily accessible AI - on Cloud
IBM AI Cognitive & Analytics Consultant
"Living all Watson services, living in the same cloud environment, they are also easily integrated with each other because many times more services are used, for example what I told you before, which analyzes the documents, takes out the insight is used very often along with the one to do the chatbots, so then the insights are returned in the form of chat. In this sense, it is also quite easy to integrate them."
AI as a centralized platform of integrated services to manage all business processes
Big company Watson services in cloud are easily integrated AI - on Cloud
IBM AI Cognitive & Analytics Consultant
IBM Watson allows to reach "new levels of information […] by analyzing large data, the large amount of data does not allow an analysis made by a single person, so it is easy there to take the information for her knowledge"
AI improves KM processes through better document organization and analysis
Big company Watson improves KM by analyzing large amounts of data AI and KM
IBM AI Cognitive & Analytics Consultant
"That is why it is a very delicate phase of a cognitive project, the training phase. First, let's say that a perimeter of knowledge is created within which Watson will be trained, and this already allows, let me say, to limit possible external influences. Secondly, a job is done with those who are experts in the field to identify how to create these cognitive models"
In the training phase AI limits external influences and uses experts to create cognitive models
Big company Watson makes the training process efficient AI advantages
IBM AI Cognitive & Analytics Consultant
"The security aspect of the cloud in the case of IBM is something that is constantly monitored, is one of the most important aspects of course"
AI can be used with individual respect and secure their personal data
AI in Cloud and personal data management AI - on Cloud
134
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM AI Cognitive & Analytics Consultant
"On the part of the end user of a Watson service it does not require any competence, because in the end as I told you, if it is a chatbot, it is like interacting with a person [...] there are different aspects depending on whether they are the holders of knowledge then they want to be the first person to feed the cognitive knowledge of the Watson system that was implemented then there is a minimum of complications in addition but it is still a matter of using services that have already been developed and then it is the configuration is quite simple, there is no need for great skills"
AI does not require specific skills for the end user and non-complex skills for those who "feed" the cognitive systems
Big company Watson use and implementation skills AI advantages
IBM AI Cognitive & Analytics Consultant
"With our chatbot we made it so that a whole series of problems, let me say, the obviously most common ones, the difficult ones actually need to have the intervention of a technician, but all those that were easily solved by the help desk by connecting have moved, have moved the solution from the user who at this point asks how it is done, the chatbot is able to give him a simple guide, and the user is able to solve it by herself" the desk operator who is now able to focus only on those more serious issues and therefore also provide a better service, more punctual to that user who really needs it"
AI chatbot answers queries and moves the most complex requests to the help desk
Big company Watson improves the help desk's work AI advantages
IBM AI Cognitive & Analytics Consultant
"The desk operator […] is now able to focus only on those more serious issues and therefore also provide a better service, more punctual to that user who really needs it"
AI chatbot improves the help desk's work and customer service
Big company Watson improves the help desk's work and customer service AI advantages
IBM AI Cognitive & Analytics Consultant
"The simple fact that you don't have to read several documents by yourself means that, in the meantime, it's time saving, I have to read 10 documents to find insights, obviously it takes a while. In this way, however, the fact that the documents have been pre-processed by Watson allows the user to focus only on what is important data, or rather to have more important data."
AI improves KM by enabling the user to focus on the most important information
Big company Watson improves KM of complex documents AI and KM
IBM AI Cognitive & Analytics Consultant
"Thanks to the use of IBM Watson it is possible [...] to feed, so to speak, the system a whole series of documents and get that information so be able to have a first processing and then of course you have to see the human component to go and analyze in detail but let's say there is a component of considerable time gain."
AI allows to manage information as first processing and does not eliminate the human component that must analyze it in detail.
Big company Watson allows time-saving in KM AI and KM
IBM AI Cognitive & Analytics Consultant
"Having developed a certain project [using IBM Watson] in an area of the world allows us to have a background, a starting point, that is, not to create something new from nothing"
Gaining experience in AI projects means creating a background to use anywhere in the world
Big company Watson applied globally and internationally
AI and internationalization
IBM AI Cognitive
& Analytics Consultant
"A[n] [IBM Watson] project, however similar [at the global level], brings with it variations from what is the information that is being used [...], a whole series of
knowledge that, however, must be revised taking into account the situation"
AI projects that have been developed worldwide
need to be contextualized at the local level
Big company Watson
applied globally and internationally
AI and
internationalization
IBM Information Technology Architect AI IBM Watson
"Artificial intelligence uses unconventional algorithms based on technologies that have been well known for years but that require considerable computational power, we are talking about technologies such as neural networks, machine learning and so on. The advent of the cloud, which is precisely the ability to delegate the computation of processes not to the laptop in front of you on your desk but to much more complex systems that are geographically distributed and optimized"
AI is available on cloud to optimize the availability, the diffusion and the speed
AI requires large processing capabilities and the cloud addresses this need AI and Cloud
135
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Information Technology Architect AI IBM Watson
"Today even the individual citizen, registering on the IBM cloud, can develop their own artificial intelligence solution, simple, as you like, but fully functional, at no cost"
AI on the Cloud allows individuals to develop simple AI solutions
AI on the Cloud also allows access to individuals AI - on Cloud
IBM Information Technology Architect AI IBM Watson
"Starting from the solutions that have been developed in the medical field that were in part the first developments of artificial intelligence applied to current business areas, so systems to help doctors to make diagnoses, to find evidence and to cross-reference patients' data, we then moved on to solutions in the financial and banking areas, and then solutions for call centers, for telephone companies, up to now where it can be said that almost all areas of business can take advantage of the usefulness of artificial intelligence precisely because compared to the past it is much easier to build solutions"
AI initially applied in the health sector and then in the financial sector, now it is open to almost all business areas
Big company Watson applicable to almost all business areas AI advantages
IBM Information Technology Architect AI IBM Watson
"The advantage [in the use of IBM Watson] is certainly to be able to include in the technologies used but especially in the business processes used, a whole series of data that were previously present but could not be used, think for example of all the unstructured data"
AI can extend KM to include all unstructured data that could not be used in the past
AI in KM handles structured and unstructured data AI and KM
IBM Information Technology Architect AI IBM Watson
IBM Watson manages "all the knowledge in terms of internal knowledge of each company, so all the experience, for example the experience of a repair technician, on what are then the options or best practices to implement to repair a certain problem, to redress a certain situation"
AI helps the human to solve problems
AI for problem solving AI advantages
IBM Information Technology Architect AI IBM Watson
"On the one hand, [IBM Watson] allows us to decipher all the non-traditional inputs that arrive, on the other hand, it allows us to schematize, classify and make accessible, even from the automatic flows, all the information that is typically managed by people"
AI improves KM processes through better document organization and analysis
Big company Watson handles information from different flows AI and KM
IBM Information Technology Architect AI IBM
Watson
"From what I have said one could imagine that the human being is completely excluded. Obviously, this is not the case, because these systems are not, let's say, an alternative to human intelligence, but simply serve to help people, hence humans, human operators, to
do their work more effectively"
AI in KM does not eliminate human intervention but helps the human being to work
more effectively
Big company Watson in KM helps to work
more effectively AI and KM
IBM Information Technology Architect AI IBM Watson
"If I ask the question to an operator about the insurance policy, while in the past the operator had to go and get the contract, read it, interpret what was written, now he can turn my question immediately to an automatic system [IBM Watson] which, if it does not already give her the answer nice and ready, it highlights all those parts of the documentation where there are the answers, and this saves a lot of time"
AI applied to daily practices does not replace the human being but helps her to get the answers she needs and save time
Big company Watson in KM provides the answers needed and saves time AI and KM
IBM Information Technology Architect AI IBM Watson
With IBM Watson "I have the opportunity to access various databases with various points of view and artificial intelligence allows me to have an eye on all this information and then to make the best decision in a faster time"
AI helps humans to make better decisions
AI improves human decisions AI advantages
IBM Information Technology Architect AI IBM Watson
"IBM, on its Watson platform that we can imagine as a fairly centralized platform with precise strategies
implemented centrally, has implemented a whole series of services that are divided by capabilities, that is by focused capabilities, and that can then be put together to build solutions that expose precisely intelligent capabilities"
AI as a centralized platform of integrated services to manage all business processes
Big company Watson as a set of integrated services AI advantages
136
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Information Technology Architect AI IBM Watson
IBM Watson allows to manage "the recognition of concepts and entities within user sentences, and can also manage the levels of dialogue, if my interaction is not a yes-no question but is based on a dialogue, the Assistant system is able to retrieve pieces of information maybe that had been said 3 or 4 interactions ago, and combine them in a cosistent context"
AI allows to manage complex forms of communications by retriving information from a small number of interactions
Big company Watson retrieves information from interactions AI and HR
IBM Information Technology Architect AI IBM Watson
IBM Watson provides "a service that makes the analysis of the text in terms of finding concepts, relationships, entities within the speech, for example, you have added a further piece. If you add a piece that manages for example the management of internal knowledge, and therefore is able to associate to a certain question that has been asked, the answers that come from what is my wealth of knowledge, mine as a company, you added yet another capability, which is to give sensible answers to questions"
AI improves KM processes through better document organization and analysis
Big company Watson manages the company's knowledge assets AI and KM
IBM Information Technology Architect AI IBM Watson
IBM Watson, "by assembling together cognitive services with clearly also traditional services, […] can build complex applications and solutions as you like, that expose even more human capabilities at the service of people
AI allows to assemble multiple cognitive services to develop complex solutions that involve multiple human capabilities at the service of people.
