Disruptive change, from digitalization to dramatic shifts in consumer behavior, is happening across industries at breakneck speed. So how can leaders get their companies to change course before their businesses are on a “burning platform” and running out of strategic options?


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The key is to act before compelling data is widely available, a challenge that is complicated by the lack of information in periods of uncertainty. A simple conceptual model — “the information-action paradox”— can help companies map where they stand when it comes to their knowledge and their ability to act. By generating the right kind of private data, lowering the threshold of proof, companies can make confident strategic moves faster than the speed of disruption.




Is it realistic that a large organization can effectively innovate beyond the core? Isn’t it easier for them to monitor start-ups and acquire them after an emerging disruptive threat has been sufficiently de-risked?

Almost all examples of large organizations pushing in new directions involve at least amount of M&A activity. This is sensible because new directions require new skills and capabilities. That said, the challenge you have with a “wait and acquire” approach is by the time the emerging disruptive threat has indeed been sufficiently de-risked, the acquisition target tends to be quite expensive. And the organization hasn’t developed any muscle around pursuing new growth. So our view is this is very much an “and.” There is plenty of evidence that large companies can indeed venture beyond their core, and proven approaches to help maximize the chances of success. Our book Dual Transformation talks about this in more depth.


If a company has an exploit portfolio that generates a lot of cash, how do you handle the internal politics and dynamics in which an explore initiative may eventually “kill” the current engine?

This question gets into some of the things we talk about it in “Persuade Your Company to Change Before It’s Too Late.” One thing that is important to do is to raise awareness around the risk related to not finding the weak signals in the private data, interpreting them through models and frameworks, and exploring them collectively in strategic dialogues helps to manage this dynamic. Additionally, you have to recognize that your organization might very well face an identity crisis as it goes through its transformation. You need to make sure there is sufficient space for people to share their feelings about it, that space is psychologically safe and you have support to manage the change. At some point, though, you have to make the call. When software company Adobe, for example, went from selling packaged software to a software-as-a-service moment it had a “burn the boats” moment to be clear there was no going back.


How do you know you are aligned?

The odds are you aren’t. There’s a fascinating article by Don Sull from MIT and co-authors that found only 50 percent of top leaders can name their organization’s top priorities. These are the people who set the priorities! That indicates people said yes but thought no during meetings. In the article “Unite Your Senior Team,” we suggest using polling tools like Sli.do to help to surface misalignment. Or go even further and “walk the line.” In a physical meeting, this involves placing a piece of tape in the front of the room and asking people to array on the tape based on their answer to a question. We actually created customized technology to do the same thing virtually. It’s remarkable when you do it how quickly different viewpoints surface. And remember, that’s ok! You want to surface those areas of misalignment and figure out the different assumptions people are making because that informs the data you need to gather or the experiments you need to run.


Why do you think companies do not use JTBD as their “North Star” when new entrants threaten their markets to drive change towards a new course?

In the Soul of a New Machine, Tracy Kidder won the Pulitzer Prize for his detailed description of the launch of a new computer by Data General. One finding from the book is that you can basically see the organization chart in the design of the computer. For better or worse, structure often drives strategy. In a company’s early days, there is a clear and obvious connection between a company and its customers. Over time that connection fragments as the organization scales, departments are formed, and so on. Ted Levitt’s classic “Marketing Myopia” described this problem 60 years ago, and the problem is remarkably persistent.


Innosight is among the few firms that overtly connects INNOVATION x TRANSFORMATION. How does one bring about a culture that focuses on both in parallel to better focus on what matters?

The argument we make in Eat, Sleep, Innovate is the key to-do is to create a culture of innovation, which we define as one where the behaviors that drive innovation success come naturally. Those behaviors are curiosity, customer obsession, collaboration, being adept in ambiguity, and empowerment. Now, how do you create that culture? The argument we make in the book is the biggest barriers are existing habits and routines. After all, innovation is something different that creates value, and organizations are designed to do what they are currently doing more effectively and efficiently. So our recommendation is to break that inertia with what we call behavior enablers, artifacts, and nudges (BEANs). You can see eatsleepinnovate.com for much more on this topic.


How does the use of custom data change how you communicate strategy and gain alignment within the organization?

Great question. Mark Bertolini, who drove an aggressive and successful transformation of US health insurer Aetna as CEO, described it this way: “The CEO’s responsibility is to create a stark reality of what the future holds, and then to begin to build the plans for the organization to meet those realities.” What we call private data in our HBR article is a critical component of creating that stark reality. As Bertolini described it, “Your top leaders have to be aligned around the long-term vision and the assumptions about the future that underpin it. But you also have to change the nature of the dialogue with them, away from one about certainty and predictability, and towards one about assumptions, managing risks, and ‘what you have to believe’ for a certain course of action to be the best one. This is a significant shift for even the most successful leaders.


Do you have examples doing the same in other sectors?

We’ve run a series of reports looking at organizations that have successfully transformed, and a common element is that they have the courage to choose before they are on the proverbial platform. Of course, the exact story is different, but the examples are from industries ranging from software (Adobe) to sausage (Rügenwalder Mühle).


Is there a leader’s behavior pattern correlated to successful leading digital transformation from your experiences?

There is some recent research by Linda Hill at the Harvard Business School on this exact topic. Her research team identified adaptability, curiosity, creativity, and comfort with ambiguity as the four most critical characteristics. This maps well with what we describe in Dual Transformation, where we say leaders have to have the courage (to choose before they are on a burning platform), the conviction (to persevere in the face of predictable crises), the clarity (to focus on a select few strategic moonshots) and the curiosity (to explore in the face of uncertainty. Lurking behind all of this is what you might call a leader’s “adaptive capacity” to be able to handle the seeming tension between demands to deliver today and invent tomorrow, to extract and exploit and to be sustainable, and so on.


Where do predictable data insights capabilities fit here? Such as AI & ML.

Having capabilities around artificial intelligence, predictive analytics, machine learning, and so on is a key enabler of being able to identify and make sense of private data. Like many 21st century problems, success requires a blend of technical skills to create, capture, and process the right data, and adaptive skills to be able to interpret, communicate, and drive action based on that data.


It seems to me that the cost of doing nothing at all is usually seen retrospectively once you are on the burning platform, and any forward-looking company will eventually move away from that mistake. What about the cost of not doing enough quickly enough? How do we realize this early and mitigate it? Use the same framework proposed in this webinar?

Yes, there certainly is a spectrum of responses. You can imagine a 2-by-2. Are we doing the right or wrong thing, and are we doing it slowly or quickly. The wrong thing slowly is the worst. The right thing slowly is problematic too. Here the challenge is going from a focused experiment to something at scale, to have that moment where you say I know enough and I need to put all my chips in. The wrong thing slowly is equally punishing. The wrong thing quickly, interestingly, can be a fine thing to do as long as you learn you are wrong and course correct. The key in all of this is being really explicit about the assumptions you are marking, running focused experiments around those assumptions, and adjusting based on the results of the experiments. Our book The First Mile talks about this in more depth.