AI for Strategy Compilation (AC Ep80)

“AI can make the process of sensing for signals much faster and much more efficient. You can think of it as a supplement to our brain. It can sort through massive amounts of data, track the latest developments, and flash alerts when something important emerges.”

– Rita McGrath

“What I found surprising in our exercises was how disruptive AI was. At first, I thought they would hate it, but they actually liked it. It made them stop and think because it forced them to break out of their usual patterns and consider ideas they wouldn’t have consciously introduced into the discussion.”

– Christian Stadler

“AI can accelerate the foresight process. It can help generate diverse perspectives, identify second-degree impacts, and uncover biases we might not notice. Of course, human critical thinking is still essential—we shouldn’t accept AI outputs as absolute truth, but rather use them as a starting point.”

– Valentina Contini

“One key area where AI excels is handling cognitive complexity. Humans struggle to hold thousands of variables in their heads, but AI can process vast amounts of interconnected data. The challenge is designing interfaces that allow humans to interact with this complexity in an intuitive way.”

– Anthea Roberts

What you will learn

• Bridging human cognition and AI for better decision-making
• How AI disrupts traditional boardroom dynamics
• Enhancing foresight with AI-driven scenario planning
• The role of AI in sense-making and strategic insights
• Why AI-generated variety outperforms human creativity
• Managing cognitive complexity with AI augmentation
• The evolving partnership between humans and AI in strategy

Episode Resources

Companies & Organizations

• Wrigley
• ChatGPT
• OpenAI

Technical Terms & AI-Related

• Artificial Intelligence (AI)
• Large Language Models (LLMs)
• Generative AI
• Cognitive Complexity
• Metacognition
• Strategic Foresight
• Decision-Making Frameworks

Transcript

Ross Dawson: One of the key themes is strategy. How do we do strategy in a world that is accelerating, with all these overlay themes? There are, as you say, 10x shifts in many dimensions of work. This brings us to human capabilities. Humans have limited, finite cognition, even though we have extraordinary capabilities far transcending anything else. Now, we have AI to augment, support, or complement us.

I’d like to dive in deep, but just to start—what is your framing around human capabilities in strategic thinking today, and how they are complemented by AI?

Rita McGrath: Sure. Well, as I mentioned, human brains think in linear terms. We think immediately in terms of getting from here to there to avoid a predator. Back in the day when we were evolving, that worked pretty well. But we don’t do very well with exponential systems because they look small, and they look small, and they go small—until suddenly they don’t. It’s the whole “gradually, then suddenly” idea.

What I argue is that you need to supplement what your brain can manage on its own. This is where I think AI comes in. What I’ve set up with companies is a series of what I call “time zero events,” which signal that a future inflection point has arrived. We don’t know exactly when, but we work backward and ask, “Before that happens, what would have to be the preceding situations?”

AI can make that process of sensing for signals much faster and much more efficient. You can think of it as a supplement to our brain. It can sort through massive amounts of data, track the latest developments, and flash alerts when something important emerges. This allows us to blend human imagination—something AI is not very good at—with AI’s ability to crunch massive amounts of data. That’s where I think AI will have a lot of power in strategy.

Ross: One of the core themes of my work, and I think yours as well, is sense-making. We have vast amounts of information out there. As strategists, we need to take in that information, make sense of it, and make effective decisions as a result. How can AI support our ability to comprehend how the world is working so that we can make better decisions?

Rita: AI is really good at taking large amounts of information and breaking it into digestible chunks. Humanity has limits to how much information it can process. There’s actually a whole line of theory on this, which states that search, in the traditional sense, is not costless. Theoretically, a rational human being would entertain every possible combination of possibilities, create decision criteria, and then select the best option. But humans have cognitive limits, whereas machines have far fewer.

Properly instructed, AI can present us with different pictures of the world. Another thing humans aren’t very good at is generating variety. Think of those old creativity exercises where someone asks you to come up with as many uses as possible for a paperclip. People start with obvious answers: “It can hold papers together,” “It can mark your place in a book,” “It can unlock things.” But after 50 or 60 uses, they run out of steam. Many ideas are anchored on the first few.

Machines, on the other hand, don’t have those biases. They might generate 300 possible uses—sure, 200 of them might be terrible ideas, but they would be more divergent than what humans come up with. That’s where AI helps in sense-making. It shows us possibilities we wouldn’t have seen otherwise.

Ross: Now, let’s dig into how AI can be used in the boardroom. One way that resonates with board directors is “red teaming,” where you have a decision and ask AI to generate counterarguments. AI can surface concerns that might not come up in human discussions. What other applications have you found valuable for AI in the boardroom?

Christian Stadler: What I found surprising in our exercises was how disruptive AI was. Imagine a group of people who have worked together for a long time. Their discussions are smooth because they know how each other thinks. Then, I introduce ChatGPT into the meeting.

I’d tell them, “Read these five pages,” and suddenly, they’re confronted with a long list of new insights. It disrupted their usual flow. At first, I thought they would hate it, but they actually liked it. It made them stop and think. The disruption forced them to break out of their usual patterns and consider ideas they wouldn’t have consciously introduced into the discussion.

Ross: What are the ways in which you are seeing or applying tools to augment the foresight process?

Valentina Contini: I started looking into this about two years ago, when GPT-3.5 was released. One of the things that frustrated me was that generating scenarios for companies took too long. You needed to involve multiple experts and stakeholders, which meant it only happened every three to five years. But in today’s rapidly changing world, that’s not enough.

AI can accelerate the foresight process. It can help generate diverse perspectives, identify second-degree impacts, and uncover biases we might not notice. It’s especially useful in tools like a futures wheel, where many perspectives need to be mapped. AI can bring in unexpected viewpoints based on large-scale data analysis. Of course, human critical thinking is still essential—we shouldn’t accept AI outputs as absolute truth, but rather use them as a starting point.

Ross: Human-AI collaboration involves complex problems where humans retain the highest-level context and decision-making ability, while AI complements our cognition. What does that interface look like?

Anthea Roberts: This is one of the most fascinating questions of our time. Both humans and AI have different strengths, and the way we interact with AI is evolving.

For example, when working with large language models, humans shift from being primary generators of content to being managers and editors. We direct how the AI works and refine its outputs. This requires metacognition—not just thinking about our own thinking, but also understanding how the AI thinks.

One key area where AI excels is handling cognitive complexity. Humans struggle to hold thousands of variables in their heads, but AI can process vast amounts of interconnected data. The challenge is designing interfaces that allow humans to interact with this complexity in an intuitive way. A simple chat interface isn’t enough—we need tools that allow for narrowing focus, cognitive offloading, and iterative collaboration.

Another challenge is balancing AI’s overwhelming amount of information with human discernment. Many people feel deluged by AI-generated content, making it crucial to develop skills for filtering and applying insights effectively.

Ross: So AI not only provides information but also changes the way we think and interact with complexity?

Anthea: Exactly. Over the last year and a half, I’ve realized that much of my work is metacognitive. I don’t tell people what to think, but I help them understand how they think. The same applies to AI—we need to recognize its biases, workflows, and limitations while leveraging its strengths.

One of the biggest challenges will be developing interdisciplinary AI agents that can collaborate across different fields of expertise. AI will evolve into an indispensable partner in decision-making, but we need to ensure that humans remain in control of the broader context and ethical considerations. How we navigate this balance will define the future of AI-human collaboration.