Big company Watson allows to assemble multiple integrated cognitive services to develop complex solutions AI advantages
IBM Information Technology Architect AI IBM Watson
"These [AI] systems must be trained and the training is done by humans so if I take a person who maybe does a training [...] based on old notions or even based on incorrect notions, or spoiled by prejudices, it is clear that the automatic system will learn the old story or otherwise spoiled by prejudices"
If the training is inadequately carried out, the result will be flawed.
AI can be flawed by inadequate training AI advantages
IBM Information Technology Architect AI IBM Watson
"To avoid these situations IBM was, I think, one of the first companies to equip itself with a layer that goes, let's say, to add itself to what is the operational chain of artificial intelligence, and that is basically summarized in our solution called IBM OpenScale, which has precisely the task of supervising the machine learning models, although the model seems to us a black box in the sense that we do not understand how it works, OpenScale is able to go and see the model while working and to pull out the criteria for which the model has taken a certain precision rather than another"
Big company, through OpenScale, has worked to keep the AI learning processes under control and thus improve the systems' accuracy
Big company OpenScale is a system to improve the learning system of the AI IBM OpenScale
IBM Information Technology Architect AI IBM Watson
"Artificial intelligence by itself does not complicate and does not simplify the aspect of data protection because in fact the presence of my personal data inside a server and the use that can be made of that data does not depend on whether this use is made by a cognitive system or not"
The use of personal data within systems or networks is independent of the use of AI
AI does not affect the use of personal data
AI and data privacy
IBM Information Technology Architect AI IBM Watson
"IBM has a very clear policy of use. First of all, the data of customers or users are never extrapolated from what is the context of the solution [...] IBM ensures that both the training data and the data that are then managed by the solution will never be used for different purposes"
AI can be used with individual respect and secure their personal data
Big company carefully manages the personal data of customers and users
IBM - Data Privacy
IBM Information Technology Architect AI IBM Watson
"Our cloud and, in general, our artificial intelligence solutions, have a very strict aspect of control over them, in our service level agreement it is clearly stated that personal data belongs to customers, so to speak, to users, and are used simply for the purposes that are declared to be used"
AI can be used with individual respect and secure their personal data
Big company applies strict controls on personal data management on Big company Cloud and AI system
AI - on Cloud / data privacy
137
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Information Technology Architect AI IBM Watson
Many IBM Watson solutions "can be used immediately by anyone who is able to the basic functions of a computer"
AI does not require a lot of computer skills for the use of many components
Big company Watson easy-to-use solutions AI advantages
IBM Information Technology Architect AI IBM Watson
"The data scientist, therefore the expert of the management of data processing, may be required when the complexity of the model is such as to require a very high degree of customization. If, for example, I want to do something [...] about certain market trends to understand which is the product and so on, maybe a data scientist can help me and can use, together with the business user, Watson solutions to build these very particular models that can then be inserted into the solutions and then realize the functionality that is needed"
AI may require the intervention of a data scientist for very complex processes.
AI system needs a higher level of knowledge to process complex systems AI advantages
IBM Information Technology
Architect AI IBM Watson
"Watson is currently used within IBM and is used in a variety of business functions especially in personnel management, because artificial intelligence helps to
find the right match between people's skills and the job"
AI is used within the company for personnel management to find the right match between
people's skills and the work done
Big company Watson employed in HR AI and HR
IBM Information Technology Architect AI IBM Watson
"IBM, but also other companies, now have systems that by analyzing the curriculum, are already able, for example, to classify the skills of people and direct them more wisely to a certain type of job than another"
AI used in personnel selection to analyze skills and identify those needed for a given job
Big company Watson and personnel selection AI and HR
IBM Information Technology Architect AI IBM Watson
"I find myself today automatically all my skills added to the internal system of human resources that traces my professional evolution, the system [IBM Watson] suggests to me what are the most appropriate things that I have to put in my curriculum and does so in a very careful way, that is, it does so on the basis of what is then the real evidence"
AI applied to skill management highlights skills and professional development on the basis of real evidence
Big company Watson applied to skill management AI and HR
IBM Information Technology Architect AI IBM Watson
"As well as the help in research, the fact of having Watson behind the research helps you to find more relevant resources in terms of documents, people, than before. So even in this, artificial intelligence has greatly changed the way of being in the company"
Use of AI in the corporate intranet finds more relevant resources and changes the way of being in the company
Big company Watson improves corporate intranet searches AI and HR
IBM Information Technology Architect AI IBM Watson
"In cognitive applications, you must still take greater account of the human factor, in the sense that being systems that basically manage unstructured information that comes mainly from people, can not be carried by weight, perhaps from one part of the world to the other and adapted without a minimum of adaptation"
Cognitive applications of AI cannot be taken from one part of the world to another without a minimum of adaptation
Need for adaptation of AI systems when taken to different geographical areas
AI and internationalization
IBM Information Technology Architect AI IBM Watson
"Artificial intelligence solutions […] can help medical staff or humanitarian associations to better understand the needs of people who may be refugees or people in need of help, perhaps in different parts of the world"
AI helps the human to improve of the quality of life
AI improve quality of life AI advantages
IBM Europe Automation Practice & Delivery Leader
"Watson is a set of products and capabilities that IBM has developed [...] When it comes to automation and knowledge management [...] We call anything that will help represent a repetition of work for a human, we will capitalize that as automation"
AI allows to manage knowledge through an automation system that will help machines to represent the repetition of human work
Big company Watson in knowledge management uses automation to represent repetitive works
AI and automation
138
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Europe Automation Practice & Delivery Leader
Layer two [of IBM Watson] would be that the answer that you give comes from many sources, right? It would be a combination of something that is written in a paper, so you would ask that student to read a particular paper or a text from a particular doc, something like that. It could be a picture, seeing videos, so that are multiple type of content that you will have.
AI can allow to find information by analyzing large amounts of data and take those related to the knowledge
AI system and KM from different structured and unstructured sources AI and KM
IBM Europe Automation Practice & Delivery Leader
Like on Facebook, if the feedback is positive, you will give a thumbs up, if the feedback is not positive you will give a thumbs down. Every time you do that, Watson learns. If you give thumbs down, Watson will roll the curated content back to the Knowledge Manager, like you, saying this is not correct anymore. And then you will have a chance to correct the content.
