As AI weaves itself into nearly every corner of business—operations, customer support, engineering, marketing—it’s easy to overlook the strategic glue holding it all together: product management.
Some executives are asking: if AI can analyze customer data, suggest features, and even run A/B tests, do we still need product managers?
The short answer is yes—more than ever. While AI is transforming how PMs work, it’s also elevating what companies need from them. The best product leaders will shift focus from repetitive analysis to the deeply human work of eliciting unmet needs, understanding customer problems, aligning teams, and driving strategic outcomes.
McKinsey estimates that up to 45% of workplace tasks are automatable with existing technology—including many routine activities in product management. But forward-thinking leaders aren’t asking whether AI will replace PMs. They’re asking how AI can extend their impact.
As Marty Cagan of the Silicon Valley Product Group writes, the role of product managers isn’t vanishing. It’s shifting—away from the busywork machines can handle, and toward high-leverage skills only humans bring.
How AI Supports—But Doesn’t Replace—Product Work
AI is increasingly capable of helping product teams move faster and make sense of large volumes of data. But it’s important to separate support from substitution. The best product teams don’t delegate decisions to AI—they use it to inform better ones.
AI tools can assist in several areas:
- Synthesizing customer feedback: Platforms like MonkeyLearn and Clarabridge help teams process large volumes of qualitative feedback—surfacing patterns in support tickets, reviews, and interviews that might otherwise be missed.
- Identifying usage trends: Tools like Mixpanel and Heap can flag underused features, user drop-off points, or spikes in activity. But interpreting that data—understanding why something is happening—still falls to the product team.
- Highlighting potential risks and dependencies: Platforms like Productboard are starting to integrate AI to forecast delivery risks or suggest adjustments based on velocity and effort estimates. These can be helpful signals, not final answers.
- Supporting experimentation at scale: AI-driven testing platforms can automate A/B test design, run variations simultaneously, and optimize traffic allocation using multi-armed bandits. But deciding what to test and how to act on results still requires human judgment and product thinking.
Where teams sometimes go astray is in expecting AI to "prioritize the backlog" or "build the roadmap." Those aren’t tasks that can be automated because they depend on product vision, company strategy, customer understanding, and team constraints. Frameworks like RICE can help structure thinking—but they don’t make the decision for you.
In short: AI can surface inputs. It can automate analysis. But the actual prioritization and strategic planning work still requires a high-context, high-collaboration team. That’s not overhead—that’s the work.
What Only Humans Can Do
AI is powerful, but it’s not intuitive. It doesn’t read the room. It can’t build trust or make judgment calls when the data is ambiguous. Those are the moments where strong product managers stand out—and where they deliver the most value.
1. Customer Understanding and Trust
AI can synthesize what users say. PMs understand why they say it. That human connection—built through conversations, observation, and empathy—reveals the insights that algorithms miss.
As Product Focus puts it: “The ability to truly empathize with customer pain points comes from human connection and conversation.”
Customer trust isn’t just a nice-to-have. It’s how teams uncover deeper needs, validate direction, and build better products.
2. Problem Framing and Strategic Judgment
Data might highlight a symptom—like a drop in engagement—but defining the real problem requires synthesis and experience. Great PMs listen between the lines, connect the dots, and weigh tradeoffs that aren’t captured in a dashboard.
Teresa Torres reminds us that reviewing a research summary is not the same as conducting synthesis firsthand. The framing, insight, and strategic choices that emerge from that work still require a human lens.
3. Stakeholder Alignment and Influence
PMs sit at the intersection of engineering, design, marketing, sales, and leadership. That’s a messy, human space—full of competing incentives, shifting priorities, and unspoken concerns. AI can propose a plan. But getting people on board? That’s relationship work.
As Product School writes: “AI falls short in relationship building. It can’t read tone, understand office politics, or inspire confidence.”
One product leader summed it up on Reddit: “Anyone who thinks AI can replace a PM has never been in a prioritization meeting.”
4. Leadership Without Authority
Product managers rarely manage people directly. Instead, they lead through influence—crafting a vision, creating clarity, and helping teams navigate ambiguity.
AI tools might suggest a roadmap based on historical data or usage trends—but they can’t determine the right roadmap. Deciding what to build, in what order, and why still requires product managers to weigh customer value, business impact, feasibility, and usability. These are judgment calls rooted in context—things no AI model has.
It takes human oversight, not just leadership, to shape those inputs into a roadmap that actually makes sense for the business and the user. The product team’s job isn’t to execute whatever AI proposes—it’s to make strategic, customer-informed decisions and align the organization around them.
Why This Matters for Business Leaders
If you’re a CEO, founder, or GM, this shift isn’t just a tech trend—it’s a strategic decision. Your product managers are no longer just backlog owners. They’re customer translators, strategy shapers, and team connectors.
To get the most from AI, you’ll need product leaders who know how to:
- Use AI as input, not instruction
- Stay close to customers and uncover deeper needs
- Build alignment across departments and teams
- Make tough calls when the data is murky or contradictory
When product managers are stuck doing tactical work, your company risks moving fast in the wrong direction. That risk is amplified by AI as the cost of moving fast is reduced. But when they’re empowered to focus on strategy, customer insight, and cross-functional alignment, the business moves more decisively—and delivers outcomes that drive real progress and impact.
The companies that treat PMs as feature shippers will fall behind. The ones that invest in empowered product teams—like those described in SVPG’s Transformed—will be best positioned to innovate, adapt, and grow.
The Human Work Behind Great Products
AI will continue to change the how of product management—but not the why. The role isn’t disappearing; it’s evolving.
The companies that succeed in this new era won’t be the ones that lean on automation alone. They’ll be the ones that pair AI-powered scale with product leaders who bring context, connection, and strategic clarity. Because at the end of the day, delivering real customer value still takes good judgment, hard conversations, and a shared sense of purpose—none of which come from an algorithm.
Lisa Hagen is Founder & Product Coach at Ready Steady, where she leverages twenty years of experience to help startups and growth teams align product strategy, empower cross-functional teams, and drive execution excellence. She co-authored Ready Steady Grow, a hands-on guide built around strategic alignment, customer focus, and outcomes-driven principles and is on the Board of Directors of the Boston Product Management Association.
Bob Levy is Founder & CEO of Immersion Analytics, where he applies patented visualization technology to simplify complex data and AI. A tech industry veteran, he brings experience from MathWorks, IBM, and Rational Software, and was the founding president of the Boston Product Management Association, where he now serves on the Board of Directors. He also serves on the board of OSINT leader LifeRaft and advises startups including nDash.
