What D-Day Can Teach Us About Judgement in the Age of AI

Have you seen the movie Pressure with Brendan Fraser? If you haven’t, you should. It’s set in the tense days before the D-Day landings in 1944, when a decision about the weather could determine the fate of the invasion.

At its center are two meteorologists studying the same charts, the same forecasts and broadly the same evidence, yet reaching different conclusions about whether Eisenhower should proceed. One sees historical patterns and the outcomes they have usually produced. The other argues that there are simply too many interacting variables to assume that a historically similar pattern will produce the same result this time.

That distinction matters. The second meteorologist isn’t rejecting experience. He is using experience to understand the limits of precedent. He knows the history, but he also recognizes that the particular combination of circumstances in front of him may be different enough to demand a different judgement.

Their disagreement isn’t really about the data. It’s about its interpretation. And the most consequential part of Eisenhower’s decision was not simply that the invasion went ahead on June 6th; it was that he decided not to go on June 5th.

This isn’t really a story about weather. It’s a story about experience, judgement, confidence and leadership. It’s also a useful way of thinking about how AI is changing where humans create value.

AI May Make Experience More Valuable, Not Less

For most of my career, experience has been one of the most valuable assets people possess. We celebrate leaders with twenty years in an industry, consultants who have ‘seen it all’ and executives who have navigated similar challenges before. Boards recruit experience, recruiters sell it and resumes are built around it.

And for good reason. Experience is not simply an accumulation of facts. It is accumulated context. It is the memory of strategies that looked sensible but failed, decisions that worked for reasons nobody initially understood, personalities that changed outcomes, and small differences in timing or circumstance that turned out to matter enormously.

Until recently, however, experienced people also had to spend a huge amount of their time doing the work required before that experience could be applied. Understanding a market meant commissioning research. Competitive intelligence took ages to assemble. Customer evidence, financial data and internal information all had to be gathered and organized before anyone could begin to interpret what was happening.

Experience helped us bridge the gaps. We generalized. We looked for patterns. We borrowed from what had worked before. We leaned on best practice, case studies and familiar frameworks because information was incomplete and time was limited.

But that does not mean experience was merely a shortcut for missing information. Quite the opposite. The more information we can obtain, the more valuable genuine experience can become — because experienced people are better equipped to recognise which facts matter, which similarities are superficial and which differences could change the outcome.

The danger is not experience. It is treating experience as precedent: ‘I’ve seen this before, therefore I know what will happen.’ The better use of experience is as context: ‘I’ve seen things like this before, which helps me understand why this situation may be different.’

AI Has Changed the Economics of Thinking

This is where AI changes the equation. We have become accustomed to saying that AI saves time. That’s true, but it’s an understatement. What AI has really done is collapse the cost of acquiring, organizing and analysing information.

Tasks that once took days now take hours. Research that once required a team of analysts can often be assembled in an afternoon. Market summaries, competitor profiles, customer insights, financial analysis and strategic options can all be produced in a fraction of the time they once required.

AI can search, summarize, and compare. It can identify patterns across vast quantities of information. Increasingly, it can offer plausible explanations and directional recommendations. None of that is infallible, and it still requires validation and skepticism, but it can remove an extraordinary amount of the heavy lifting that used to consume human intellectual effort.

The most interesting consequence of AI is not that it diminishes the value of experienced people. It may do the opposite. If an experienced executive no longer has to spend most of the available time gathering and organising the evidence, that time and intellectual bandwidth can be redirected towards something more valuable: understanding what the evidence means in this particular situation.

AI can help tell us what has happened before, what usually happens and what the available evidence suggests. Experience helps us understand the significance of those patterns. Judgement decides how much of them applies here.

AI does not replace experience. It gives experienced people more opportunity to use it.

Context Is Everything

That brings us back to Pressure. The crucial disagreement is not between experience and inexperience. It is between two different uses of experience.

One view says that because a particular pattern has historically produced a particular outcome, it is reasonable to expect the same outcome again. The other says that historical patterns are useful only if we understand the variables that produced them — and whether those variables are sufficiently similar now.