AI adopts systems to improve information content through feedback
Big company Watson adopts information content improvement systems AI and KM
IBM Europe Automation Practice & Delivery Leader
"The main advantage [in the use of Watson's cloud system] of information dissemination is that I can reach to large sets of people the information, as soon as I see this information, it is available to all those users"
AI provides information and knowledge dissemination
Big company Cloud and information dissemination AI - on Cloud
IBM Europe Automation Practice & Delivery Leader
Using IBM Cloud for information dissemination "I can segregate the users and I can look at the amount of information that should be available to a particular user, and I can make sure only the relevant user gets that relevant information, so I can control GDPR, any kind of government regulations that apply, particular users aim to get particular information, and I can do it in a cost-effective manner"
AI on Cloud allows to manage large amounts of data and ensure compliance with data privacy
AI on Cloud and Data Privacy policy Data Privacy
IBM Europe Automation Practice & Delivery Leader
"In terms of use of information [in IBM Cloud], when Watson is set with a lot of these large datasets of information, you can also now perform analytics on these large datasets, to understand which dataset is corresponding to another, adding more insights coming out of it, giving more insights coming out of it, who are the most frequent users of the data, what is the way in which they are using the data, all of those things can be monitored"
AI system on Cloud processes large amounts of data, and performing complex analysis, identifies relationships, and analyzes access and use of information
AI system on Cloud works on large amounts of data by performing complex analyses AI - on Cloud
IBM Europe Automation Practice & Delivery Leader
"Watson is constantly learning. So even if the information is incorrectly input and coded into Watson, it will be quickly rejected by the user without using it. As soon as they find that that information is not relevant or it is not making sense, we are going to get that feedback that they are not happy with that information, and so it gets rejected in the system"
When you enter obsolete or incorrect data, AI system can correct data because the continuous learning process will receive negative feedback that will reject that information
AI system as a learning system that excludes obsolete or incorrect information AI advantages
IBM Europe Automation Practice & Delivery Leader
"Personal data can be controlled completely. Watson looks at who needs information, then if the person has it in excess then what is the level of content that the person has, and what is the time frame for which that information needs to be provided. So, all of those things can be deployed to effectively make sure of compliance to all regulatory bodies."
AI can be used with individual respect and secure their personal data
AI system respects data privacy
AI and data privacy
IBM Europe Automation Practice & Delivery Leader
"It takes about five minutes to learn [how to use IBM Watson], so it is not complex at all"
AI is easy to learn and to use
Big company Watson is not complex to learn AI advantages
IBM Europe Automation Practice & Delivery Leader
"We had a lot of internal wikis and an IBM connection tool. These used to be the most common tools for storing the knowledge. But other than that, we would also use a lot of commercial tools, that could be SharePoint"
AI provides information and knowledge dissemination
Big company used multiple knowledge sharing tools
IBM - KM in the Past
139
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Europe Automation Practice & Delivery Leader
KM through cognitive tools such as IBM Watson "has been optimized, definitely optimized, but optimized in the sense that the Knowledge Management was never a rule per se, every one of us, as part of our jobs, we would make use of Knowledge Management tools and also sharing that knowledge with other IBMers"
AI allows to optimize knowledge management and save time but this is not a fixed rule for every person
Big company Watson allows to optimize KM AI and KM
IBM Europe Automation Practice & Delivery Leader
"So we use [IBM Watson] systems to make more time decisions on our work on a daily basis"
AI helps humans to make better decisions
AI improves human decisions AI advantages
IBM Europe Automation Practice & Delivery Leader
"Watson does not have the physical boundaries, once you apply Watson it can be used by anyone, that person might for instance be based in India, Japan or America"
AI has no physical boundaries but can be used by anyone, anywhere in the world
Big company Watson applied globally and internationally
AI and internationalization
IBM Europe Automation Practice & Delivery Leader
"It really depends on the curation of the content, so if my colleague cannot understand English, they will need to use Watson, they will need to curate content on their own"
AI may have a limit in KM due to knowledge of the language in which the information is handled
Big company Watson, KM and language use AI and KM
IBM Europe Automation Practice & Delivery Leader
"IBM is moving towards open cloud and making sure that these technologies are available to the general public as well, so they can come up with their own usage of these Watson technologies, sharing our knowledge so that, you know, the manager can make more use of this knowledge and create more content"
AI on the Cloud adds new business opportunities
Big company Watson on Cloud creates more opportunities for knowledge sharing AI and KM
IBM AI IBM Watson Explorer Architect
"I work with is unstructured data, or textual data, or what we call content and the main part of the content that work with this textual nature so think about documents, think about word documents, PDFs, e-mails or tweets, SMSs, anything where there’s written text [...] I work with tools that help those organizations extract meaning from that text and therefore not just one meaning from just one document, e-mail or whatever but from thousands, tens of thousands, up to tens of millions of documents"
AI can allow to work on structured and unstructured data, managing large amounts of data
AI system and management of large amounts of data of different nature AI and KM
IBM AI IBM Watson Explorer Architect
"So the mere sign that [through IBM Watson] we can reach documents, that volume, and interpret them and understand them in a way that a business person would interpret and understand them, means that we
can understand and analyze orders of magnitude, more data, unstructured data, that any human being could, and we can do more accurately and consistently"
AI can allow to work on structured and
unstructured data, managing it as accurately and consistently as an industry expert would
AI system enables accurate and consistent data and KM AI and KM
IBM AI IBM Watson Explorer Architect
"Human beings are actually quite poor at understanding and interpreting textual data, so a system like Watson can do it more consistently and more accurately and to way more, you know, at a much higher volume, speed, that a human being could"
AI can allow to understand and interpret large amounts of data very quickly
AI system enables faster data and KM AI and KM
IBM AI IBM Watson Explorer Architect
"The volume of information shared with the employees and gained from employees is far too vast for any one person or group of people to effectively analyze, interpret and use in any way and Watson allows us to do this very very quickly and also more actively and reliably than human being could"
AI enables all employees to use the corporate intranet in a fast and reliable manner
Big company Watson used by all employees in a fast and reliable way AI, KM and HR
140
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM AI IBM Watson Explorer Architect
"If you’re just going to rely on machine learning without any kind of governance around, who does the machine learning? Who gathers the training data? And how the training data is used? And that is a real danger, in fact that will absolutely happen. But in the techniques we use, first of all, we provide also the tooling for governance, and for data quality analysis, and then, as I said, we don’t just use machine learning methods, that’s just one of the techniques. Another major technique we use, linguistic rules, helps to mitigate that risk".
To reduce the risk of errors in AI's learning process, alternative governance tools and qualitative data analysis, such as linguistic rules, must be used
With AI system alternative governance tools and qualitative analysis can be used to reduce learning errors AI and KM
IBM AI IBM Watson Explorer Architect
"The principle that personal data belongs to the person and not to the organization that speaks to leverage it has a profound effect on anything not just unstructured data but also structured data, any data we have from, any organization has from customers. So requires an extra level of vigilance, and care in dealing with the data [...] I can say about the policy and that is any customer that uses IBM services, cloud services, can usually opt out to of having IBM do anything with the data they work with. So, what I mean by that is, when you use a Watson service, you are sending data or using your own data to train the service. Unlike our competitors, IBM will not, if you do not wish, IBM will not learn from that data".
AI can be used with individual respect and secure their personal data
Big company protects personal data and applies control systems
AI and data privacy
IBM AI IBM Watson Explorer Architect
"There are two different groups, end-users, which is complex or easy as you make the solution, Watson solutions are just a way of building an application for an end-user, to do something. The tools themselves to configuring use are generally very straight forward, and easy to use. It’s a question of getting comfortable with them and the greater challenge is giving accurate and effective data with which to train them"
AI solutions are written applications for end users and can be used simply and intuitively
Big company Watson easy-to-use solutions AI advantages
IBM AI IBM Watson Explorer Architect
"The main problem with trying to use these services that are based on a certain level of unstructured data is if human beings are just very poor at understanding of processing any data, any amount of data really, so the first thing Watson does by simulating how we interpret and understand textual data, Watson allows us to get to a much larger amount and process more consistently and reliably"
AI can allow to collect, interpret and process large amounts of structured and unstructured data quickly, consistently and reliably
AI system enables large amounts of data to be processed quickly, consistently and reliably AI advantages
IBM AI IBM Watson Explorer Architect
"So that can be any process, that can be HRM process, we’re trying to figure out who’s performing best based on reports of their work, that’s based on, that could be employees’ surveys, trying to figure what employees think of their organization, customers surveys, anywhere where textual data is at the heart of the process, Watson provides a way of analyzing that much more effectively"
AI improves HR processes for information analysis, helping and speeding up personnel management systems.
