THE STRATEGY & INNOVATION PLAYBOOK: THOUGHT LEADERSHIP FROM MIT SLOAN EXECUTIVE EDUCATION

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TABLE OF CONTENTS

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5 Warning Signs That it ‘s Time to Change Your Digital Business Strategy

What Crisis Teaches Us About Innovation

Integrating AI into Customer Experience

Nail it. Scale it. Sail it.

Innovating with Algorithms

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Strategy and Innovation: Now More Than Ever

INTRODUCTION

To remain competitive over time, a company must be able to move quickly in response to major changes in society, technology, competition, regulation, labor markets, and more. If 2020 taught us anything, it’s that our ability to adapt, pivot, and innovate our way through disruption is essential to business survival. Prioritizing innovation today is the key to unlocking postcrisis growth.

MIT Sloan is world renowned for the development and advancement of strategic and innovative management methods and practices. The executive education courses in our Strategy and Innovation track introduces these

breakthrough concepts to help executives deliver on great ideas and successfully drive innovation throughout their organization.

The articles in this Playbook are representative of just a few of the courses we offer designed to help executives build resilient business strategies and create cultures of innovation. We hope you find this content useful, and we invite you to learn more about our courses, Executive Certificates, free webinars, and more on our website.

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5 Warning Signs That it‘s Time to Change Your Digital Business Strategy

Insights from Stephanie Woerner

When a company has embarked on a digital transformation program, it can sometimes be hard to gauge the success of the transition. Individual digital project metrics, for example, may not provide a complete picture of progress. Stephanie Woerner, a Research Scientist at the MIT Sloan Center for Information Systems Research, recently spoke to The Enterprisers’ Project and shared five warning signs that it’s time to reorient a digital transformation for greater momentum.

1. Employees don’t agree about the end goal

For digital transformation to work, everyone in the organization—from the bottom to the top—needs to align on its desired outcome. As Woerner says, “Successful digital transformations are about organizational

change, and changing an organization is hard enough without trying to do one where a good chunk of the organization doesn’t agree on the end goal, like the targeted business model or the process. The CEO must communicate the goal of the transformation internally and externally, and work with all levels of the enterprise.”

2. Leaders want short-term results instead of long-term ones

It’s hard not to look for immediate ROI when you’re making a big change as a company, but Woerner says leaders must resist this impulse and instead look further down the road: “[Executives may be] giving in to demands to fund projects that might benefit a business unit but don’t support the transformation,” Woerner says. “This takes discipline and the willingness to forgo short-term gains for long-term benefits for the entire enterprise.”

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Stephanie Woerner is a Research Scientist at the MIT Sloan Center for Information Systems Research (CISR). She studies how companies manage organizational change caused by the digitization of the economy. Her research centers on enterprise digitization and the associated governance and strategy implications. Woerner teaches in the MIT Sloan Executive Education course, Digital Strategies for Transforming Your Business. Drawing on her cutting-edge research, this course provides a powerful framework for transforming businesses on two dimensions: knowing customers better and optimizing business design.

3. There isn’t a specific roadmap

To reach a new destination, you typically need directions. The same is true for digital transformation. “A roadmap, and perhaps a dashboard to track progress, accomplishes a few things,” says Woerner. “Employees across the enterprise have transparency to what is going on, they know what to expect, and they are able to see their role in the transformation. And a clear roadmap makes it much more difficult to highjack or change the process.”

4. There are no measurable metrics

This builds off of warning sign #1: everyone needs to agree about the end goal, then they can establish metrics to measure that goal. Otherwise, how will they know if they’ve met it? “Metrics focus attention, and if your enterprise has thought seriously about the desired outcomes and selected metrics that measure those outcomes, metrics can let you know if you are on the right track,” Woerner says. “Metrics also support enterprise learning. Not hitting a metric is a signal that things are not on the right track and that the enterprise should take some time and figure out why the transformation doesn’t seem to be working.”

