Product management is built around getting the answer to one question right.
What’s worth doing?
That’s what I’m thinking about every day.
The complexity goes into the systems that help answer that question.
And a lot of product management exists to compensate for the fact that the systems can’t run themselves.
AI has the potential to turn some of those systems into infrastructure.
We build humans into our workflow
A customer asks for something.
Someone notices.
Someone works out if it’s been asked for before.
Someone finds more context about that account. Is there any previous research? Are there existing roadmap items and strategic initiatives that this relates to?
Is it a feature request? A bug? A problem with process? Or merely something to take a note of.
And the question I demand for everything.
So what?
The systems underneath these questions and judgments are passive. Jira doesn’t notice that three conversations describe the same problem. Linear can’t decide that an opportunity lacks the evidence to prioritize. Our customer knowledge base doesn’t tell us it thinks the roadmap needs to change when it gets new information.
People do all those things.
AI is changing that.
The system doesn’t need to be passive
We can add an active system to the same operating model.
A new piece of customer information comes in.
Our system recognizes that something connects to an existing problem. It finds the evidence it needs. It determines the broader strategic context. It puts together a case.
I don’t mean an extensive, overwritten, AI-generated PRD.
A short argument. A ticket.
What happened? Who cares? How much evidence is there? Does this change any of our existing thinking? Why should we care, and why now?
Another agent tries to knock down that argument.
If the evidence is weak, it stops there.
Strong evidence gets past that gate. It reaches the PM.
The product manager is still there. They’ve simply been moved closer to the actual decision.
Two different jobs, one of them is product
A lot of the AI-workflow thinking out there is about optimizing the execution.
Writing the ticket. Writing a requirements doc. Writing the weekly status update. That’s project management. It creates useful artifacts and it keeps work moving.
But it doesn’t tell you if what’s being executed is right.
Product judgment is deciding what evidence counts. What criteria a case has to meet before it’s worth a second conversation. What hypothesis sits behind the decision: if we do this, what do we expect to happen?
It’s also about deciding which of those calls a system can make on its own, and which still require a person.
Cheap generation doesn’t remove the need to choose
AI can produce plausible work fast and cheap.
It can analyze a thousand signals and write fifty business cases. Generate twenty initiatives. Build a backlog with forty-five items marked High Priority.
Those backlogs were always nonsense.
AI just generates the nonsense faster.
Our bottleneck isn’t simply execution capacity. It’s decision-making.
Generation got cheap. The decisions about what’s worth doing are still scarce.
That makes them more important.
Without evidence thresholds, we’re asking for extensive AI slop. Explicit caps on what can be considered, what can move forward, and how much work the system can introduce aren’t process bureaucracy.
They’re prioritization infrastructure.
When the case for anything costs almost nothing to produce, choosing what deserves attention matters even more.
A track record for judgment
By being explicit about our decision layers, we can measure them.
How often did new evidence change an existing decision?
How often did the PM overturn the system’s recommendation?
Which cases repeatedly succeeded? Which repeatedly failed? Why?
That creates a track record of judgment for both the person and the machine. That’s the start of a real model for AI trust. Keep getting things right and the system can move straightforward cases forward without human approval. If the error rate rises, reduce the autonomy.
We can become more nuanced than “do we trust AI?”
Instead:
Which decisions has this system earned the right to make?
The PM moves up a level
Product management is no less important.
The parts being exposed are the parts that weren’t product management at all.
Moving tickets. Collecting status. Drawing a link between two customer requests.
Those aren’t strategy, or judgment, or prioritization. They were things we had to do first because the tools were inert.
The tools are already getting less inert.
Our job moves up a level.
Define the evidence. Set the criteria. Decide where we’re comfortable with automation. Look deeper at the exceptions. Keep refining our model against reality.
Continue to demand:
So what?
Not traffic control. Systems design.
We spend a lot of time looking at how AI can make everyone in a project work faster.
The more important question is whether it can help us get what’s worth doing right in the first place.
Further reading:
Why I want my AI projects blessed by Jesuits - on what history can teach us about applying judgment.
Johncox, A. Why AI can’t replace product thinking (as told by 5 product experts). balsamiq, Oct 2025.
Pearson, J, et al. Examining human reliance on artificial intelligence in decision making. Nature Scientific Reports, Feb 2026.
Balogun, H. The Misunderstood Identity: Product Manager vs. Project Manager. Medium, Oct 2024.
Article photo by Denys Nevozhai on Unsplash.