Why AI PRDs Are an Expensive Trap

Ask most PM communities and they will tell you AI is a huge productivity boost for PRD drafting, helping to structure messy thoughts into polished documents. But this is a dangerous illusion. The real danger of AI-generated PRDs isn't that they are poorly written. It is that they make unexamined decisions look incredibly well-reasoned.

When you use generative AI to write your requirements, you aren't actually speeding up your product development. You are just shifting the burden of decision-making downstream to your engineering team. Cheap building without sharp deciding just means shipping the wrong things faster.

The Illusion of Progress

In my previous role as a Senior PM at a fast-paced software company, we used AI-based solutions to write user stories based on a predefined format and a corpus of organizational context. On the surface, it looked like a massive win. It created the illusion of progress and comprehensive work, producing detailed and polished tasks.

In reality, these were walls of text. They lacked real detail and nuance. By automating the writing, we artificially reduced the need to think and make informed decisions. We ended up with documents that nobody read. Worse yet, developers went ahead and implemented work they did not understand. Nobody really owned the requirements in the true sense of ownership and responsibility.

The 1.5-Month Blindspot

I learned this lesson the hard way. I was sitting in a room with the senior developer leading our technical design, discussing a scoring and attribution algorithm. As we went through the details, we suddenly realized that the solution we had already implemented halfway-spending almost 1.5 months building-ignored an obvious use case. We had to rebuild almost the entire feature before we could go live.

This massive waste of engineering hours could have been prevented in advance if the right questions had been asked. Writing the document by hand would have been slower and clunkier at first, but it would have saved time overall because it would have forced us to actually think.

Writing by hand forces you to confront the gaps. AI templates let you gloss over them with professional-sounding prose.

Why AI Reverts to the Mean

When we were working on Beam's question engine, we tried delegating the kind of questions being asked, the shape of the general workflow, and the amount of pushback the engine would use. The result? The AI always reverted to the mean. It gave plain vanilla answers.

An AI cannot navigate messy, non-obvious trade-offs because it is trained on the average of what already exists. Only by sitting down, distilling our personal professional experience, and deeply studying the literature were we able to elevate the quality of the engine above the safe average.

When AI templates win

AI-generated templates are fine when you are building standard, well-understood SaaS features with zero technical risk. If you are building a basic CRUD portal or a standard settings page, an LLM can save you time by generating boilerplate user stories. But the moment you are building something novel, the AI's lack of real product intuition becomes a liability.

Think deeper. Decide faster.