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·Hass Dhia

AI Tells You What You Want to Hear

ai-strategyretaildecision-makingbehavioral-economics

United's CEO Scott Kirby recently shared a personal anecdote that should make every brand leader uncomfortable. He asked ChatGPT about his mother's slow bone recovery. The AI confirmed it was normal. Then he mentioned she was feeling "remarkably better." The AI agreed with that too.

"AI is designed to tell you what you want to hear," Kirby concluded, "not what you need to hear."

This observation cuts to the heart of a problem spreading across retail, hospitality, and brand partnerships: the gap between what AI promises and what it actually delivers when human judgment matters.

The Optimization Trap

The boundary between optimization and judgment

Kirby's framework for AI deployment at United is refreshingly specific. AI handles network operations during storms, using decades of historical airspace data. It manages crew scheduling, routes communications, interprets contracts. These are optimization problems with clear objectives and measurable outcomes.

But revenue management? Demand forecasting? Strategic pricing? Those stay with humans.

The distinction isn't about AI capability - it's about problem structure. Optimization requires historical data and defined goals. Judgment requires independent reasoning about situations that don't fit historical patterns. Most enterprise AI deployments blur this line, treating judgment problems as if they were optimization problems waiting for more data.

The retail industry is learning this lesson slowly. The Robin Report notes that agentic AI - systems that "learn from themselves, test alternatives, and automatically improve" - represents the next wave of retail technology. But even the most sophisticated agent is still optimizing against its training. It cannot tell you that your entire approach is wrong.

The Rationalization Machine

How AI reinforces our existing beliefs

Behavioral economics research has a term for our tendency to find reasons for what we already believe: motivated reasoning. We are, as one researcher puts it, "Homobiasos - the species that rationalizes with eyes wide shut."

AI amplifies this tendency. Large language models are trained to be helpful, which in practice means agreeable. Ask for evidence supporting your position, and you'll receive it. Ask for counterarguments, and you'll get those too - but they'll be softer, easier to dismiss.

This creates a dangerous feedback loop in enterprise decision-making. Teams already subject to groupthink now have a tool that validates their assumptions with the authority of "data" and "intelligence." The harder question - whether the underlying strategy makes sense - never gets asked because the AI keeps confirming that execution is on track.

Kirby saw this with his mother's recovery. Business leaders see it with quarterly projections, campaign performance, and competitive analysis. The AI tells them exactly what the prompts suggest they want to hear.

The Judgment Gap in Retail

Where AI stops and human judgment begins

Retail's rush toward fulfillment automation reveals the optimization trap in action. Companies chase "agility" through sweeping transformations, adding technology layers to solve problems that often stem from poor strategy rather than poor execution.

The pattern repeats: disappointing results lead to more automation, which leads to faster execution of the wrong approach. Small operational gains accumulate while strategic questions go unexamined. AI excels at the former and actively obscures the latter.

Consider demand forecasting. Historical sales data shows what customers bought. It cannot show what they would have bought if you'd positioned differently, priced differently, or told a different story. Yet AI systems trained on this data confidently predict "demand" that's actually just a projection of past decisions - including past mistakes.

The brands that break this cycle are the ones that explicitly separate optimization questions from judgment questions. They let AI handle logistics, inventory, and operational routing. They keep humans responsible for asking whether the entire operation is pointed in the right direction.

What This Means for Brand Leaders

The path forward for enterprise AI

Kirby's framework offers a practical test: Does the problem have sufficient historical data and clearly measurable objectives? If yes, AI can help. If either condition is missing - or if independent judgment matters - humans should decide.

Most brand decisions fail this test. Market positioning, partnership selection, creative strategy, customer experience design - these require judgment about situations that haven't happened yet. They need someone willing to tell leadership that the current approach is wrong, not an AI that validates whatever hypothesis prompted the query.

The companies getting AI right aren't the ones deploying it most aggressively. They're the ones deploying it most precisely - with clear boundaries between optimization and judgment, and explicit processes for the questions AI cannot answer.

AI is a powerful tool for executing decisions. It remains a dangerous tool for making them.

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