In HR, Big company Watson provides the way to collect feedback and process data AI and HR
IBM AI IBM Watson Explorer Architect
"The IBM Cloud solutions are online and accessible to anyone who has internet access, so anyone can get it online, go to IBM Cloud and start, and provision a service, and use it. You can also buy a license for some forms, almost all of them now IBM, download it and run on a private cloud behind your firewall. Watson solutions in general support 11 languages and can score up to 20 languages depending on what you’re trying to do"
Multilingual support provides effective service to implement AI on Big company Cloud
Big company Watson on Big company Cloud with multilingual support AI - on Cloud
141
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Senior Watson AI Consultant
"There is no longer a limit to the sources, new problems have arisen, problems concerning which information is relevant, which are true, which are the most correct. I speak especially with regard to companies, the figure, which does not have to be necessarily the last, that is the most correct [...] "This is not valid in large companies where knowledge of the various portals has different certification times"
In data management, unstructured data in particular, there may be problems in certifying the validity of the information
Information validity (knowledge) must be certified KM
IBM Senior Watson AI Consultant
"The protection of personal data varies from project to project, it is not so much Watson but the type of project you are dealing with"
AI can be used with individual respect and secure their personal data
Data protection does not depend on the use of AI system
AI and data privacy
IBM Senior Watson AI Consultant
IBM Watson's simplicity of use "depends on the type of application, some can be used even if you do not have specific knowledge and then it is enough, others instead require the technical knowledge"
AI is easy to learn and to use
Some Big company Watson applications are very simple others are more complex AI advantages
IBM Senior Watson AI Consultant
"Before IBM Watson, knowledge was managed through repositories of information and it could happen that the information entered and shared could be contradictory to each other. IBM Watson has transformed the way information is managed, making this process more transparent and effective"
AI has transformed KM by making it more transparent and effective
Big company Watson and optimal KM AI and KM
IBM Senior Watson AI Consultant
IBM Watson's "the ability to process a quantity of information that has no precedent, can only be able to read so much information and propose it to a specialized professional already a sort of summary on papers that would have taken at least months to search for"
AI has transformed KM by making it faster and more accurate
Big company Watson and fast and accurate knowledge management AI and KM
IBM Senior Watson AI Consultant
IBM Watson "provides much more information than before, makes, even the decisions of the professional more facilitated by more information, then the decisions can be made by the professional, but enabled by more information"
AI helps humans to make better decisions
AI improves human decisions AI advantages
IBM Senior Watson AI Consultant
"Every single [Watson] experience in the world is important, because it is a competitive advantage, every experience brings a lesson and must be shared within us.
AI experiences become a shared asset of a company and used worldwide
Big company Watson used worldwide to gain competitive advantage
AI and internationalization
IBM Senior Watson AI Consultant
"Truth management has been one of the main topics of discussion for a long time, it is one of the dilemmas because the training part is one of the most difficult of these systems and there is the shortcut of self-learning, in some cases it works well, in other cases it does not work so well" [...] "when these systems are not controlled in which what is generated is not exactly what was desired, that is why we go to check the training base that we provide"
Optimal management of learning systems means that the training system is properly controlled to improve the quality of the data processed.
Monitor AI system's learning system to improve quality AI and KM
IBM Project Manager Application Automation "IBM Watson is very easy to use and easy to learn it"
AI is easy to learn and to use
Big company Watson is not complex to learn AI advantages
IBM Project Manager Application Automation
"Watson's IBM Cloud Strategy is powered by the latest innovations in natural language processing, visual recognition, and automatic learning, and thanks to its recommendations, intuitions, insights, Watson can predict and model business forecasts for companies, so that it can improve critical decisions by reasoning in real time with its integrated Machine Learning processes "
AI's Cloud Strategy is aligned with the latest innovations in the use of natural language, and helps improve critical business decisions
Big company Watson applied to decision-making AI advantages
142
Interviewee Representative Quotation Concept Concept aggregation Theme
IBM Project Manager Application Automation
"Business workflows become smarter because Watson integrates into workflows to add AI where it is needed"
AI and AI integrates with all major business workflows, improving processes
Big company Watson integrates into major business workflows AI advantages
IBM Project Manager Application Automation
"Watson on IBM Cloud allows access to unstructured data, and can learn from small data sets, that is the quality of the data that makes the difference, not the quantity, and helps to increase its value by analyzing it more deeply, the Deep Learning mechanism"
With small data sets, AI system identifies models and relationships by processing structured and unstructured data
AI system performs in-depth data analysis AI advantages
IBM Project Manager Application Automation
"By simplifying, accelerating and regulating deployments, AI enables organizations to produce business value"
AI simplifies and accelerates the distribution of information by creating business value for companies
Big company Watson allows you to simplify and accelerate the dissemination of information AI advantages
IBM Project
Manager Application Automation
In IBM Watson's learning processes "the dirty data is also taken into account, but a minimum percentage,
even if loaded continuously, does not affect the forecasts or the percentages of effective applicability for which you identify the suggestions"
AI system applies systems that take into account incorrect data and activate forms of correction that
do not affect the effectiveness of the analysis
AI system autocorrects incorrect data AI advantages
IBM Project Manager Application Automation
"The information is absolutely protected, and a correct diffusion and diffusion of the data cannot prescind from accurate policies of security. IBM is committed to providing customers and partners with innovative solutions for privacy, security and data governance"
Big company's strategies include a commitment to ensuring customer privacy and security in personal data management
Big company applies strict controls on personal data management on Big company Cloud and AI system AI - Data Privacy
IBM Project Manager Application Automation
To take advantage of many IBM Watson features "just basic IT application knowledge is required; Watson Machine Learning is an integrable solution and allows an inter-functional team to deploy, monitor and optimize models quickly and easily "
AI does not require a lot of computer skills for the use of many components
Big company Watson is not complex to learn AI advantages
IBM Project Manager Application Automation
"Watson Machine Learning's intuitive dashboards make it simple for teams to manage models in production, and its uninterrupted workflows enable new, ongoing training to maintain and improve model accuracy"
AI Machine Learning's dashboards enable continuous improvement of models' accuracy
Big company Watson improves work processes AI advantages
IBM Project Manager Application Automation
Before IBM Watson and AI were used, KM in IBM was handled "essentially through communities and posts within them, and topics were searchable by keyword in a search box within the corporate network through a search engine; obviously search results were generic and by keyword"
Old Knowledge Systems use keyword and limited possibilities
Old KM with limited possibilities
AI- KM in the Past
IBM Project Manager Application Automation
Artificial intelligence will transform the world in dramatic ways in the coming years, and IBM is advancing in the field through its portfolio of research focused on three areas, advancement of AI, rescaling AI, and confidence in AI.
Evolution of AI in Big company to extend it and support confidence in its use
AI evolution at Big company AI advantages
143
APPENDIX E: Coding of Public Domain Interviews and Speeches
Interviewee Representative Quotation Concept Concept aggregation Theme
SAXENA
"What Watson can do is read through in fact all of the Wikipedia all of most of the world's information we need to put into Watson now it can read a paragraph [...] and say you know based on my understanding of human semantics and syntactics and synonyms and similes and metaphors I can
deduce that Jack Welch and GE at this time right it's the beginning of computers that start and you know what you got as cognitive computing computers that understand not just calculate"
AI facilitates the identification of information within a large number of documents
AI facilitates information identification AI advantages
SAXENA
"If Watson learns like we learn as human beings Watson learns by reading stuff so Watson's learns over two million pages of cancer research and all of cancer data in the world is already unit so Watson learns by reading just like we do. Watson learns when people ask questions of it just like a parent and teachers ask question of us and say you know what do you think about it in the characters, we have oncologist we have call center people training and teaching Watson and correcting Watson and then Watson learns by doing just like we learn by doing this is a whole different paradigm of machines that are able to learn and interact there's not a whole lot of programming in modern Watson it's more reading and understanding and interacting and growing from it."
