Taste Is A Leadership Skill

Daniel Pink recently identified taste as one of six human skills that become more valuable as AI advances. His argument: AI is excellent at generation. It can produce endless options, drafts, strategies, designs. But generation without discrimination is just volume. Someone has to look at the output and know what’s good. That’s taste. And it’s a capacity AI doesn’t have.

This matters for leadership more than most discussions about AI acknowledge.

#What Taste Looks Like in Practice

Analysis can narrow five strategic options to three. It can model scenarios, weight risks, and rank expected outcomes. What analysis cannot do is tell you which option fits this team, this competitive position, this stage of growth. That final act of recognition draws on something harder to articulate: a sense of fit built from years of watching strategies succeed and fail, noticing which conditions produce which outcomes, developing an internal model for what “right” looks like in context.

That sense of fit is taste. It matters precisely because it can’t be reduced to a framework or a checklist.

#The Science of Expert Recognition

Herbert Simon spent decades studying how experts think. Through extensive research on chess masters and professional decision-makers, he found that experts store tens of thousands of meaningful patterns in long-term memory. When they encounter a new situation, they don’t analyze it from scratch. They recognize it as an instance of a pattern they’ve seen before, and that recognition triggers stored knowledge about what works.

Simon put it plainly: “The situation has provided a cue; this cue has given the expert access to information stored in memory, and the information provides the answer.” Intuition, in his framing, is nothing more and nothing less than recognition.

Gary Klein’s research on naturalistic decision making confirmed this from the field. He studied firefighters, critical care nurses, military commanders, and other experts making high-stakes decisions under time pressure. What he found challenged the conventional model of decision-making, which assumes people generate options and then compare them. Experts under pressure don’t do this. They recognize the situation as a type, the type suggests a course of action, and they mentally simulate that action to see if it will work. If it will, they go. If it won’t, they adapt.

Klein called this recognition-primed decision making. Experts aren’t choosing between alternatives. They’re recognizing what fits. The first option they consider is usually viable, because their recognition is drawing on a deep library of patterns built through experience.

#Taste as Applied Pattern Recognition

Simon and Klein’s research maps directly onto what Pink describes. Taste is pattern recognition applied to quality and fit. The same mechanism Klein observed in firefighters operates when a leader reads a strategy deck and feels something is off before they can say why. The pattern doesn’t match their internal library, and the mismatch registers as felt sense before articulated critique. AI pattern-matches statistically across training data. Human experts pattern-match against lived context. The difference matters most when the question is “does this fit us, here, now?”

This is what I explored in Borrowing Knowledge: owned knowledge versus borrowed knowledge. Taste can’t be borrowed by reading a strategy book or attending a workshop. It’s built through direct experience. Making decisions, watching outcomes, updating the internal model. The cycle has to be lived.

And as I wrote in The Focus You Fear, depth over breadth is how this kind of knowledge accumulates. Fifteen years in one industry builds a richer pattern library than three years in five industries. The depth itself is doing the work.

#Why AI Can’t Replicate This

AI generates by statistical pattern matching across its training data. In a narrow sense, this resembles recognition. But it’s missing the experiential foundation that gives human taste its power.

When a leader recognizes that a strategy “fits,” they’re drawing on embodied knowledge: the memory of watching a similar strategy fail at a previous company, the felt experience of a team that was ready for a particular kind of challenge versus one that wasn’t, the accumulated sense of what this specific market rewards. This knowledge is contextual, situated, and personal. It can’t be abstracted into training data because much of it was never written down.

Ira Glass described a version of this in creative work: “All of us who do creative work, we get into it because we have good taste. But there is this gap. For the first couple years you make stuff, it’s just not that good. But your taste, the thing that got you into the game, is still killer.” The gap he describes is the distance between recognizing quality and being able to produce it. The taste comes first. The skill catches up through volume of practice.

In leadership, the equivalent gap is between recognizing the right strategic direction and being able to articulate why. The recognition often arrives before the reasoning. And that’s fine. The reasoning can be reconstructed. The recognition can’t be manufactured.

#Developing Taste Deliberately

If taste is pattern recognition, and pattern recognition develops through experience, then the question becomes how to accumulate the right kind of experience.

Kahneman and Klein’s joint paper on the conditions for developing genuine intuitive expertise identifies two requirements: an environment with enough regularity that patterns exist to be learned, and sufficient practice with feedback to learn those patterns. Both conditions matter. Practice without feedback doesn’t build accurate recognition. And feedback without sufficient repetition doesn’t build speed.

For leaders, this means that taste develops through a specific cycle: make a decision, observe what happens, connect the outcome back to the conditions that existed when you decided. The connecting step is where most people lose the thread. Decisions happen. Outcomes arrive. But the explicit linking of “I chose this because I saw that pattern, and here’s what actually happened” is the deliberate part of deliberate practice.

Few leaders do this deliberately. Decisions pile up. Outcomes blend together. The pattern library grows slowly and somewhat randomly instead of being actively curated. Meanwhile, AI is getting better at everything around taste—the analysis, the option generation, the data synthesis. The gap between what AI handles and what only you can judge is widening. The taste that fills that gap is either sharpening or it isn’t.

#Something to Try

After your next significant strategic decision, write down what made you choose. Not the post-hoc rational justification. The actual moment of recognition. What did you see? What pattern triggered the response? What felt right, and why?

You might struggle with this. The recognition often happens below the level of conscious articulation. That struggle is useful. It forces you to surface the mental models you’re actually operating from, which is the first step toward refining them.

Taste is earned. And developing it deliberately, by paying attention to your own recognition patterns, builds a capacity that no amount of AI-generated analysis can substitute for.