5. Digitization isn’t influencing work culture

If your digital transformation hasn’t also transformed the way your business works, it likely hasn’t gone far enough. “If the enterprise culture is not changing, that’s a problem. Digital transformations are about making changes to increase operational efficiency, customer delight, and innovation,” says Woerner. “Habits, norms, and work practices all reflect the culture, and you should be seeing changes in the ways that people work and the way that they understand the enterprise and what it is trying to accomplish.”

By looking out for these warning signs on the path to digital transformation, you can course-correct before it’s too late and ensure success.

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What Crisis Teaches Us About Innovation

Insights from Fiona Murray and Elsbeth Johnson

The spread of COVID-19 sparked a crisis that spurred organizations to innovate faster than ever before. But why is innovation more possible during a crisis? And can innovation continue once the crisis has passed? In their MIT Sloan Management Review article, “What Crisis Teaches us About Innovation,” Fiona Murray and Elsbeth Johnson say yes, it can.

A crisis provides a sudden and real sense of urgency.

The COVID-19 pandemic invoked three cognitive concepts that worked together for innovation. First, as a society we’re apt to succumb to normalcy bias -- that is, until we see evidence that our situation isn’t normal, we can be pretty blasé. But according to prospect theory, we’re more likely to react to

something negative than something positive. The coronavirus was certainly a negative stimulus. Then, the images of death and healthcare shortages provoked our availability bias, where our tendency to respond outstrips the perceived danger. Leaders can recreate doomsday scenarios using actors and scripts in company meetings to counteract natural inertia.

This urgency enables organizations to drop all other priorities and focus on a single challenge, reallocating resources as needed.

Under normal circumstances, organizations avoid spending resources on potential products, and instead prioritize the development of products and services that can give them short-term profits. That’s because leaders are better

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at prioritizing activities than they are long-term projects. The spread of COVID-19 gave organizations a single-minded focus, taking away the decision-making process about priorities. Leaders must stay focused on long-term goals and leave activity prioritization to those closer to the action and instead prioritize the development of products and services that can give them short-term profits. That’s because leaders are better at prioritizing activities than they are long-term projects. The spread of COVID-19 gave organizations a single-minded focus, taking away the decision-making process about priorities. Leaders must stay focused on long-term goals and leave activity prioritization to those closer to the action.

With this singular focus and reallocated resources, it’s now everybody’s job to come together to solve the problem, bringing a new diversity of viewpoints and perspectives.

Empirical research shows that groups of people with differing backgrounds and skills tend to be better at solving problems than more homogenous groups. But because there is inherent friction in heterogeneous groups, they can be perceived as less efficient. However, these groups can be highly efficient if they’re given the right constraints, including clear parameters and specifications. Leaders need to spend time defining specifications for deliverables to help these disparate teams function at optimal levels.

This urgency and singular focus legitimizes what would otherwise constitute “waste,” allowing for more experimentation and learning.

In a crisis, leaders are less concerned about the fact that it might take a number of failures before reaching the solution. Most management methods are designed to minimize activities that can potentially create value, but during a crisis leaders are naturally more willing to allocate resources to experimentation, also known as “slack.” But efficiency isn’t the only goal in an organization. Leaders need to develop resilience, effectiveness, and innovation – all of which require investing in more risk than organizations are comfortable with. Ultimately, however, this investment leads to organizations being better prepared for future challenges.

Because the crisis is only temporary, the organization can commit to a highly intense effort over a short period of time.

We all know that people can put forth a Herculean effort for a shorter period of time. Like any other crisis, COVID-19 is time-bound. Deadlines help amplify efforts in a way nothing else can. This means teams can’t remain in crisis mode indefinitely. Sprints, which are now common in Agile methodology, capitalize on this idea of breaking down larger efforts into smaller, more intense tasks to work more effectively over a longer period of time. But it’s not just that crises are temporary. Leaders tend to stay engaged with the problem during a crisis until it’s solved. When leaders are engaged, they can provide energy and excitement that creates momentum.

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The Post-Crisis Environment

By understanding the conditions that crises foster, leadership can learn how to create proxies for emergencies so that organizations can continue to innovate – well into the next era.