AI learns and activates systems to correct incorrect information
AI autocorrects incorrect data AI advantages
SAXENA
"With technologies like Watson, humanity now, if you look out 50 years. 100 years I believe is going to be a defending of the knowledge for humanity because now you're not just capturing the information about people capturing the knowledge about those people that experiences world people" ... "We are capturing his knowledge and putting this into a machine so generations from now on can benefit from his knowledge and insight"
AI provides information and knowledge dissemination
AI can manage the knowledge for humanity AI and KM
SAXENA
"Imagine a technology like Watson working as an assistant to the doctor right this is not making decisions on behalf of you, it is like a GPS system for doctors. That's taking all the knowledge that's known to mankind and it's like having the power of a thousand best oncologists behind every oncologist is out there in doing a diagnosis because the machine is able to understand and comprehend stuff that the human brain just cannot"
AI allows to manage information as first processing and does not eliminate the human component that must analyze it in detail.
AI in KM provides decision support
AI and decision-making
SAXENA
"A government agency, you can start imagining but particularly where the kind of recent stuff that we have been through here also how they could start using technologies like this so very promising areas
where I see a massive shift and a mass movement towards adoption of a new technology (AI and IBM Watson) that will change the way we think, act, and learn from."
AI and AI's new technology change the way of thinking, acting and learning of people
AI change the way of thinking, acting and learning AI advantages
144
Interviewee Representative Quotation Concept Concept aggregation Theme
WEBER
"The MIT IBM lab is a new initiative just started last year IBM has committed 240 million dollars to joint research and we think of it as the leading academic
and industry alliance in advanced AI research"
Big company high investiment in AI research
(with MIT) Strategy: AI AI strategy
WEBER
"So the advance that I'm most excited about right now is advances in deep learning for graph structure data. So deep learning to date has mostly been focused on Euclidean one-dimensional, two-dimensional three-dimensional data structures, Euclidean data structures, but non-Euclidean data structures like graph data or manifest data is much more difficult because you're not just accounting for the various observations, you're accounting for the relationships between observations and that causes the combinatorial complexity to explode." ... "Just earlier this summer two of my career my colleagues that at IBM Research Geo Chen, Teng Fame, and Danica Chow published a new giant leap forward in this effort called fast GCN and they were able to use important sampling and integral transformations that that improved speed on deep learning for graphs by orders of magnitude above previous benchmarks."
AI manages complex data elaboration and correlation
AI leads to more accurate data analysis AI advantages
WEBER
"If we can develop much better techniques for monitoring large graph data sets, like transaction data sense, and identifying patterns, identifying suspicious nodes then we can I think make the financial system a much safer place and that, in turn, I hope, can lead to more financial inclusion"
AI makes the financial system safer and data management improves AI is safer AI advantages
WEBER
"If you look at the results from the fast, you see on paper and you can blog it very easily, fast GCN IBM and you'll find they find the information, was the ability to improve speed, holding the computing power constant they're able to improve speed by orders of magnitude without sacrificing accuracy"
AI allows to be faster at managing data but with adequate levels of accuracy AI is fast AI advantages
WEBER
"And I believe strongly in a vertical integration of our values throughout every operational aspect of our business so I would be in research that means that when you're thinking of social good it's not just
like what special social good programs do we have but how do we conduct ourselves with every single person that we interact with, so that's office place ethics and how we respect one another that's how you relate to and respect clients and customers, that's how you relate to and respect even your competitors you know your suppliers" AI is used for social good. AI used for social good AI advantages
AUSTRAAT
"Watson […]it's a set of capabilities and our customers can compose them in a way that addresses their needs. So that could be anything from just using one capability […] or building out entirely transformative solutions between low yield and medium yield cognitive tasks"
AI as a centralized platform of integrated services to manage all business processes and build transformative solutions
AI allows to assemble multiple integrated cognitive services to develop complex solutions AI - Cognitive
AUSTRAAT
IBM Watson "is a system that knows how to understand, reason, learn, and give insight and it uses a number understanding of documents, to understand images, understanding audio and even doing things like machine translation"
AI facilitates the identification and enrichment of information from a large number of documents
AI facilitates information identification and enrichment AI advantages
145
Interviewee Representative Quotation Concept Concept aggregation Theme
AUSTRAAT
"With Watson we have very consciously tried to democratize machine learning and data science so if you look at for example what would it take to take your unique corpus of let's say millions of pages of operating procedures or any other large corpora that that you would like to annotate and enrich and make uniquely useful to you"
AI is easy to learn and to use AI easy-to-use solutions AI advantages
AUSTRAAT
"Watson Knowledge Studio […] is an online web-based tool that lets you with very little training actually create machine learning models by simply highlighting text and teaching Watson"
AI allows faster and easier access to data in processes from various sources AI is fast AI advantages
AUSTRAAT
"We have a customer in the insurance world and we hyper accelerated their ability to adjudicate claims. So, the claims adjustment process went from three hours to a few minutes"
AI allows faster and easier access to data in processes from various sources
AI and facilitated data access AI advantages
AUSTRAAT
"Transformation with cognitive comes with obviously a transformation of functions, individual functions, but then also of the overall enterprise and that touches everything from the complete employee and engagement lifecycle from hiring, selecting, using this technology and then ultimately then repurposing it for training as well. So it really means looking at your enterprise holistically, looking at transformative power, not just as a speeds and feeds, but also as a business process and business model, sometimes"
AI used in personnel management to analyze skills and identify those needed for a given job
AI and personnel management AI and HR
KENNY
"She has a very rare form of leukemia but because the system was built on a lot of prior data including every patient ever at Memorial Sloan-Kettering they found in ten minutes what they had not solved in six years and she's better and I just think the chance to actually extend life to bring computing power to these real everyday decisions matters"
AI allows to be faster at managing data but with adequate levels of accuracy, enhacing everyday decisions
AI enables more efficient data management and decision support
AI and decision-making
KENNY
"What we're automating is sort of the base level rote work, and that frees up time and capacity to solve bigger problems and to actually be more clear what's on solve so you can find in the patterns of leukemia what we don't know and that'll help researchers find solutions"
AI allows to save time and helps to find solutions to complex problems
AI saves time and helps solving complex problems AI advantages
KENNY
"The world still has a lot of unsolved problems so for me I think freeing human capacity to solve hard problems is what all this is going to do"
AI frees human capacity and helps to solve complex problems
AI helps solving complex problems AI advantages
KENNY
"30% of the Watson diagnoses had not been found by humans so it opened new doors and new answers for one thing I would say secondly we do find that the machines do, over time, come to better predictions"
AI learns and comes to better predictions over time AI constantly learns AI advantages
ROMETTY
"Watson can run on your premise, it can run in any cloud and it can connect between them, and so that's really what clients are asking for"
AI meets clients needs by running on premise, on cloud, and connecting between them AI meets clients needs AI - on Cloud
146
Interviewee Representative Quotation Concept Concept aggregation Theme
ROMETTY
"We actually we had to do a lot of work around the IBM cloud private which is what Watson runs on […] Red Hat is coming up and so this allows it to move anywhere out there. This is a big piece […] of hybrid cloud which you've heard me say we think that's a trillion-dollar market and we'll be number one in it so that gives you a good feeling"
Big company high investiment in cloud platforms AI and Cloud AI - on Cloud
ROMETTY
"The AI we are working on is to train with a lot less data, I mean the next state-of-the-art Rev on AI is less data, in fact one-shot learning it's called"
AI in the future will be trained with less data AI will learn with less data AI in the future
ROMETTY
"We're also announcing today the most secure public cloud you will see an announcement later today that it is the most secure public cloud something called hyper protect that's out there. So they would walk away and say if I've got to modernize my mission-critical apps IBM is the only partner to do that"
Big company applies very strict cloud and AI controls to ensure that personal data is used for purposes for which they were declared
