Making intelligence work for youA new way of using artificial intelligence to its full potential

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There is a lot of buzz around artificial intelligence (AI), a lot of traction and sky-high expectations. AI’s capabilities have grown in leaps and bounds during the past few years, and interest follows. But when you look at large corporations, the ability to translate those capabilities and potential into sizeable, impactful business outcomes hasn’t happened yet. That’s why it’s still the biggest challenge for many of them.

Intelligence delivered is all about business outcomes. It’s about how to get better business outcomes based on a shrewd reading of the market, as well as a keen situational awareness that drives proactive action. There are many different AI-related initiatives out there, and just as many misconceptions. The first one is that everyone thinks about algorithms, about solving complex problems with machine learning and deep learning techniques, but there’s a catch. One very important factor about AI is that it doesn’t mean much unless you have well-managed data. You need access to operational data, data that’s available and ready to train AI.

AI is also currently solving discrete problems, disjointed from one another to a certain extent, like processing both images with computer vision and speech with natural language processing in separate instances. Therefore, in order to generate business value, multiple elements of those AI solutions need to be joined together. The data is just one piece of the puzzle. AI is currently focused on solving a vertical slice of the problem, so to operationalise it, we need multiple, integrated AI capabilities. Unless AI is operationalised in this way, generating large-scale intelligence out of the data won’t be possible.

Putting AI to work

If you want to operationalise AI, it should never stay as an isolated proof of concept or just gather dust somewhere in a lab. It should be properly integrated with other business solutions, and with the right data feeding into it. That’s the first part of the challenge. But even with operationalised AI, once you transform data into intelligence, unless that intelligence is actionable and action is taken, it won’t necessarily translate into business outcomes. Thus, corporations need to take intelligence-based actions by embedding AI into the business processes. This means integrating AI back to the business processes and applications, such as ERP environments.

Many corporations use SAP or other types of business applications, CRM applications like Salesforce, HR applications like WorkDay or legacy applications — some going all the way down to the mainframe in certain financial institutions. However, integrating these AI solutions back into those applications doesn’t have to be very complicated. All these applications are API enabled, and by simply using rest APIs, the technical integration can be made to work. And the intelligence you’re generating out of the data using AI, machine learning and deep learning capabilities can then be embedded into the business process, which means intelligence delivered at the right time, to the right target.

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From a technical standpoint, integration is straightforward. The challenge lies in end-to-end management of the operationalised and integrated AI — if improperly managed, a “Eureka!” moment can turn into something much less desirable. Any AI algorithm, whether created by a data scientist, a research institution or a boutique consulting company, whose purpose is to generate intelligence out of the data, suddenly becomes a critical part of your business-critical application flow. Consider the following: You hire a brilliant data scientist, PhD and all, to rework how you do risk assessment from the ground up. This person has embedded her AI algorithm into your policy creation process … and then, she goes on annual leave or has a personal matter to attend to. Suddenly, the algorithm stops working. No one knows how to fix it. The company cannot publish or create any new policies, and the entire process grinds to a halt. All because one person was unavailable.

Therefore, end-to-end management is crucial — once an AI solution is deployed and the data flows link to it as part of business-critical business processes, it needs to be managed as well. AI without data doesn’t mean much, making managed data pipelines the first critical element. Managing those data pipelines for these kinds of business-critical applications or business capabilities, but also for other types of business processes as well, requires a new form of managing data. It’s called DataOps, and the marketplace is buzzing about it right now. An automated, process-oriented methodology, DataOps is used by analytic and data teams to improve the quality and reduce the cycle time of data analytics. With DataOps, we implement the learnings we’ve discovered via agile working and DevOps concepts, and we integrate those learnings not only into data management information governance but also into the use of data in business outcome generation.

Then there’s the managed life cycle of AI algorithms. A popular AI-related myth in the industry is that once you solve a problem and have an algorithm, it’s solved forever. This is never the case. The conditions you operate in are in constant change, not to mention that other players are always adapting their response to it, which is why your algorithms need to evolve as well. With that in mind, we need to manage the AI, deep learning and machine learning models’ life cycles on a continuous basis, so that they continue to evolve as the market — and the way you respond to it — changes. Of course, being able to run those algorithms on an ongoing basis requires different types of infrastructure, so cloud becomes a very important element.

And not just cloud, but a multi-cloud setup — a combination of on-premises environments with public cloud deployments. Staying agile and innovating rapidly requires easy access to the latest technologies. Together with technology partners such as AWS, Microsoft, HPE and Dell, DXC Technology is able to provide the best solution to ensure impactful business outcomes for our clients.

