What recruiters look for in AI Product Managers

The “AI product manager” has rapidly shifted from buzzword to business-critical role. As more candidates position themselves as AI-savvy, the challenge for employers is no longer awareness, it’s distinguishing genuine capability from surface-level literacy.

At Intelligent People, co-founder Doug Bates has a front-row seat to this shift. Alongside his work in recruitment, Doug also helps run Partner Up, a free product mentorship programme, and is a regular supporter of Women in Product and CPO Track, consistently giving back to the wider product community.

Through this work, he’s recently been fielding more and more questions about AI – from MBA students at London School of Economics (LSE) to senior Chief Product Officers and experienced product mentors. The theme is consistent: how is AI really changing the expectations of product managers, and how should companies assess that in hiring?

To unpack these questions, Doug sits down with journalist and former Daily Express writer, Liam O’Neill, to explore what recruiters and hiring managers should actually be looking for and how to test it in practice.

Liam O’Neill (LON): How has the definition of a “strong AI product manager” changed over the last few years?

Doug Bates (DB): The role has evolved significantly. A few years ago, AI was often treated as a buzzword, something candidates could reference without much real-world application. That’s no longer the case.

Today, strong AI product managers are people who have actually built AI-driven applications or meaningfully incorporated AI into products. That might include integrating large language models into platforms or delivering features like AI-generated summaries for content products.

In other words, it’s moved from theory to execution. Employers now expect hands-on experience, not just conceptual understanding.

LON: When assessing candidates, how do you distinguish between someone who simply understands AI concepts and someone who can actually build and ship AI-driven products?

DB: You have to stress-test what they’ve actually done.

Ask candidates to go deep into the products they’ve worked on, what exactly they built, what outcomes it delivered, and what their personal contribution was. It becomes quite clear, quite quickly, who has real experience versus surface-level knowledge.

Another emerging signal is whether they can “vibe code”- that is, whether they’re comfortable working hands-on with AI tools and collaborating closely with technical teams in a more fluid, experimental way. That practical fluency is becoming increasingly important.

LON: What practical interview methods or exercises are most effective for testing AI product management capability?

DB: Competency-based interviews are still the most effective foundation. You want structured questions that probe real experiences, how candidates approached problems, made decisions, and delivered results with AI in the mix.

If the role requires a more hands-on capability – especially around prototyping or working closely with AI tools – then a practical task at the final stage can be very valuable. That might involve evaluating an AI use case, designing a feature, or demonstrating how they would approach building something.

The key is to align the assessment with the actual demands of the role.

LON: Where do you most often see candidates overestimating their readiness to work on AI products?

DB: Interestingly, we don’t see a huge amount of overestimation in the traditional sense.

What we do see is a large proportion of candidates who haven’t yet fully grasped that AI is the future and that it will fundamentally change their role as product managers.

So, it’s less about overconfidence, and more about underestimating how important it is to adapt. The gap isn’t always skill – it’s mindset.

LON: As AI capabilities evolve so quickly, what qualities make a product manager adaptable enough to stay relevant in this field?

DB: It comes down to mindset and intellectual curiosity.

The best AI product managers have a flexible mind, they’re open to change and comfortable operating in uncertainty. They’re intellectually sharp, able to grasp complex concepts quickly, and most importantly, they have a genuine thirst for knowledge.

This space is moving incredibly fast. The people who succeed will be those who are constantly learning and evolving alongside it.

Final thoughts

What emerges from this conversation is not hype, but a clear shift in expectations. The era of AI as a talking point is over, replaced by a much more practical question for hiring managers: can this person actually build, ship, and deliver value with AI?

As Doug outlines, the strongest candidates are no longer defined by their ability to explain AI concepts, but by their proximity to execution. Whether it’s integrating large language models, launching AI-powered features, or working hands-on with emerging tools, experience now carries far more weight than theory.

For employers, that means interviews need to evolve. Competency-based questioning remains essential, but it must go deeper, probing tangible outcomes, individual contribution, and decision-making in real AI-driven scenarios. In some cases, introducing practical tasks or exercises at later stages can help separate those who can “talk AI” from those who can actually operate in it.

At the same time, there’s a quieter but equally important trend: not overconfidence, but hesitation. Many product managers are still underestimating how fundamentally AI will reshape their role. The real risk, as highlighted here, is not that candidates think they know more than they do – but that they haven’t yet engaged deeply enough with what’s coming.

There is also an important reality to acknowledge: AI is still new, and it is evolving at such a pace that everyone, from junior product managers to seasoned CPOs, is learning in real time. In that sense, hiring for AI capability is not purely about past experience; it is equally about mindset. Curiosity, adaptability, and a willingness to experiment are fast becoming the defining traits of those who will succeed.

Ultimately, while tools and technologies will continue to change, those underlying qualities remain constant. In a landscape this dynamic, the product managers who stay relevant will be those who keep learning, keep testing, and keep building.

This conversation is intended as a snapshot – a high-level look at how AI is changing product hiring today. For those looking to explore this in more depth, particularly from a hiring perspective, you can contact Doug Bates directly or click below to book a meeting with him.