The drawer test: why tacit thinking is now a job requirement
Your competitor had perfect data.
Market research showed 73% of their target demographic drank coffee daily. Pricing analysis confirmed willingness to pay $45/month. Their app ranked highest in UX testing. They launched in Q1, riding the “new year, new habits” wave.
The logic was airtight. If these conditions exist, then success follows.
They canceled after eight months.
What the data couldn’t see
Here’s what someone paying attention would have noticed: successful coffee subscriptions aren’t bought—they’re gifted. The Nespresso machine sits prominently on the counter, but the subscription box gets shoved in a drawer. People post unboxing videos but rarely mention drinking the coffee. The language customers use is telling: “I should use it more.”
That phrase—”I should use it more”—carries guilt, not joy. No amount of market research captures that distinction.
Two types of thinking, one clear winner
We’ve built our businesses on deductive reasoning: if A, then B. AI has accelerated this approach to superhuman levels. It can process market research, pricing analysis, and timing optimization faster and more thoroughly than any team of analysts.
But deductive reasoning only works when you’re asking the right questions. It operates on explicit knowledge—the kind you can measure, survey, and optimize.
Abductive reasoning works differently. It starts with observation and works backward to the most likely explanation. You notice the box in the drawer. You hear the guilt in “I should use it more.” You connect patterns that don’t appear in any dataset.
This is tacit thinking—knowledge gained through experience, observation, and intuition rather than explicit instruction.
Why this matters now
For decades, we could coast on deductive excellence. Get the data right, follow the logic, achieve results. AI has compressed this advantage to near-zero. Any company can run the same analyses, optimize the same variables, reach the same conclusions.
What AI cannot do—at least not yet—is notice that the subscription box lives in a drawer. It cannot hear the difference between “I love this” and “I should use this more.” It cannot sense the gap between stated preferences and actual behavior.
For engineers, tacit thinking is already a necessity. The most effective AI practitioners aren’t the ones who write the best prompts. They’re the ones who notice when something feels off, who sense which approaches will fail before running experiments, who recognize patterns that haven’t been documented.
For business leaders, this shift is coming fast. The competitive moat is moving from “better data analysis” to “better observation.” Companies that cultivate tacit thinking will spot opportunities and threats their competitors’ dashboards never show.
The new skill set
Tacit thinking isn’t mystical. It’s a skill built through deliberate practice:
Observe before you analyze. Spend time with customers before you survey them. Watch what they do, not just what they say.
Trust discomfort. When something feels wrong but the data says otherwise, investigate the feeling. It often knows something the spreadsheet doesn’t.
Practice pattern recognition across domains. The “drawer phenomenon” shows up everywhere—products people buy but don’t use, features they request but ignore, goals they set but abandon.
Document your hunches. Keep track of intuitions before you validate them. Over time, you’ll calibrate which instincts to trust.
The drawer test
Before your next launch, ask yourself: where will this product live in the customer’s life? On the counter, in active use? Or in the drawer, generating guilt?
No survey will answer this question. Only observation will.
The engineers and leaders who master tacit thinking will build what the data says is impossible. Everyone else will keep wondering why perfect logic produces imperfect results.