Business is no different. Two companies can operate in the same market, sell similar products and face apparently similar problems, yet have different customers, cultures, leadership teams, competitive pressures, financial constraints, timing and appetites for risk. They can look remarkably similar from a distance and still be different enough for the same strategy to produce a completely different result.

That is why ‘best practice’ can be dangerous when it becomes detached from context. Six Sigma may have been highly effective at GE, yet the same discipline was widely criticised when applied at 3M because practices designed to drive consistency and efficiency could also constrain the experimentation on which 3M’s innovation culture depended. The framework was not inherently right or wrong. The context changed what it meant.

AI is extraordinarily good at finding analogies and surfacing what usually works.

Experienced leaders can use those analogies far more intelligently because they bring years of context to the comparison. But neither the analogy nor the experience makes the decision for them. That is where judgement begins: deciding which similarities matter, which differences matter more, and what all of it means for us, here, and now.

Interpretation Is Becoming the Scarce Skill

If AI gives us more information, more quickly, and experience gives us the context to understand it, the next question is what we do with both. Isn’t that interpretation?

Interpretation isn't simply analysis. AI is already becoming remarkably good at analysis. It can compare options, identify patterns, even challenge assumptions and suggest plausible explanations.

Interpretation is deciding which of those things matter in our particular circumstances or given a particular context. An experienced leader has accumulated thousands of observations, decisions, successes, failures and surprises. Previously, much of their time was still consumed acquiring and processing the information needed to make the next decision. AI changes all that.

It gives experienced people more intellectual bandwidth to do something potentially much more valuable: bring what they've learned to bear on the specific combination of circumstances in front of them.

But experience alone still doesn't provide the answer. In fact, used badly, it becomes a trap. "I've seen this before" can quickly become "I know what happens next." The meteorologists in Pressure illustrate the distinction perfectly. Both are experienced. Both understand the historical patterns. The difference is in how they interpret their relevance to the conditions confronting them now.

Perhaps that's where human value is moving. Not away from experience, but towards the ability to apply experience while maintaining one’s objectivity? AI can expand what we know, experience can deepen our understanding, and judgement is still required to decide what it means — here, now, and for us.

The New Skill Is Knowing What to Ignore

Judgement in an information rich environment has become about three things:

Relevance — Which information actually matters to the decision we're making?

Significance — Which differences in our circumstances are important enough to change the likely outcome?

Weight — How much importance should we attach to each piece of evidence?

Experience helps with all three.

Someone who's actually lived through market entries, acquisitions, sales transformations, product launches or whatever else we're considering may be much better equipped to spot the seemingly minor variable that really matters.

Experience has a dark side. It can also cause someone to overweight familiar information because it confirms a pattern they already recognize, creating a bias.

So the skill isn't simply having experience. It's knowing when your experience is illuminating the current situation and when it's distorting it.

Perhaps the executive who asks the best questions, recognizes the most important differences and exercises the strongest judgement will outperform someone with twice the experience but who relies too heavily on historical patterns. Maybe second guessing ourselves will become a positive!

In an age of information scarcity, knowing more was an advantage. In an age of information abundance, knowing what is a greater one.

Conclusion: What Matters Now?

The lesson from Pressure isn't that one set of experiences failed. Both meteorologists were experienced. Both had access to the evidence. The difference was how they judged what was relevant, how much weight they gave to historical patterns, and how they interpreted the particular combination of circumstances in front of them.

AI is rapidly removing many of the constraints that once limited our access to information. It can research, compare, analyze and identify patterns at a scale and speed that was unimaginable a few years ago. That doesn't make experience less valuable. It makes it more valuable. But it does change how we need to use it. How so?

The advantage will belong to the person who can decide what information matters, recognize which differences are significant, and apply their experience without being trapped by it.

For decades leaders have used frameworks designed to help them organize information, recognize patterns and make better decisions, in theory.  But if AI can increasingly perform much of that work for us, where should we be spending our time instead?

AI isn't replacing human thinking. It's changing where human thinking creates value. And increasingly, that value lies in knowing what matters, why it matters, and what to do about it.