Big company applies strict controls on personal data management on Cloud
AI - Cloud - Data Privacy
SUDARSAN
"Imagine you're offline you're in airplane mode you have no connectivity you have, you know, inconsistent connectivity, you can still run your machine learning models and you can actually point your camera to or take pictures with your iPhone or iPad and have them classified locally"
AI does not require internet connection for data classification AI and data classification AI advantages
SUDARSAN
"We also made available an SDK (software development kit) that allows you to very easily combine your offline visual recognition processing with the rest of the IBM Watson services on the cloud and so as a developer again you can very easily call, you know, for additional richer insights from data from services that are running on the cloud that has access to a lot more data"
AI allows to retrive more data from multiple services AI and optimal KM AI and KM
SUDARSAN
"So that's where now you're combining the speed, and the processing in an offline nature with the
richness of insights that you can actually get from the cloud"
AI on Cloud processes large amounts of data and
information quickly even when offline
AI on Cloud manages data
and information quickly and offline AI - on Cloud
SUDARSAN
"Being able to be productive and now use their camera instead of just pointing and using the light for that now they actually can start scanning and capturing information so it saves them valuable amount of time, in part identification, in issue detection, diagnosis, and then in some cases, you know, repair ideas as well, right? And that's kind of the whole way of looking at it"
AI can detect, diagnose, and even "repair" ideas, and saves significant amount of time
AI saves significant amount of time AI advantages
ROSSI
"For us it should be trust between the users of an AI system and the AI system itself so the system should be trustworthy in the sense that maybe is not biased, is fair, is explainable, and the way uses the data of the user is transparent"
AI must be trustworthy and transparent in data processing
AI must be trustworthy and transparent AI ethics
ROSSI
"Another dimension is trust among different stakeholders involved in in AI and among different communities, among different corporations producing AI, they should collaborate we are beyond the fact that they may compete on the marketplace"
AI must be trustworthy and foster collaboration between the different stakeholders
AI must be trustworhy and foster collaboration AI ethics
147
Interviewee Representative Quotation Concept Concept aggregation Theme
ROSSI
"Trust among different cultures that may have a different idea of how AI should be developed, deployed, and used"
AI must promote trust among different cultures
AI must promote global trust Ai ethics
ROSSI
"We think that if AI is not trustworthy then it will not be adopted as widely as it could be and these benefits would not be exploited by us and it's not good to under trust AI because if we don't trust AI enough then we will not be able to get all the benefits that it can give but also if we trust it too much also it's very bad because we are assuming that it has capabilities and maybe it doesn't have so that's not going to be good so we really want to build AI that we can understand what is the correct level of trust"
AI should allow users to understand its correct level of trust
AI must promote its correct level of trust AI ethics
ROSSI
"I would say two years or two years and a half there has been really a huge amount of initiative that have been started, research centers, units within corporations, or within universities, or within governments, declarations, strategies for AI in Europe, everywhere, China, Russia, U.S., wherever, and so around, you know, really trying to understand what it means to build AI that is beneficial for individuals for societies and in trustworthy, in a responsible way such that not only the AI can be trusted but also the corporation building AI can be trusted"
Individuals, corporations and institutions must strive to understand its correct use of AI that is beneficial, responsible and trustworthy that allow trust in AI and corporations
AI must promote its correct level of trust Ai ethics
ROSSI
"IBM [...] has inside a lot of initiatives both from the research point of view there are a lot of papers that are being published regularly around how to detect bias in data, how to mitigate and how to recognize bias even if you don't have access to training data, how to make AI systems more explainable and so from the research point of you, how make them value alignment to make sure that they fall of some optimization criteria and to reach some objective, but at the same time also they follow some ethical guidelines that may be relevant for the tasks that they are trying to address and so inside there is a lot of work in terms of research, but also work in terms of collaborating with the rest of the world in trying to understand what it means to build this responsible AI"
Big company strives to build responsible AI through publications on detecting bias and by following ethical guidelines
Big company strives to build responsible AI AI ethics
ROSSI
"IBM has published a data responsibility policy, IBM is a company where we don't want to we are not going to reuse the data our clients for other clients or other tasks and that of course is very, you know, very attractive for our clients but on the other hand put us in a kind of a more difficult position because of course if you have less data than, you know, your machine learning approaches, your data driven approaches, have less data they can work with, so we have to compensate with other things like symbolic AI, domain knowledge, reasoning and so on"
Big company applies very strict cloud and AI controls to ensure that personal data is used for purposes for which they were declared
Big company applies strict controls on personal data management on Cloud and AI
AI - Data Privacy
148
Interviewee Representative Quotation Concept Concept aggregation Theme
ROSSI
"For us AI should augment human intelligence and not replacing it so that means that we are focused on that kind of AI because we are working to help other companies to use AI in whatever they need to do so, want to build AI that helps professionals do their job as well as possible"
AI does not eliminate human intervention but helps the human being to work more effectively
AI allows to work more effectively AI advantages
ROSSI
"The partnership on AI was funded by six companies, among which IBM [..], we started this idea of a platform for discussion, multidisciplinary, a multi-stakeholder discussion on issues related to the pervasive deployment of AI in our society and the impact of AI we decided that this initiative was going to be open not just to companies but to many other stakeholders like NGOs, civil societies, universities, professional associations, so now we have 53 partners starting from six in beginning of 2017 [...] of which only I think about 30% are companies and everybody else is non-for profit
because we think that only this very multi-stakeholder approach can help really understand what the issues are, identify them define them and resolve them and possibly get to the best practices on how to deal with these issues"
AI must be trustworthy and foster collaboration between the different stakeholders
AI must be trustworhy and foster collaboration AI ethics
NOAH (AI robot)
"Robert, did you know that 80% of the world's data is unstructured and 80% of that has come about over the course of the last two years?" […] "And it is only going to grow if you think about all the videos and tweets that you are all sending now but also the research materials, logs, and personal sense or data that everyone makes every day. Until now we have not had the ability to look and analyze that data but with Watson we can"
AI allows to find more insights by managing large amounts of data of different nature
AI and management of large amounts of data of different nature AI and KM
NOAH (AI robot)
"Let's look at sense personality insights capability now. This helps companies understand the individual. Everyone likes to be interacted with differently. Personality insights is able to understand what each of us is looking to do, how we expect to be treated in the interaction, communication, and engagement that we prefer"
AI allows to understand personality insights and patterns of interaction, communication and engagement
AI helps to understand individual traits AI advantages
COLE
"The way in which we see what Watson can do is it's very much helping as we said to scale, enhance, and accelerate that human expertise and not replacing it"
AI does not eliminate human intervention but helps the human being to work more effectively
AI allows to work more effectively AI advantages
COLE
"Imagine being a doctor and having all the latest medical reports, trends, treatment insights and research material at your fingertips helping to make the best decisions possible for your patients"
AI allows faster and easier access to data from various sources
AI enables more efficient data management and decision support
AI and decision-making
COLE
Cognitive systems like IBM Watson "interact naturally with humans, using vision, language, and speech which means they're able to read and ingest data in ways that are human wouldn't be able to do in as much complexity as it can but then help us as humans to understand it"
AI allows to find more insights by managing large amounts of data of different nature and helping humans understand it
AI and management of large amounts of data of different nature AI advantages
149
Interviewee Representative Quotation Concept Concept aggregation Theme
COLE
Cognitive systems like IBM Watson use "machine learning and deep learning to be able to learn at scale and all the knowledge about a particular subject whether that be medical, whether that be oil and gas, whether that be retail, whatever the domain that it's actually learning in"
AI learns from multiple domains of knowledge
AI and KM from multiple sources AI and KM
COLE
Cognitive systems like IBM Watson take "all of that data and turn it into suggestions to help people have a higher confidence level when making decisions"
