Becoming a Product Manager in 2026

McKinsey found demand for AI fluency in job postings had grown nearly sevenfold in two years, mostly in management and business roles. And yet, in the teams I work with, (including one building machine learning and GenAI solutions to transform financial crime detection) our hardest problems still aren't technical. They're about building the right thing, managing dependencies, and connecting technology to the business. That gap between expectation and reality? That's where product management lives in 2026.

The market has polarised

TrueUp lists 6,798 open PM roles globally right now. 1,138 of them explicitly AI-focused. That's one in six. Senior PM hiring is up 20% year on year, leadership roles up 22%, while mid-level and associate positions remain the soft part of the market. Every open role ties directly to revenue generation, risk reduction, or AI adoption. The comfortable generalist middle ground is shrinking fast and it's worth being honest with yourself about which side of that line you're on.

People still run the world.

Most of the problems I help clients solve aren't technical, they're human. Prioritisation battles, misaligned incentives, leaders who are genuinely committed to change but struggle to behave like it. The most important PM skills in 2026 aren't new. They're the foundational capabilities that have always separated great PMs from average ones and AI is making that gap wider, not narrower. The ability to read a room, build trust, and bring people with you is still the thing that separates good from great. AI can't practise that for you.

AI literacy matters, but it's being misread.

Working with AI is table stakes now. The real skill is knowing which tool fits the job, how to prompt it well, how to iterate, and how to honestly judge whether the output is actually any good. You also need enough understanding of how models work and crucially, where they fail to make sound calls. You don't need to be technical. But you do need to be honest about your own limitations. Learn the core skills yourself first, then use AI to move faster. Not the other way around.

Volume is not quality.

AI makes it easier than ever to produce a strategy deck, a research summary, a PRD. What it isn't doing is increasing the quantity of quality. Noise has always been the real problem in product — not lack of output. My career has taught me that just because you can doesn't mean you should. Restraint and judgement still beat abundance every time.

Accountability has shifted.

As AI absorbs more of the execution work, PMs are judged on the quality of their decisions and outcomes not their artefacts. There are fewer places to hide. The thinking behind the work is still hard. And it's getting harder to disguise when it isn't there.

The skills haven't changed. The tolerance for not having them has.

References

• McKinsey & Company — AI Adoption in the Workplace (2025) — AI fluency demand in job postings

• TrueUp — Product Manager Job Tracker (May 2026) — trueup.io/product

• TrueUp — AI Product Manager Job Tracker (May 2026) — trueup.io/ai-product

• Lenny Rachitsky — State of the Product Job Market, Early 2026 — lennysnewsletter.com

• Productside — The AI Product Manager Skills Every PM Needs in 2026 — productside.com

• University of Wisconsin — AI Skills Drive Job Growth in Weak Hiring Market (2026) — uwex.wisconsin.edu