What leaders at winning companies know–and what you need to learn

Becoming a better leader

What does it take to be a leader who senses and responds to opportunities and threats? The subject is broad, of course, and probably best summarized as “leading under uncertainty.” The following recommendations aim to help you become aware sooner of what’s coming at you, more comfortable with change, and better equipped to act once you’re ready. One theme that weaves through the discussion has to do with making the most of your own human thinking and capitalizing on the ever-emerging strengths of AI—while recognizing the inherent flaws in both.

Look for weak signals and anomalies

Let’s start with what you know. Being well-informed drives your ability to recognize the opportunities and threats your company faces. Nearly all of us would agree that continuous learning and knowledge seeking, with parallel skills adaptation, is a virtue in highly complex, uncertain, and fast-changing environments. But let’s suppose you’re already reasonably well informed about the megatrends driving business change and the way they affect your industry, market, and organization. Is there something else you could be doing to supplement your approach?

One way to reinforce broad, generalist knowledge is to focus on so-called weak signals, defined as seemingly random or incoherent information that appears to be background noise, at least within a given mental model or fixed set of assumptions. By connecting these signals with other pieces of information—and seeing them through a different lens or frame—you can put weak signals into a new and more useful context, where they might appear as harbingers of significant change.

To search for weak signals, start paying closer attention to edges and peripheries. These might be geographic peripheries, such as emerging markets; demographic peripheries, such as the behavior of fringe consumer groups; or market or sector peripheries, where new entrants are pioneering business models. Peripheries also occur among the weak ties in our social and professional networks. Getting out of our own bubble can be useful for discerning weak signals in out-of-the-way places. 

Meanwhile, try to explore anomalies, which can help us, in the words of cognitive psychologist Gary Klein, “see what others don’t” when analyzing data. Most people drop the outliers when it comes to data analysis, but Klein recommends investigating rather than eliminating unexpected events, behaviors, or outcomes. Perhaps you’ll find an anomaly in financial performance that can’t be easily explained by normal business operations or market conditions. Some of these anomalies could be cause for alarm, but others may signal the emergence of, say, a potentially disruptive revenue model.

Consider how Netflix kept its ear to the ground regarding key weak signals emerging in the early 2000s, when it was still a DVD-by-mail business. It saw a range of early indicators—a shift in consumer preference toward instant access and digital content, the growing availability of broadband internet connections, and improvements in video compression algorithms, among other signals. By interpreting these as indications of future demand, Netflix made the strategic decision to invest in a streaming platform supported by new partnerships with content providers in a new ecosystem centered on streaming services. Netflix used weak signals to shift its business model and position itself as a leader in the emerging streaming market that it has since come to dominate.

Use technology for insight

Now that you’re paying attention to weak signals and anomalies, to complement being well-informed more broadly, how do you make the most of the data, information, and knowledge you’re acquiring? Here’s where technology again comes into play. For the human mind, the interactions and co-occurrences among and across diverse information sources can be opaque. But the right AI engine can help make sense of them. Machine learning algorithms can discern relationships among variables and features in data, highlighting correlations, dependencies, and associations that might be difficult for humans to perceive. Using proprietary or vendor-provided AI platforms and engines, leaders can harness the power of technology to analyze data, discern patterns, and perform predictive modeling. They can consider information from such diverse sources as industry news, patent filings, investments and innovations by traditional competitors and new entrants, research publications, clinical trials, and search trends. Companies are already using AI, of course, to predict shifts in consumer behavior, among other things—as with the AI-powered algorithm of in-flight internet access and entertainment company Gogo Air, which uses customer data to predict customers’ interests.

Non-technology executives looking to make the most of AI should first acquire a foundational understanding of AI concepts and terminology—and its potential applications in their industry. One way to do so, fittingly enough, is by using generative AI, which can help speed the process of understanding AI more broadly.

Get the bugs out of your “meatware”

Even a sophisticated use of technology can’t entirely overcome the human flaws that in a software setting are typically called bugs. In some cases, technology can further amplify these flaws. 

What sort of flaws do we mean? Consider the common impulse to lean on our own experiences and instincts when it comes to looking toward an uncertain future. Research shows humans (the meatware in our system) can rely on experience or intuition only when making decisions in a high-validity environment, which is one where their previous experiences have relevance and where they have had numerous opportunities to learn from those experiences. Because leaders face an uncertain future and are contemplating novel moves, they more often operate in low-validity environments, in which their experience and intuition are of little value. 

Furthermore, humans are naturally inclined to cognitive biases—mental shortcuts and prejudices that influence their judgment and decision-making. Nobel laureate Daniel Kahneman’s best-selling book Thinking, Fast and Slow may have helped many leaders become aware of these biases and learn to observe them in the behavior of others, but being aware of our biases is not enough to counter them in ourselves. In fact, it’s effectively impossible to “de-bias” ourselves.

But leaders can turn to process-based remedies to help their companies de-bias decision-making. Organizations can institutionalize these approaches to help their leaders evaluate the threats and opportunities they face and make decisions related to them. For example, processes that lead to formation of an “outside view” are useful to countering the planning fallacy (in which planners inadequately adjust schedules despite previous projects regularly taking longer than anticipated). A “pre-mortem” process, to articulate how an effort might fail, is useful to counter overoptimism, and presenting countervailing views through an “A team/B team” approach is useful for countering confirmation bias (the human tendency to discount disconfirming evidence). Similarly, checklists can be effective for decisions that need to be made quickly and that do not warrant heavy analysis. Algorithms and AI are suitable for decisions that reoccur and are relatively complex. And decision analysis processes are helpful for large, high-consequence decisions.

Embrace transformation

The “T-word”—transformation—is on the lips of most executives these days, and for good reason. PwC’s latest Global CEO Survey found that 45% of CEOs think their organization will no longer be economically viable in ten years’ time if it continues on its current course.

The urgency to change is broad based, but it’s striking nonetheless to see the degree to which top-quintile companies in our survey are heeding the call. For example, more than three-quarters of respondents at top-performing companies say that transformation was already a priority three or more years ago, compared with about half of respondents at other companies.

Moreover, top performers appear to be putting their money where their strategy is. Top-performing companies are nearly four times as likely as other companies to have increased their investment by 30% or more in that same three-year time frame across the areas we studied—business ecosystems, managed services partnerships, and new technologies (see chart). Many leading companies are using managed services partners, for example, to plug capability gaps such as shortfalls in specialized technology talent. 

Leaders should also look more broadly at the future workforce. Understanding the wants and needs of younger workers can provide a clear talent advantage. Given the high correlation between leading companies and executives who act with foresight and a bias toward action, it’s no surprise to find the best leaders at the companies that are transforming the most.


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