The benefits of a hybrid IT environment

In today’s digital reality, you need strategies that enable traditional IT to work in harmony with analytic-intense data streams located on-premises, in the cloud and at the edge. Pressure to move information processing close to the data — due to high data volumes, data variety and the need to minimise analytic latency — is forcing enterprises into public clouds at a remarkable rate. Even enterprise transactional systems are beginning to migrate and transform for agility and flexibility. As the industry undergoes this dramatic transition, hybrid IT operations that span on-premises, data centre, cloud and edge become essential.

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Hybrid IT embraces integrated teams that have development, deployment and operational experience and blend business, IT and partner expertise. These teams focus on business outcomes for accelerated performance while driving learning at scale. In a nutshell, hybrid IT takes modern approaches, including DevOps, automation, community sourcing, machine learning and AI, and pulls the legacy environment forward to provide more business value.

The core promise of hybrid IT is that it will improve efficiencies and do so at a pace and scale the business requires. Based on DXC’s experience with clients, hybrid IT can significantly speed an organisation’s growth while simultaneously reducing IT capital costs, streamline the number of business applications in use and ultimately provide a dramatic drop in overall operational costs as well.

Why AI needs the cloud

Although the power of machine learning grows as more data becomes available, and we are generating new data at an astounding rate, we are far from putting this data to good use. By some analyst estimates, we may be capturing only 20 to 30 per cent of the value of our manufacturing data, 10 to 20 per cent of the value of public sector data (European Union), and 10 to 20 per cent of the value of our healthcare data (United States).

In every industry, the barrier to capturing the full value of data is nearly the same. Data is locked away in silos. Getting a machine to spot meaningful insights typically takes a lot of processing power and data storage capacity. And the enterprise has yet to be shown a convincing demonstration of the data’s potential.

These are all challenges that can be met with the proper deployment of cloud technology. The cloud is the fastest and least expensive way to integrate data. The cloud brings sophisticated algorithms, fast computing platforms and massive storage capacity within reach. It lowers the barrier to adoption and makes it easier for enterprises to get their feet wet with inexpensive experiments. Cloud services make it easier to build compelling applications quickly by delivering in small, meaningful chunks.

The cloud is also what will enable AI to become a ubiquitous and essential part of business operations. By bringing data together in the cloud, which is emerging as the future platform for the intelligent enterprise, algorithms trained by faster, smarter data will play a key role in enterprise transformation.

Getting started

Your first step is to connect with the business to establish clear lines of communication and to set common goals. Within the IT organisation, CIOs must set policies that identify the need to prepare for hybrid IT as a top priority.

Then we recommend assessing the company’s applications and IT estate and creating a roadmap to guide the journey to optimal hybrid IT. This roadmap should compare current and future run costs to create a business case for change; assess application and workload placement and data locality options; recommend application transformation requirements; consider IT policy and operating model factors; and ultimately produce clear, data-based recommendations with a proposed execution plan to enable hybrid IT transformation to happen.

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Getting there will require transforming and migrating applications, integrating and orchestrating environments, automating and monitoring business services, enabling digital processes, and integrating data and security (see Figure 1).

Figure 1. How to use the cloud to build a smarter enterprise

Tactics and techniques

As companies embrace hybrid IT, they must address both technology and the human side of change. There are several key actions to take:

• Staff and train differently. As applications move from traditional platforms to the cloud, current IT staff must be trained and reskilled. Companies should recruit developers adept in agile methodologies and support a culture of learning at scale. Siloes should be broken down, and the workforce should become more integrated, multifunctional, flexible and agile.

• Overhaul change management. The existing governance processes, gates and approval procedures designed for traditional legacy IT environments are no longer appropriate in a cloud environment. Companies should revamp their change management systems to allow changes to happen quickly, and use automated workflows to reduce manual intervention.

• Integrate cloud operations. As organisations move workloads to the cloud, the IT operations function should adapt to manage both on-premises and cloud- based applications. This new model, called CloudOps, can provide continuous integrated operations in a multi-cloud environment to enable rapid response to events, incidents and requests. Adding DevOps to the mix then utilises automation, integration and organisational change to enable more frequent enhancements that result in higher-quality software.

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• Automate support. To the extent possible, IT support functions need to be automated. For example, the traditional trouble ticket system can be manually intensive and inefficient. Automation can improve service and free up IT personnel for higher-level activities. Longer term, companies will be able to deploy machine learning and AI to take log data from cloud-based systems and automatically take actions to resolve or even prevent incidents. The idea is to learn once, fix with code and share learnings to improve code over time and scale knowledge.

• Manage “shadow IT.” Business units are often acquiring the cloud services they need because IT moves too slowly. At some point, those services must be integrated back into the traditional IT environment for operational and security reasons through a services governance model that encompasses hybrid IT elements. In addition, it’s important to have a handle on what the enterprise is spending on IT services. The only way to accomplish this is to adopt hybrid IT and demonstrate to business units that IT can support the pace and scale the business requires.

