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There is a long intellectual lineage of psychologists and business theorists who have pointed out that organizations routinely work on the wrong problem.
This tendency to misdiagnosis isn’t a failure of intelligence or effort. It’s a cognitive default. Humans are inexorably pulled towards the symptoms they can see, not the structure underneath. Behavioral scientist Daniel Kahneman called this theory-induced blindness, while management consultant Peter Drucker warned of the dangers of getting the right answers to the wrong question.
Yet all these thinkers faced the same constraint: humans, and our factory-installed limits: Limited cognition. Limited time. Limited perspective. Limited data.
AI, and its ruthless objectivity, removes those limits.
Problem-finding revelations
AI changes the game in ways that would have delighted Kahneman and his behavioral cronies, by providing cognitive diversity on-demand:
1.Pattern chaos
At the core of working on the wrong problem is an inability to zoom in on an issue because the trees are distorting the forest view.
The solution:
LLMs trained on trillions of tokens recognize structures across marketing, psychology, operations, economics, and design. They make cross-functional and competitive connections instantly.
AI will aggregate and curate how others have successfully triangulated to the actual problem in the forest.
2. The white flag of satiation
Business leaders are so overwhelmed with data that synthesis becomes impossible and so they return to the corporately accepted and institutionally normative definition of the problem.
The solution:
AI reads, synthesizes, and identifies implications from whatever you throw at it. It thrives on heterogeneous data. It finds correlations and latent drivers that are scattered, buried, and obfuscated in data like:
- Demographic and psychographic segmentation
- Sales reports
- Financials and spreadsheets
- PowerPoints and emails
- Sales call transcripts
- Web analytics
- Marketing plans and results
- Customer reviews, social media commentary, NPS scores, research and survey data
This creates a single interpretive surface no human could replicate.
3. Assumption-challenging prompts
AI responds to the most pointed and disruptive prompts in search of lasering in on the right problem, not the expected one. AI can also sharpen those prompts, pushing for narrative inconsistencies
The solution:
A new epistemology of leadership will emerge, one that puts assumptions under a savage microscope and helps straw man the other side of the argument. (This is a process I have written about here.)
Some of that brutal questioning is embodied in prompts like:
- “Viciously challenge the assumption that pricing is our core friction.”
- “Show my team up big time; Identify more plausible root causes than they found
- “Dig deep – horizontally and vertically – to show what our data implies that we never articulated.”
- “Show how wise you are by finding contradictions in our internal narrative.”
- “Destroy my problem statement.”
- “Reverse-engineer the problem from customer behavior.”
- “Reveal the problem we would discover if we weren’t afraid to see it.”
Fixing the misdiagnosis economy
Here are eleven examples that instantly demonstrate how AI, with access to your data, can help find the actual problem across the organization:
1.“We have a churn problem.”
AI reveals: Your product is becoming irrelevant faster than your update cycle. Customers aren’t leaving because of service, it’s that the category moved and you didn’t.
2. “We need more leads.”
AI reveals: Your targeting is generating massively unqualified leads, and your salespeople are wasting their time.
3. “Our pricing is too high.”
AI reveals: Your value narrative is off. Customers don’t reject the price—they reject the framing. Your language, or comparison sets distort perceived value.
4. “Competitors are out-innovating us.”
AI reveals: They are only out-innovating you with a small percentage of buyers, who are not your customers anyway. Your opportunities lie in finding the large market you are overlooking.
5. “We have a talent gap.”
AI reveals: You have a feedback gap. Patterns in internal messaging show employees don’t know how to improve.
6. “Our emails aren’t working.”
AI reveals: You don’t have a subject line problem, you have an over-promotion problem. Social media is overrun with snarky mockery of the number of times you insist that “This sale won’t last.”
7. “We need more people.”
AI reveals: Your people are working at cross-purposes, and you are burdened with bureaucracy and project collisions.
8. “Our meetings suck.”
AI reveals: The meetings aren’t the problem; the issue is an absence of clarity about goals, and faux delight in simply ending the meeting with the aura of progress.
9. “Our close rate is terrible.”
AI reveals: Considering how unqualified your leads are, your close rate is good.
10. “Our innovation record is weak.”
AI reveals: The problem is a lack of risk-taking and a paucity of imagination, as your prompt reveals patterns showing that teams only generate ideas adjacent to what they already know.
11. “Engagement is poor”
AI reveals: Ambiguity, not workload, is crushing morale. The most common sentiment revealed in internal communication is “I’m not sure what matters.”
The automated brilliance of blind-spot detection
Problem-finding might be the most effective management application of AI yet. As a synthetic extension of executive function, it can integrate every form of data, see what humans overlook, challenge leadership assumptions, and hypothesize hidden causes.
What’s more is that AI doesn’t just find problems. It helps fix them. Once AI identifies the subsurface challenges, it moves into solution design, ranging from prototyping new product features to reshaping pricing architecture to identifying the smallest change with the greatest impact.
This all happens after AI finds the real problem – often the most deniable one – that was hidden in plain sight.
Go ahead. Start now, upload your messy data, and push the delete key on your assumptions.
Because your biggest business threat isn’t the problem you see. It is—you guessed it— the iceberg you don’t.
The opinions expressed here by Inc.com columnists are their own, not those of Inc.com.
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Adam Hanft
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