AI allows to have a higher confidence level on decisions based on suggestions
AI enables more effective decision support
AI and decision-making
COLE
"All of this means that unlike traditional systems where they essentially you put it into a data center and they lose value straight away, actually cognitive systems get smarter, and therefore more valuable, the more data you put into them the more you interact with them and the more they learn"
AI learns and comes to better predictions over time AI constantly learns AI Cognitive
COLE
"Cognitive technology and machine learning is nothing new from an IBM standpoint. We've been doing it for the last kind of 30 to 40 years what is new is the commercialization of that technology"
Big company used cognitive technology and machine learning in the past but did not commercialized it
Big company use of cognitive technology and machine learning in the past AI Cognitive
COLE
"From there as you would expect from IBM we worked with and continued to work with big financial organizations to understand and help them differentiate themselves using this cognitive technology and then the really interesting thing from my perspective was that we started to look outside of the normal IBM kind of walls and open up the technology to anybody in everybody that wanted to come and play and enhance odd or build solutions on top of the Watson capabilities"
AI applied to operations outside big company to make the difference using cognitive technology
AI applied outside big company AI advantages
COLE
"We're also helping organizations with the objectives of saving money on call centers, but most importantly allowing their customers to have the information that they require available straight away and by interacting with an app or a robot in the same way that we would a friend or a colleague"
AI applied to call centers provide cost savings, time savings, and customer interactions with AI system resemble a conversation with a human being
AI provide cost savings, time savings, and better customer interactions AI advantages
COLE
"Watson is working with [New York Genome Center] by ingesting cancer patients DNA information and searches the vast medical literature to identify the most likely DNA mutations or other issues driving the cancer. It pinpoints relevant drugs that can target those specific DNA issues and prevents the information to doctors with the supporting evidence in a matter of minutes, instead of in a matter of weeks or months"
AI allows to find information by analyzing large amounts of data and take those related to knowledge
AI effectively manages information AI advantages
150
Interviewee Representative Quotation Concept Concept aggregation Theme
COLE
"From an IBM standpoint I can't talk for all organizations but there are strict security processes that we have in place in order to prevent [improper use of AI] happening but as I was talking to some of your colleagues earlier, obviously, you know, with anything they fall into the wrong hands then bad things can happen"
Big company applies very strict security processes to prevent improper AI use
Big company applies strict security processes on AI Ai ethics
COLE
"One of the things that that that cognitive technologies give us is the elimination of [the emotional] bias so that we can make decisions based on facts and based on the data and the knowledge that we have as opposed to bringing in that kind of bias"
AI allows us to make decisions without the influence of emotional biases from cognitive systems
AI allows to make decisions without the influence of bias
AI and decision-making
COLE
"We kind of describe the whole reasoning aspects of it now we have different capabilities within Watson that allows it to do different things and the more capabilities you put together the better the answer is going to be, the more confident the answer is going to be in the more scope that it is given to look at different areas so if it doesn't think that it's got the right answer, it will go off and come back with a set of different answers and reasons why it's come up with those answers to give you the ability to then choose which you believe the right answer is"
AI allows to have a higher confidence level on decisions based on suggestions
AI enables more effective decision support
AI and decision-making
COLE
"If you look at [Watson] from a medical perspective, you know, I still want my doctor making that advice but I want it to be based around the best possible information that he or she can have now that might be in a different part of the globe where they don't currently have access to, Watson could give them that access"
AI does not eliminate human intervention but helps the human being to work more effectively
AI allows to work more effectively AI advantages
COLE
"What's really important and we haven't really mentioned it is the curation of that data behind it and the ability to understand exactly what questions are being asked of the technology, and making sure that you are serving, you know, the clients, the customers, the people, whoever asking, you know, those questions, and making sure that you have the relevant answers AI it is being pointed at the right data to look at those answers moving forward"
AI understands exactly the questions posed to the technology and examines the right data to provide a more accurate answer
AI manages information effectively AI advantages
KELLY III
“AI systems […] will require new innovations to tackle increasingly difficult real-world problems to improve our work and lives”
AI helps to solve complex real-world problems
AI helps solving complex problems AI advantages
151
APPENDIX F: Coding of IBM Documentation
Interviewee Representative Quotation Concept Concept aggregation Theme
FORRESTER
"Major data science projects are more effective, generating $2.5 million in incremental revenue or cost savings per project. With improved access to data and modeling tools, data scientists can drive more value on major projects. With an average operating margin of 10%, this equates to an incremental $750,000 in operating margin per project."
AI increase profits and revenues, and saves costs
Cognitive and AI improve business profits AI advantages
FORRESTER
"Watson Knowledge Catalog helps organizations improve data governance policies, reducing the risk of penalties and fines from noncompliance. On average, organizations avoid up to $270,000 in fines and penalties per year with improved rules and policies managed with Watson Knowledge Catalog."
The use of AI (Knowledge Catalog) helps organizations to better manage governance policies and reduce risks and penalties for non-compliance
AI reduces risks in the correct application of processes by reducing penalties for non-compliance AI advantages
FORRESTER
"Difficulties in accessing data, using data in modeling tools, and writing code in existing tools limited data-scientist productivity. Data scientists are a valuable and expensive resource for organizations. The more time a data scientist can spend building models, the more valuable insights an organization can receive to use in important business strategy decisions." ... "Data scientists can also use Watson Studio to generate dashboards to more effectively share insights with business decision makers."
AI helps humans to make better decisions
Cognitive support to strategic decisions AI - Cognitive
FORRESTER
"With an integrated platform, organizations can reduce the cost of analytical toolsets, administration overhead, and external consulting. Organizations can replace some existing analytics tools and integrate data and modeling tools in one platform, creating a “one stop shop” for data analysis."
AI as a centralized platform of integrated services to manage all business processes
Integration of cognitive systems leads to optimization of costs and capabilities AI - Cognitive
FORRESTER
"In a managed-cloud (with IBM Watson) environment, infrastructure and administration costs are significantly reduced, and data scientists can access new environments immediately. Open source tools can be used in a managed environment, reducing compatibility and version control issues."
AI increases profits and revenues and save costs
Cloud and Open Source reduce costs AI - on Cloud
FORRESTER
"The organization’s data is in one place with (IBM Watson) Knowledge Catalog, structured and unstructured data, and administrators, data stewards, and chief data officers can easily create rules and policies to remain in compliance with security regulations and restrict access to sensitive information."
AI effectively manages personal data protection
AI and personal data protection
AI and data privacy
152
Interviewee Representative Quotation Concept Concept aggregation Theme
FORRESTER
"Collaborative features (IBM Watson Platform) like access to a community of peers and shared resources increases skills development and speeds up model development. With time savings, data scientists can experiment more and build more models. With a click of a button, data scientists can deploy models into applications."
AI provides information and knowledge dissemination AI improves KM AI and KM
FORRESTER
"Improving productivity across AI teams creates substantial business value: By increasing access to data, improving collaboration between roles, and increasing the speed at which data scientists can build models, data scientists can spend more time generating and delivering valuable insights."
AI improves productivity and increases speed with which models are built
AI improves productivity AI advantages
FORRESTER
"One of the most significant benefits for interviewed and surveyed customers (using IBM Watson) is the ability to efficiently generate and communicate important insights to business decision makers."
AI allows to find the insights of the texts and then extract the concept
AI enables more efficient communication and decision support AI advantages
FORRESTER
"Watson Knowledge Catalog allows organizations to improve the speed and ease to data access. This allows data science teams to quickly acquire and prep useful data for their projects that was previously hidden in their various data sources."