Fiona Murray is a professor of entrepreneurship and the associate dean of innovation and inclusion at the MIT Sloan School of Management, where she codirects the MIT Innovation Initiative. She teaches in the MIT Sloan Executive Education courses Algorithmic Business Thinking, Corporate Innovation, and Innovation Ecosystems.

Elsbeth Johnson is a senior lecturer at the MIT Sloan School of Management and a visiting fellow at the London School of Economics. She teaches in the MIT Sloan Executive Education course Leading Change in Complex Organizations.

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Integrating AI into Customer Experience

Insights from Renée Gosline

Every experience has the potential to make or break a customer relationship. Unfortunately, creating consistently delightful customer experiences (CX) is also harder than ever for businesses in today’s digital ecosystem. COVID-19 has further complicated things by reducing the CX workforce and limiting in-person services, driving even more operations to digital networks.

Evolving customer expectations and communication channels has become exceptionally difficult for a human workforce to manage on their own; introducing technology into the process is the smartest way to create and execute a successful customer experience strategy for modern businesses. That said, the relationship between technology and human service has to be symbiotic; it has to feel effortless for the customer to really make a difference in their satisfaction, trust and loyalty

over time. Understanding when and why to use algorithms, automation and machine learning in CX is critical for developing a successful strategy.

“The development of bots and algorithms to aid behavior is outpacing our understanding of who and when they help most,” says MIT Sloan professor Renée Gosline. “We need to think about the increasing outsourcing of decisions — who does it, when, and what the implications are when we do it.”

Understanding the Why, When and How of Using AI in CX

The key to building a really effective AI-enhanced CX strategy is knowing when and how it best serves to enhance or elevate the human aspects of CX. Gosline has done

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extensive research on human and AI interactions in customer experience to understand the best strategies for successful implementation.

Gosline’s study found that the average American makes about 35,000 choices per day; and most of those choices are predicated on some kind of goal conflict. In many ways AI can help alleviate goal conflict and help people feel like they are making better, more informed decisions. And this is where Behavioral Science comes in for understanding what causes acceptance or aversion to inviting technology into this processing.

“When we think about it, this processing we do is done quickly and we use mental shortcuts called heuristics and they’re wonderful because they help us navigate things quickly,” Gosline says. “But we know that our brains are prone to cognitive bias and so here at the IDE we’re trying to understand, can AI help, and under what circumstances can it? And that’s really important because human beings are increasingly outsourcing cognitive tasks to artificial systems, where our choices and our thoughts are not merely limited to what we think in our brains, but what the brain in our pocket tells us what we might want to think as well.”

The Fine Line Between Acceptance and Aversion to AI & Machine Learning

One of the biggest factors hindering progress with AI in CX is the long-standing discomfort people have with it. In her study, Gosline found that 62% of the population still have a bias against algorithm-based programming. From data privacy to job security, people still struggle with the undeniable value and perceived threat that AI and machine learning present in their day-to-day lives.

Privacy is an obvious double-edged sword in this scenario. According to research from JWT, 89% of consumers think companies are intentionally vague about the ins and outs of the ‘data for benefit’ exchange. However, even with that on their minds, many people still won’t give up the benefits that come with data sharing, particularly personalization and (in the COVID times) enhanced safety precautions.

So, what are businesses to do in this situation? Well, to build strong relationships with customers, businesses have to show they care about and understand every individual’s needs, and now, anticipate what they could need next even if they themselves don’t know yet. Part of doing this well means offering as much transparency as possible on how and why customer data is used in these situations. They also have to be open and honest about how AI is enhancing, and not replacing, their workforce. Job loss is a huge concern right along aside data privacy and a proactive approach is the best offensive defense against negative reactions.

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It’s still not completely clear to behavior scientists like Gosline on predicting when or why people choose AI versus human support. What is clear is that you can never go wrong by making sure the customer always feels in control of the experience and empowered to choose their adventure; AI is appreciated when it helps people do better and make the most informed decisions — it can’t make the decisions for them though.

Tips for Integrating AI into Customer Experience

1. Automate simple, repetitive, low-value tasks so CX teams have more time to spend with customers and doing higher value, strategic tasks.

