AI in Product Management: What 379 Product Managers Revealed About the New Reality

PRODUCTBOARD

22nd October 2025Product Management, Product Leaders, Product Excellence

The speculation phase is over. AI adoption in product management has moved from pilot programs to full-scale integration—and the data reveals exactly how teams are adapting, which skills matter most, and where governance is falling dangerously behind.

We partnered with independent research firm UserEvidence to survey 379 product professionals from enterprise organizations with 500+ employees. What we found challenges conventional wisdom about AI's role in product teams and reveals a field in dramatic transition.

Want the complete dataset, industry breakdowns, and strategic recommendations? Download the full State of AI in Product Management report →

Finding #1: AI Adoption Is Universal (But Maturity Varies Wildly)

Every product team we surveyed is using AI tools. Not 99%. 100%.

More striking: 96% report using AI consistently, with nearly half describing it as "deeply embedded" into their workflows. For context, generative AI reached 39% adoption among U.S. adults just two years after ChatGPT's launch—faster than the internet or personal computers.

Among product professionals specifically, 94% use AI daily or often. Zero respondents said they don't use it at all.

But usage patterns tell a more nuanced story. Individual PMs prioritize different AI use cases than their teams do.

As Mark Poole, Senior Product Manager at Turnitin, explains: "AI is freeing us up to spend more time on the difficult decisions and analysis, and less time on what I call 'paperwork'—managing and prioritizing tasks, tickets, and stories."

Finding #2: AI Is Saving 33+ Hours—And Redefining the Product Manager Role

The time savings are substantial. Product professionals report saving an average of 4 hours per task with AI, totaling approximately 33 hours across their core functions.

The top time-savers? Creating presentations, writing PRDs, competitive research, and roadmap creation.

But here's what matters more: 98% of respondents said they've changed or are planning to change their team structures because of AI. The role of Product Manager isn't just evolving—it's being fundamentally rewritten.

The full AI in Product Management report includes detailed breakdowns of time savings by task, seniority level, and industry—revealing which teams are seeing the most dramatic efficiency gains.

Finding #3: Strategic Thinking Just Became the Most Critical PM Skill

As AI handles more tactical work, the skills gap is widening between PMs who can think strategically and those who can't.

When asked which skills are becoming increasingly important, product professionals selected an average of 3.5 options. The top four:

Data literacy (58%)
Synthesizing customer insights (54%)
Systems-level thinking (53%)
Strategic thinking (52%)

Adam Judelson, former Head of Product at Palantir, frames it this way: "A lot of the less technical work that a product manager has to do is arguably done better, or at least at an average or slightly above-average level, by generative AI systems. A lot of the new alpha is in deeply understanding what's happening in generative AI, applying that to new situations, testing out those tools, and figuring out what they can actually do."

The implication: AI doesn't replace PM judgment. It raises the bar for what good judgment looks like.

Finding #4: The AI Tech Stack Is Fragmented (And Creating New Risks)

Product teams aren't standardizing on a single AI tool. 88% use two or more different AI models. For AI prototyping specifically, 60% use two different tools, with no clear market leader.

This fragmentation creates flexibility but introduces serious challenges around governance, context, and integration. And here's the troubling part: While 100% of respondents use AI tools, only 65% say their company has a documented AI policy.

More than one-third are operating in a governance vacuum.

IBM's 2025 Cost of a Data Breach Report found that the average cost of an AI-related data breach is $4.46 million—and 97% of companies that experienced one lacked proper access controls.

The teams seeing the deepest AI adoption? Those with centralized governance.

Curious which governance models work best? The full State of AI in Product Management report breaks down AI ownership by role, company size, and maturity level.

Finding #5: There's No Universal Formula for Measuring AI ROI

Product teams track AI impact through multiple lenses. Respondents use an average of three different ROI metrics, with the most common being:

Increased output
Improved product quality or customer satisfaction
Cost savings

Only 40% currently measure AI ROI through broader business outcomes like ARR—suggesting most teams are still in the "efficiency gains" phase rather than connecting AI to strategic business impact.

The measurement approaches also vary significantly by industry, with software companies prioritizing output, financial services emphasizing quality, and manufacturing focusing on cost reduction.

What Product Teams Should Do Next

AI adoption in product management has crossed the chasm. The question isn't whether your team should use AI. It's whether you're using it as effectively as your competitors.

The teams seeing the most value are:

Establishing centralized governance before scaling adoption
Investing in strategic skills development, not just tool training
Measuring impact through business outcomes, not just efficiency metrics
Building purpose-built AI workflows rather than duct-taping general tools together

Ready to see how your team compares?

Download the complete State of AI in Product Management report for: