You used Claude to write a PRD in ten minutes. It was well-structured, comprehensive, included acceptance criteria and success metrics. It was also completely wrong for what your team could actually ship.
This is the reality of AI for product management right now: there's a lot of noise, and the signal is getting lost.
Most "AI for product management" tools fall into one clear category - they help you produce artifacts faster. Write a PRD. Generate user stories. Summarize meeting notes. Draft a competitive analysis. Create wireframe copy. If the hard part of product management was the typing, these tools would have solved it. And yes, there's genuine value here. A PRD written in ten minutes is better than a PRD written never. A template you can edit in two minutes beats starting from a blank page.
But if you've been in product long enough, you know that writing faster isn't the problem you actually have.
The hard part of product management is making the right decision when you have incomplete information. Should we build this feature or that one? Is this request aligned with our strategy? Can we actually ship this in Q2? Do we have the technical capacity? What's the right scope? What should we say no to? These decisions sit at the intersection of what customers want, what we can build, what the market will pay for, and what aligns with where we're going.
No AI tool that helps you write a PRD faster is helping you make these decisions better.
The distinction matters because it changes everything about what product management AI should actually do.
Artifact generation tools are useful. I use them. They save time on the overhead work - the stuff that eats calendar time and energy without moving the needle on decisions. They're genuinely good at pattern-matching against thousands of PRDs and wireframes and user stories that exist on the internet, and applying those patterns to your specific context. If you need a template, they're fast.
But they can't answer the questions that actually matter.
Is this technically feasible in the timeline we're talking about? That requires understanding your codebase - not in theory, but in reality. Your specific architecture, your technical debt, your team's velocity, what's actually deployable. Claude can't see your codebase. It's trained on public code samples. It doesn't know your system.
What features do we already have that we're not leveraging? What gaps exist relative to competitors? This requires a real inventory of what you've actually built. Not a sales narrative of what you claim you've built, but what's actually in your system, what's documented, what's used by customers.
Why do our estimates keep slipping on this type of work? This requires understanding patterns in your actual delivery - what work takes longer than expected, what always has hidden complexity, what estimates have consistently been wrong. That's historical data that AI doesn't have.
Who owns this part of the codebase? Who will this change affect? These are organizational questions. They require knowing your system and your team.
This is the gap between AI that makes you faster and AI that makes you smarter. The former helps you produce work products. The latter helps you understand reality well enough to make better decisions.
Decision intelligence looks different. It's grounded in your actual codebase, your actual customer data, your actual historical performance. It surfaces real tradeoffs - not hypothetical ones. When you ask "is this feasible in Q2?" the answer is tied to your specific technical reality, not a general statement about feasibility.
The decisions that AI can help you make better right now are concrete ones, not abstract ones. Concrete decisions have evidence. They have a real answer, not a spectrum of possibilities. "Is this technically feasible?" has an answer - your codebase either supports it or it doesn't. "What features do we have that we're not leveraging?" has an answer - you can count them. "What's the right scope for this?" is harder - that's more spectrum - but even that gets clearer when you have real information about what your system can and can't do.
When you're looking at AI tools for product management, this is the distinction to look for. Does it help you move faster? Or does it help you decide better? Most will claim both, but they're not the same thing.
The artifact generation tools will continue to get better. ChatGPT will write better PRDs next year. The documents will be faster to produce. But they won't be smarter. They won't know your system any better.
The tools that matter in 2026 are the ones built on a different foundation - the ones that understand your specific reality. Your codebase. Your feature inventory. Your technical constraints. Your team's actual velocity. Your customer data. When AI is grounded in this reality, it stops being a fancy template engine and becomes something that actually helps you decide.
The best product teams I know make decisions differently now. They're not starting with hypotheticals. They're starting with data - real data about what they have, what they can build, what customers want, what's working and what isn't. They're using AI to synthesize that data into clearer pictures and better questions. They're moving faster not because they're typing faster, but because they're making smarter decisions sooner.
That's the future of AI for product management. Not faster typing. Smarter decisions.