What’s the Real Cost of a Bad PRD?

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Over the years, I’ve seen many teams struggle with missed deadlines, rework, and scope creep. Often, it came down to one root problem: unclear or incomplete product requirements.

We all know they slow us down. But how much?

I recently ran an experiment to quantify the impact. PMs created PRDs in different ways, and I simulated engineering costs to measure rework, scope changes, and delivery speed.

Here’s what I found:

• PRDs with free-form AI-assisted drafting had 42% “late-discovered work.”
• PRDs with structured AI assistance had just 8%.
• The difference translated into hundreds of thousands of dollars per feature and resulted in more than an 80% faster implementation.

The takeaway: PRD quality has a direct impact on cost and speed. And the way we use AI matters a lot. Treating requirements with the same rigor as code reviews isn’t just a best practice; it’s a business necessity.

For those interested, join the conversation here.

This experiment was part of my work on Wisary, where we explore how AI supports teams in writing clearer, more effective PRDs.

I’m curious, how do your teams approach measuring PRD quality? Do you have methods for catching assumptions or gaps early?