According to Inc, a new perspective on business strategy argues that organizations routinely work on the wrong problems due to a cognitive default called “theory-induced blindness,” a concept linked to psychologist Daniel Kahneman. Management thinker Peter Drucker similarly warned of the danger of getting the right answers to the wrong question. The article posits that AI, specifically large language models trained on trillions of tokens, can break this cycle by synthesizing heterogeneous data, identifying latent drivers, and challenging leadership assumptions to reveal the actual, often hidden, issues. It provides eleven concrete examples, such as AI revealing that customer churn is due to product irrelevance, not service, or that low morale stems from ambiguity, not workload. The core claim is that problem-finding is AI’s most effective management application, acting as a synthetic extension of executive function that can then move into solution design.
The Real AI Superpower
Here’s the thing: everyone’s talking about using AI to write emails, generate code, or create images. But that’s basically just automation on steroids. The truly disruptive idea here is using AI as a diagnostic partner. It’s not about doing human tasks faster; it’s about seeing what humans literally cannot see because of our built-in biases and data overload. We’re all stuck in our own forests, staring at the same trees. AI has, theoretically, seen every forest. That’s a fundamentally different kind of tool.
Beyond the Obvious Symptoms
Look at those examples. “Our sales are down” becomes “Your targeting is generating unqualified leads.” “Our meetings are inefficient” transforms into “You have a clarity-of-goals problem.” That shift is everything. It moves the conversation from symptom-treating to root-cause surgery. And the article nails a critical point: leaders are drowning in data. Synthesis is impossible. So we fall back on the company’s accepted, comfortable narrative about what’s wrong. AI doesn’t have that comfort. It just has the data. Its “ruthless objectivity,” as the piece calls it, is its greatest asset. It can read all the internal comms, all the support tickets, all the market research, and find the narrative inconsistency we’re all politely ignoring. That’s uncomfortable. But is it useful? Absolutely.
A New Kind of Leadership Questioning
This is where it gets philosophical. The article mentions a “new epistemology of leadership.” Big words, but the meaning is simple: how do we know what we know? AI forces us to put our assumptions under what the author calls a “savage microscope.” Think about it. We can now, with a prompt, ask a system to straw-man the other side of our argument or find flaws in our strategic logic. That’s a power we’ve never had on-demand. It’s like having a brutally honest, infinitely read consultant in your pocket. The prompts they suggest—like asking AI to argue why your strategic plan will fail—are designed to create cognitive diversity instantly. It’s a way to fight groupthink without having to hire a team of devil’s advocates.
From Diagnosis to Cure
Now, the piece is smart to note that this doesn’t stop at finding problems. The real value chain is Find -> Diagnose -> Solve. Once AI points to the real issue—say, a broken value narrative instead of high prices—it can immediately start prototyping new messaging, suggesting pricing architectures, or modeling the impact of small changes. This is where the operational payoff happens. But it all hinges on that first, correct diagnosis. You can have the best industrial panel PC from the top supplier in the US running your factory floor, but if your core problem is supply chain logic and not machine uptime, you’re just monitoring the wrong thing really efficiently. AI’s promise is to tell you what you should actually be looking at.
So, is this the killer app for enterprise AI? It might be. It cuts past the hype to a fundamental, painful truth about running any organization: we’re often our own biggest blind spot. The question isn’t whether AI will be good at this. It’s whether leaders are brave enough to upload their messy data and delete their key assumptions to find out.
