Three Steps to Prepare for Data and AI Leadership

Clarity about the most critical business problems can shield you from pursuing data and AI for their wow factor

Published: Tuesday, June 1, 2021 - 11:02

With big data and artificial intelligence (AI) transforming business, it’s almost certain that every executive will need to leverage these technologies at some point to advance their organization—and their career. However, doing so carries a heavy intimidation factor for most leaders, and this is often exacerbated by skill-heavy job descriptions for leadership roles related to data, analytics, or AI. However many of these descriptions misunderstand what’s required to drive successful business outcomes using data and AI.

Although analytical thinking is certainly important, many traditional leadership skills are equally essential when undertaking AI/big data for the first time. It’s also critical to be comfortable with ambiguity, have the capacity to drive consensus among disparate players, and understand the levers of value to prioritize accordingly. Some of the most effective leaders in data and AI are those who think commercially while applying expertise. Moreover, today’s data-driven business leaders must be politicians and communicators, able to harness the potential of data and AI to drive revenue, efficiency gains, and innovation, while exerting influence and explaining the value they create.

Of course, success also requires knowledge of existing and emerging data and AI technologies—though not necessarily sophisticated technical expertise all around. You do need sufficient technical knowledge to grasp the likely value, costs, and risks associated with implementation; track relevant trends; and inspire data science and engineering teams. However, it’s neither necessary nor possible to know it all, so don’t expect to.

While there is a lot to do to prepare yourself and your organization to leverage these technologies, we outline three key steps to apply to succeed as big data and AI leaders. Our recommendations are drawn from our work and our recent digital@INSEAD webinar—we invite you to watch for more detail.

Know your ‘why’

Before starting, it’s important to gauge the CEO and executive board’s vision for data and AI. Their commitment is key, but why do they want to build a data capability? Because they read about it? Wrong answer. Because consultants told them to? Wrong again. Because their competitors are doing it? Still not right.

Being able to answer the big why question is crucial because it gets to the heart of a company’s vision and frames investments in data and AI as solutions to deliver that vision, rather than as standalone capabilities. At Vodafone, for example, Katia Walsh, as the group’s first global chief data and AI officer, sought to articulate the vision, starting with a simple question: Why is it that people can’t live for two minutes without their phone, yet easily switch providers—quitting the very same company that made that phone come to life? The ability to arrive at that clear why allowed her team and their partners to narrow their focus amongst all the possible opportunities with data to those that would allow them to make the company as indispensable to its customers’ lives as the devices it connected for them.

It’s also important to gauge the quality of your organization’s data. Ask questions such as: Do we have the data necessary to answer the questions that are pertinent to our vision? Is our data consumable and organized? If such questions are not addressed early, you could end up doing data clean-up work for the first three years of a mandate, without delivering much value during that time. You simply can’t create value without a single source of accurate and high-quality information.

Build your team

When organizations try to turn themselves into a data and AI-ready enterprise, they often focus on hiring the brightest technical minds. Of course, technical talent is valuable, but data effectiveness can be limited by the motivation and capacity of stakeholders and partners to engage. It’s also important—and at times can be more effective, not to mention cost-efficient—to invest in training people across the organization.

For example, you could identify and train people in sales, marketing, and operations on how to identify opportunities and ask questions that help solve business problems through data and AI, and get them excited about the power of these technologies. This builds internal support and can be more successful than hiring legions of top technical talent who, in the absence of engaged partners, will struggle to create business value.

In every organization there is a point at which it’s critical to increase the data literacy of the enterprise. Companies are starting to realize this and invest heavily in employee upskilling. Furthermore, the data and AI teams that are best at driving value contain diverse profiles extending beyond analytics and engineering to anything from quantitative Ph.D. degrees to philosophy graduates, lawyers, or self-taught experts without university degrees. A quality they should all have in common, however, is resilience. Change management can be arduous work, and the ability to bounce back from setbacks is what carries a team forward.  

Define your road map

Once you’re satisfied with the “why” and “who” of your data and AI endeavor, it’s time to define your “how”—and time is always limited. There are still too many problems within the larger umbrella of an organization’s mission to solve them all effectively.

The great thing about data analytics is you can do so much with it; that’s also the bad thing. To deliver business outcomes requires discipline and focus. Having a clear vision and an overarching “why” from the top is critical, but there also must be a systematic way of breaking down that vision into concrete goals that a team of analysts, data scientists, and engineers can help to achieve.

For example, if the overall goal is to increase customer satisfaction, you still need to define the most effective way of moving that metric. Should you focus on increasing the quality of the user experience or on improved customer service? Would it be more effective to develop chatbots, better product recommendations, or better targeted promotions?

There are so many problems to solve in organizations that it can be tempting to jump straight into solution mode. Although you must be quick on your feet and begin to deliver value early on, it’s important to continuously improve your understanding of problems and to socialize them with relevant stakeholders and partners. This ensures that the right solution is brought to bear on the right situation.

Clarity about the most critical business problems can shield you from pursuing data and AI technologies for their wow factor rather than the value they will deliver.

With clearly identified, defined, and well-scoped problems that support your “why,” in what order should you pursue them? The obvious answer is by ranking opportunities according to the likely value they will deliver and the feasibility of success, picking those that are most likely to deliver the greatest return. Assessing the technical feasibility, based also on the availability and quality of necessary data, is only part of the equation. Feasibility will depend critically on working with stakeholders and partners who are clear about what they seek, and who are engaged and open to trying new things. It also helps if they are honest and values-oriented because data can reveal uncomfortable truths.

Another key dimension is how long it’s likely to take to deliver that expected value. People lose patience, so it’s important to prioritize data and AI applications that build momentum and deliver value quickly. In many organizations it’s often the simple applications that build the most momentum and help convert the sceptics.

Jeff McMillan, Morgan Stanley’s chief data and analytics officer, first concentrated on giving stakeholders better access to basic information—such as sales figures—instead of chasing the most tempting opportunities using AI and machine learning. By doubling down on training junior people in data visualization and data warehouse access, McMillan engendered tremendous trust, buy-in, and support to then pursue ambitious projects using more advanced AI and machine learning technologies.

At the end of the day, you don’t get credit for making it harder than it needs to be. You get credit for adding value.

First published April 12, 2021, on INSEAD Knowledge.

About The Author

Caroline Zimmerman

Caroline Zimmerman is a research associate at INSEAD, researching data leadership and how organizations can better link their data capabilities to business outcomes.