2. Use chatbots online to handle basic customer requests and connect customers who want or need human assistance to the appropriate representative quickly and conveniently.3. Use CRM data to segment customers by product, service, etc. and keep customer records up-to- date and accurate.4. Personalize as much as possible in every experience, especially with bots and automated communications. Progressive profiling through website forms and customer surveys is another great way to gather information on users over time and make them feel taken care of — without creeping them out. 5. Once you have a good foundation built with AI-enhanced customer experience, explore things like predictive modeling and natural language processing (NLP) systems to improve CX productivity and profitability over time.

Renée Richardson Gosline is a Senior Lecturer in the Management Science group at the MIT Sloan School of Management and a Principal Research Scientist at MIT’s Initiative on The Digital Economy. She is a 2020 honoree on the Thinkers50 Radar List of thinkers who are “putting a dent in the universe,” and has been named one of the World’s Top 40 Professors under 40 by Poets and Quants. Her newest MIT Sloan Executive Education course is Breakthrough Customer Experience (CX) Strategy. This new customer experience course uniquely applies a behavioral science foundation to help you develop breakthrough digital customer experience CX for your brand that resonates in this highly digitized world.

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Nail it. Scale it. Sail it.

Insights from Charles Fine and Loredana Padurean

Fresh out of a day-long session in a modern, open-concept classroom in Kendall Square, Kate Dillon was upbeat, motivated and clearly energized to return to her work in Australia and apply her fresh knowledge and thinking. “I’m a nailer in a sailing environment assisting with process efficiency,” she declared confidently, going on to further explain that “because I’m a nailer in sailing environment, I look for as many tools as I can to persuade sailors to get aboard.” Kate does not work in shipping, travel, overseas commerce or any type of nautical industry. In fact, she works with business process optimization in a legal environment. But she has enthusiastically adopted a new vocabulary in her week at MIT Sloan, one that helps her

articulate her skill sets and strengths, and why they work effectively to serve her firm’s present needs. ‘Nailing, Scaling, Sailing’ is the brainchild of MIT professors Charles Fine and Loredana Padurean, who have been collaborating for the past five years to develop a brand-new framework for understanding and defining the complex role of entrepreneurial operations. The NSS framework, as it is called for short, is a central component of their jointly-taught course, Driving Strategic Innovation, as well as the subject of an upcoming book to be published by MIT Press. The concepts of ‘Nailing, Scaling, Sailing’ applies to both the entrepreneurial life cycle of companies and organizations as well as the

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attributes of the people best-suited to support organizations at each stage. At a high level, the framework breaks down into three distinct stages, as described by Padurean: Nail ItThe earliest stage is all about figuring out and successfully prototyping a value proposition that works simultaneously for all the members in your ‘value chain’ (customers, employees, suppliers, distributors, investors, etc.)

Scale ItThe ‘scaling’ stage comes once a company has proven some key aspects (e.g. technology, customers, pricing) of its value proposition and now must grow in parallel to its market alongside its production and delivery capabilities.

Sail ItThe third stage, ‘sailing,’ typically comes much later, after a company has realized a significant fraction of the growth opportunity of its value proposition… At this point, the focus may be more on the sustainability of the business and continuous improvement to navigate the market. The NSS model is not only relevant to companies or organizations. It also applies on a personal or departmental level. In this way, Padurean says, it also “functions as a self-diagnosis profiling tool” for individuals or teams. That additional layer helps organizations not only identify their present growth stage, but also determine the best type of talent they need now and in the future in order to continue to evolve. She offered this example: “By applying the NSS framework, someone may say, ‘Oh, I just realized my company is in scaling model. That’s why so many nailers are leaving. We thought we were losing our best people but maybe those aren’t the people we need now that we are scaling up.’” Hiring someone for an early-stage startup who worked most of her life at a larger organization may not make the most strategic sense. But why? The NSS framework provides a foundation for identifying which skills and personalities thrive best at various stages without drawing sharp, black-and-white boundaries. Instead, Fine emphasizes that it’s “a model for understanding the evolution of ideas in an organization.” This emphasis on an evolutionary process is only appropriate for any framework that applies to the concept of innovation, which, as Padurean points out, “is not just a new product or a new process. It’s a continuous state of mind.” “When we talk about innovation in a strategic environment, we talk about the big companies: Apple, Facebook and Google,” Padurean says. “But then we forget that at some point, they were small startups and we forget that because they had an innovative service or product, they became big companies.”