AI allows faster and easier access to data in processes from various sources
AI allows faster and easier data access AI advantages
FORRESTER
"With Watson Knowledge Catalog, organizations can handle structured and unstructured data in one platform, and they can capture and share models, dashboards, and notebooks. Data scientists save a significant amount of time on finding and preparing data"
AI allows to collect, interpret and process large amounts of structured and unstructured data quickly, consistently and reliably
AI enables large amounts of data to be processed quickly, consistently and reliably AI and KM
FORRESTER
"Watson Knowledge Catalog helps organizations’ data stewards and chief data officers govern and anonymize data and control access and use. Organizations get more transparency into their data and how people use it. Its active policy engine applies layers of governance and control, and sensitive data can be automatically masked, ensuring that data is used correctly. With improved governance of data, organizations can avoid penalties and fines associated with fast changing regulations"
AI has transformed KM by making it more transparent and effective
AI respects data privacy
AI and data protection
153
Interviewee Representative Quotation Concept Concept aggregation Theme
FORRESTER
"Increasing the use of Watson Studio to generate more insights will provide additional productivity and business impact benefits"
AI allows to find more insights, boosting productivity and business benefits
AI improves work processes AI advantages
FORRESTER
"The more data that can be included in Watson Knowledge Catalog, the more value will be delivered through increased productivity, improved security and compliance, and increased access to data to generate insights"
AI allows to find more insights, boosting productivity, security, and business benefits
AI improves work processes and data protection
AI and data protection
MORGAN
"Doctors can’t possibly keep up with all of the data and new studies being created every day, but Watson can scan through millions of records for new data and treatment suggestions. By showing where the information and recommendations are coming from, Watson expands what human doctors can do and provides them with resources to make the best decisions for their patients."
AI allows to have the best information available
AI effectively manages information AI advantages
GUENOLE AND FEINZIG
"HR departments were once primarily administrative functions." ... "Until recently, the primary benefit of technology has been to provide efficiency gains; it allowed us to do the same things we always did, but faster and more cost effectively. For example, previously technology allowed us to recruit people faster over the internet, but now AI lets us recruit the right people faster by assessing skill match for roles, predicting the likelihood of future success, and estimating the expected time to fill any given role."
AI improves HR processes for information analysis, helping and speeding up personnel management systems.
AI promotes efficiency in personnel selection AI advantages
GUENOLE AND FEINZIG
"To solve pressing business challenges AI enables HR organizations to deliver new insights and services at scale without ballooning headcount or cost. Persistent challenges, like having the people resources to deliver on the business strategy and allocating financial resources accordingly, can be addressed through the thoughtful application of AI solutions."
AI allows to find the insights of the texts and then extract the concept
Business strategies: AI applied to HR AI and HR
GUENOLE AND FEINZIG
"To attract and develop new skills. The business world is constantly being disrupted. In order to cope with this disruption, businesses need to respond faster to opportunities, and to work in an agile way to stay ahead of competitors. This means finding an effective way to compete for the skills required to innovate in this new operating environment. AI applications enable HR departments to acquire and develop employee skills in lockstep with shifting market demand."
AI improves HR processes for information analysis, helping and speeding up personnel management systems. AI applied to HR AI and HR
154
Interviewee Representative Quotation Concept Concept aggregation Theme
GUENOLE AND FEINZIG
"To improve the employee experience. People have started to expect something different when they come to work; they want a personalized experience, not a standard one. They want things to be tailored and offered to them in a way that works for them from the start to the end of a process."
AI helps people to improve the way of work
AI improves work processes AI and HR
GUENOLE AND FEINZIG
"To provide strong decision support. The speed of change and rate at which information is being generated means that business decisions today are best made analytically. Because the amount of information that needs to be considered is vast, AI can be used to make sense of it and deliver recommendations. As a result, the information managers and employees require is there just when they need it. AI also provides the opportunity for employee voices to be heard and acted upon in real time"
AI helps humans to make better decisions AI decision support AI advantages
GUENOLE AND FEINZIG
"To use HR budgets as efficiently as possible AI can enable HR to become more efficient with its funding. HR spend can shift to higher value and more complex problem solving, without reducing levels of service for workers who have more routine HR queries. HR savings made in this way can be reinvested in further AI deployment, increasing HR’s ability to solve business challenges, continuously develop strategic skills, create positive work experiences, and provide outstanding decision support for employee"
AI increases profits and revenues, and saves costs
AI applied to HR with better use of human resources AI and HR
HIGH
"IBM Watson is a deep NLP (natural language processing) system. It achieves accuracy by attempting to assess as much context as possible. It gets that context both within the passage of the question and from the knowledge base (called a corpus) that is available to it for finding responses"
AI allows to find the insights of the texts accurately and then extract the concept by assessing the context from the question and the knowledge base AI and accurate KM AI and KM
HIGH
IBM Watson "can tease apart the human language to identify inferences between text passages with human-like accuracy, and at speeds and scale that are far faster and far bigger than any person can do on their own. It can manage a high level of accuracy when it comes to understanding the correct answer to a question"
AI allows to be faster at managing data but with adequate levels of accuracy, enhacing everyday decisions
AI enables more efficient data management AI and KM
HIGH
"Of paramount importance to the operation of Watson is a knowledge corpus. This corpus consists of all kinds of unstructured knowledge, such as text books, guidelines, how-to manuals, FAQs, benefit plans, and news. Watson ingests the corpus, going through the entire body of content to get it into a form that is easier to work with. The ingestion process also curates the content. That is, it focuses on whether the corpus contains appropriate content, sifting out the articles or pages that are out of date, that are irrelevant, or that come from potentially unreliable sources"
AI allows to find information by analyzing large amounts of data and take those related to knowledge and discarding unreliable data
AI effectively manages information, excluding obsolete or incorrect data AI and KM
155
Interviewee Representative Quotation Concept Concept aggregation Theme
HIGH
"When Watson responds to your questions, even answering you correctly, you might realize that you need to ask other, better, and more important questions to help consider your business problem in a whole new way. You start to think in ways that help you to understand the competitive threats and opportunities in your marketplace that never occurred to you before"
AI allows to find more insights, boosting productivity and business benefits
AI used worldwide to gain competitive advantage AI advantages
HIGH
"Recent breakthroughs in inference chaining (determining that this infers that, which infers something else, and so on) are creating deeper insight" […] These types of multilevel inferences can be captured as an inference graph from which we can observe a broad spectrum of downstream considerations. More importantly, convergence in the graph is a powerful way of deriving more significant inferences, such as answers that can reveal deeper insights and hidden consequences"
AI allows to derive more significant inferences (more insightful answers) through an inference engine
AI effectively manages information AI and KM
BANERJEE
"Woodside are harnessing the power of IBM Watson technology and cognitive computing to extract meaningful insights from 30 years of complex engineering data to enable fact-driven decision making on complex projects"
AI allows to collect, interpret and process large amounts of structured and unstructured data quickly, consistently and reliably
AI effectively manages information AI and KM
BANERJEE
"Woodside are realising 10 million AUD savings in employee costs because of faster access to and more intuitive analysis of engineering records. The geoscience team is realising a 75% reduction in time spent by the team reading and searching through data sources"
AI enables large cost savings through faster and easier access to data in processes from various sources
AI leads to cost savings through faster and easier data access AI and HR
BANERJEE
"Working with Watson, Woodside Energy built a customized tool that allowed its employees to find detailed answers to highly specific questions, even on remote oil and gas facilities. Watson ingested the equivalent of 38,000 Woodside documents, this would take a human over five years to read"
AI allows to find information by analyzing large amounts of data and take those related to knowledge
AI effectively manages information AI and KM
IBM Corporation (2019)
"Firms are embracing more data sources on the cloud, combining it with existing data on premises, and applying analytics and AI on the Cloud to drive new insights"
AI on the Cloud generate new insights
AI and new insights generation AI - on Cloud
IBM Corporation (2012)
"IBM’s vision is to define, create and lead markets for this new class of cognitive system by: 1. Addressing meaningful industry and societal challenges, where conventional approaches don’t work. 2. Developing a cognitive class of solutions built on a secure, scalable modular framework. 3. Delivering demonstrable, quantifiable value as defined by the client."
AI as a centralized platform of integrated services to manage all business processes
AI improves work processes AI- Cognitive
IBM Corporation (2012)
“Watson solutions are best suited to data-intensive industries and issues that: • Require the analysis of a high volumes of both structured and unstructured data • Benefit from the speed and accuracy of a response to a question or input provided • Desire to systematically learn with every outcome or action taken, getting smarter with interaction and new evidence • Have critical questions that require confidence weighted recommendations and supporting evidence”
AI as a centralized platform of integrated services to manage all business processes
AI improves work processes AI- Cognitive