The goal of hybrid IT is a well-managed, well-integrated environment that consists of rock-solid traditional IT environments for legacy applications, as well as private and public clouds for deploying new features and functionality at a dramatically improved pace and scale. If you partner with the business and guide teams to integrate and optimise workloads across multiple platforms, you’ll see the path to digital transformation become much shorter.

Protecting your investment

When you have such critical data pipelines and algorithms in place, they become juicy targets for hackers and other malign forces. Therefore, we need to ensure those models and algorithms, as well as the data pipelines, are well-managed from a security perspective. This is referred to as managed security.

The first step was to move away from an isolated AI proof of concept or an algorithm in a lab environment. Then we placed multiple pieces of AI and other non-AI applications together, and operationalised and industrialised them using the right data flows and the right mechanisms.

Secondly, we took the intelligence generated from the data — preferably actionable — and embedded it into the business processes by integrating the AI solutions into the back-end applications, whatever those back-end applications were: ERP, CRM, HR, client-facing workflows, etc. Then, we needed to ensure that those integrated and operationalised AI solutions created value instead of problems in the business process. We used the managed AI framework to ensure those models and life cycles are managed, that the data pipelines are managed with the right level of rigor and at the right price scale and that you have an infrastructure element using multi-cloud by default at the infrastructure level. This, in turn, ensures the cybersecurity of all those flows. We have taken the data, generated actionable intelligence out of it and embedded that intelligence into the business process to ensure that it is actioned. We have ensured that the actioned intelligence doesn’t create disruptions to the business but generates business value instead.

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There are only a few companies that have been able to do this for a select set of business processes, but to get impactful business outcomes, and to be able to monetise those capabilities, this needs to be done at scale. This is more of a change management than a technical challenge — a different way of working where you need the business units and the functions working together, creating a community of practices. Everybody in the corporation should take advantage of those capabilities to solve business problems and address business opportunities, so that we can scale and create impactful outcomes.

This is where we feel the market currently stands — mostly focused on isolated AI instances in the form of proofs of concept. In contrast, DXC has the capabilities to not only operationalise and integrate but also manage AI, as well as drive change management, all with the goal of monetising AI to create impactful business outcomes. Therefore, we do not view niche boutiques, research institutions or think tanks as competition or irrelevant — quite the contrary. We follow their work closely and constantly evaluate it to see if it can scale in a way that would be beneficial to our clients’ business.

And regarding our clients’ business, the thing that matters most to us is always the big idea. We’re always on the hunt for exceptional ideas, since we’re one of the very few companies with the capabilities to realise them at scale. Here’s one such example.

The BMW story

When production development for autonomous driving got underway at the BMW Group, one thing was certain: The tremendous challenges involved in the development of a secure platform for highly and fully automated driving could only be overcome by teaming up with leading technology partners in the various disciplines.

In the case of BMW’s D3 platform, this partner is DXC Technology. The core component of DXC’s work is to set up and run the data centre and to develop applications with the objective of supporting the autonomous driving development process. The digital solutions from DXC put the BMW development teams in a position to collect, store and manage the data from vehicle sensors — and make that data available for the requisite AI training — in a matter of seconds.

The DXC solution was developed in an open-source environment and is available on premises and in a hybrid multi-cloud environment, allowing workloads to be shifted easily. This paves the way for agile cooperation between engineers, regardless of their location.

Using a single platform for data storage, processing and AI training lowers the hardware and software requirements, thereby reducing costs and complexity. Data can be gathered globally but monitored centrally, which maximises efficiency while simultaneously minimising costs.

We call this solution DXC Robotic Drive.

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Putting the future in perspective

DXC Robotic Drive accesses and collects huge amounts of data — sensor data coming from test drives — and then rapidly ingests and cleans that data, making it available for agile experiments. These experiments go on to become AI solutions, whether from the clients’ in-house teams, from DXC or from other partners. Those solutions then generate insights, which become embedded into the business processes. Executing the business processes generates more data, restarting the entire cycle.

Even though the data and processes in this case are in an automotive context, the capability building blocks in DXC Robotic Drive are implementable in any industry. When learning about Robotic Drive, a CIO of a global pharmaceutical company commented: “ If we can do this for the connected car, there’s nothing stopping you from implementing these solutions for the connected patient, or connected customer, or connected investor, or whatever your target is.”

And that, right there, is the power of Intelligence Delivered.

Learn more at www.dxc.technology

About DXC TechnologyDXC Technology, the world’s leading independent, end-to-end IT services company, manages and modernizes mission-critical systems, integrating them with new digital solutions to produce better business outcomes. The company’s global reach and talent, innovation platforms, technology independence and extensive partner network enable more than 6,000 private- and public-sector clients in 70 countries to thrive on change. For more information, visit www.dxc.technology.

© 2019 DXC Technology Company. All rights reserved. DG_2379a-20. October 2019

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