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That’s a key reason why Padurean integrates so many key examples from early-stage startups, the “nailers.” “They are a much more fertile ground for innovation. How can a very large company, which is represented by many people taking this course, learn from them?” Driving Strategic Innovation has been taught 33 times over the course of 16 years and from one session to the next, it has never been the same experience or curriculum. True to its title, the faculty are constantly innovating the course and engaging in experimentation with new material and guest professors. Fine feels strongly that the NSS model – as well as the larger DSI curriculum – is not limited to a business environment, but rather as a rejuvenation tool for any organization. A lot of people came to me and said, “This really changes the way I think about my organization, my team and my own role because now I understand the pains and the challenges that come at each stage.”

Charles Fine is the Chrysler Leaders for Global Operations Professor of Management at the MIT Sloan School of Management. SInce 2015 he has also served as CEO, President, and Dean of the Asia School of Business (in Kuala Lumpur), established in collaboration with MIT Sloan.

Loredana Padurean is the Associate Dean and Faculty Director for Action Learning at the Asia School of Business, established in collaboration with MIT Sloan in Kuala Lumpur, Malaysia.

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Innovating with Algorithms

Insights from Paul McDonagh-Smith

It is well understood that algorithms have allowed organizations to scale in ways that weren’t possible even five or 10 years ago. From Amazon’s recommendation engine to dynamic pricing for airlines to improving fleet productivity, algorithms offer big pay offs for organizations seeking to rapidly scale their products and services.

In light of the technologies powering our progress, enterprises need to consistently upskill their workforce and rethink how to approach organizational strategy, leadership, and management. According to Paul McDonagh-Smith, Senior Lecturer and Digital Capability Leader at MIT Sloan Executive Education, companies that want to keep pace with innovation need to understand what’s behind

the technology and translate that into practical opportunities for sustainable growth.

McDonagh-Smith specializes in translating and converting computer and data science into clear and measurable business value for teams and organizations. He is behind the new, self-paced online course Algorithmic Business Thinking: Hacking Code to Create Value, which he co-teaches with a team of renowned MIT Sloan faculty. In this course, McDonagh-Smith and his colleagues introduce the concept of algorithmic business thinking—a framework for understanding the key principles of algorithms, code, and data and a methodology for applying those principles across role and departments.

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“Algorithmic business thinking can allow us to upgrade a number of business activities and functions and optimize them for the digital economy,” explains McDonagh-Smith. “When you understand the building blocks of these technologies, you can apply them not only to products and service but to business functions like finance, sales, or marketing.”

For example, firms are increasingly turning to algorithms to help them make hiring decisions. MIT researchers recently developed a dynamic learning model that yields a significantly more diverse pool of high quality applicants than existing machine learning tools. You can read more about how exploration-based algorithms can improve hiring quality and diversity in this article.

Algorithmic Business Thinking: A Double Helix Structure for the Digital Economy

In a recent webinar with IEDP, McDonagh-Smith digs deeper into the human qualities that are needed to unlock the increasingly complex technology that businesses are now having the opportunity to leverage. You can watch this webinar below.

Paul McDonagh-Smith is a Senior Lecturer in Information Technology at the MIT Sloan School of Management. He is the Faculty Director and teaches in the Executive Education courses Digital Learning Strategy and Algorithmic Business Thinking: Hacking Code to Create Value. He is also the MIT Sloan Office of Executive Education Digital Capability Leader.

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To learn more about s t rategy and innovat ion courses at MIT Sloan Execut ive Educat ion, as wel l as our Execut ive Cer t i f icate in Strategy

and Innovat ion, vis i t : execut ive.mi t .edu.