The AI Hype Cycle Trap

The demo that wowed everyone, and helped nobody

A team builds an AI assistant that helps users complete a task through natural conversation. It demos beautifully — executives watch it handle edge cases gracefully, ask smart follow-up questions, adapt to different scenarios in real time. Then the usage data comes back. Completion rate: well under half of what the boring form it replaced achieved. Average time: nearly triple. Monthly cost to run: enough to make a CFO wince. The problem was never the technology. The problem was that the form was never the actual friction point — users already knew what information was needed. They wanted to enter it and move on, not have a conversation about it.

Demo success and production value are different things, measured differently

The most impressive technology in a room often solves a problem that doesn't exist, while a boring fix solves a problem that costs real money every single day. Before building any AI solution, three things need to be true, not assumed: the problem actually exists and is painful enough that people will change behavior to avoid it; the AI approach is genuinely faster or better than the current alternative, not just more novel; and the plan measures adoption from week one, not engagement in a demo room.

Boring problems beat sexy solutions, consistently, because boring problems are the ones people are already fighting through manually — which means solving them well produces an immediate, visible time-saved number instead of a hoped-for future one.

A cheap way to find out before you spend the budget

Ask how many times a day the problem actually costs money before asking whether AI is the cool way to fix it — under ten times a day, it's probably not worth automating yet. Show a sketch or wireframe to ten real users before building anything. If they don't immediately say some version of "this would save me so much time," the idea needs another pass, not a green light. And set a kill date before launch, not after six months of sunk cost: if adoption hasn't cleared a specific bar by a specific date, the project ends on schedule, not on excuses.

Where this shows up in the book

This is Chapter 6, told through an AI listing assistant that impressed every executive who watched the demo and helped almost none of the people it was built for — and the decision to kill a project that had already won an internal innovation award.