Same title, different jobs: AI PM interview notes 01

I’ve done a few rounds of AI PM interviews lately, and there were some questions that stumped me, or that I only figured out after the interview was over.

I want to turn them into a series, one question per post. These aren’t model answers—they’re my interview notes.

Part 1: same title, different jobs. Two roles can both be called AI PM and still be hiring for different people.

01 | Enterprise delivery

These roles usually deal with lots of customers, business teams and internal stakeholders.

- Several customers each say their request is the most important. How do you prioritize?

- Commitments have already been made externally, but resources are limited. What do you do?

- Requirements keep changing. How do you control scope and expectations?

Product judgment matters here too, but expectation management, trade-offs and complex collaboration are a big part of the job in their own right.

02 | Product / feature

The questions are closer to discovery, prioritization and outcome:

- Who has this problem?

- Why is it worth solving?

- Is AI a better fit than rules or regular automation?

- Once it ships, how do you tell whether it works?

The product’s stage changes the focus too. A mature module cares more about adoption, quality and ongoing improvement; at 0→1, you might not even know yet which problem is worth solving.

03 | Platform

If the direct users are internal engineers or product teams, the PM has to think about more than whether a given use case has business value. Also:

- Which capabilities are worth building once, centrally?

- How do you cut down on duplicated work?

- How do you help other teams get AI applications into production faster and more safely?

The value of an end-user product usually shows up more directly in business results; the value of a platform shows up more in helping those products get built faster, more safely and more reliably.

04 | Exploration

Then there are roles where the company knows AI matters but isn’t sure which workflow it belongs in.

Here the PM isn’t starting with a roadmap. The first job is to find a problem worth solving, and then decide whether AI is the right fit.

These aren’t strict, mutually exclusive categories—just a few different centers of gravity I saw across these interviews. What tells you more than the job title:

- What does it own?

- Who are the direct users?

- What stage is the product at?

- What are this PM’s most important decisions?

The skills that matter differ accordingly:

- Enterprise delivery leans on stakeholder and expectation management;

- Product/feature leans on discovery, prioritization and outcome;

- Platform leans on technical depth and system thinking;

- Exploration leans on the ability to handle ambiguous problems.