Why AI Products Fail: The Three Hidden Gulfs That Kill Even Simple Applications

How a “simple” email assistant reveals the complexity gaps that doom most AI products

The product demo looked flawless. Our AI-powered email assistant could extract sender names, summarize key points, and categorize messages with impressive accuracy. The engineering team was confident, the stakeholders were excited, and we were ready to ship.

Then we launched to our first 1,000 users.

Within 48 hours, support tickets started flooding in. The AI was tagging the wrong people as senders, creating summaries that missed critical context, and categorizing urgent requests as low-priority newsletters. What looked like a simple three-step process in our demo had become a nightmare of edge cases and unexpected failures.

That’s when I learned a hard truth about AI product development: most AI products don’t fail because the technology is inadequate. They fail because teams never properly defined what “working” means until it stops working.

The Illusion of Simplicity

Let me walk you through what seemed like a straightforward example. We were building an email assistant with three basic functions: