Product Management Busy Work is Over
New AI tools are lifting the weight, but PMs still have to lead towards outcomes
Making the Decisions that Matter
Felt even more acutely in recent years, the product management discipline has faced a growing complexity of fragmented tools, constant context switching, increasing expectations and accelerating delivery cadences.
Into this environment arrives AI, not as a silver bullet, but as a tool that can increase efficiency and expedite decision making. What we choose to accelerate, however, should remain very much a human decision.
I recently had a conversation with Ross Webb, whose experience spans leading large product teams at Just Eat and building AI automation systems at Amazon Robotics. His work also now focuses on implementing AI-driven product operations tooling for companies aiming to unlock higher velocity and insight across their organisations.
The tooling he showcased was impressive. It integrates data from sources like Productboard, Linear and other analytics platforms, synthesises insight, and pushes clear recommendations back into the hands of product teams.
The promise of AI tools is simple, less time spent navigating dashboards and more time spent on decisions that matter.
Yet while time savings are measurable, Ross explained somewhere between five to seven hours per week per Product Manager, the deeper question I keep returning to was whether these tools actually improve the quality of product decision-making.
Faster doesn’t always mean better. And just because something is well-packaged and surfaced efficiently doesn’t mean it’s been thoughtfully considered or effectively actioned.
There is a growing risk that in the pursuit of even more velocity, teams accept AI-generated outputs uncritically, bypassing the uncomfortable but vital work of collaboration, negotiation, debate, prioritisation and trade-off analysis.
The tools can tell you what changed in the data, what features are delayed, or where customers are dropping off. But they cannot tell you what really matters to your business or your users unless you’ve been rigorous in defining that context upfront.
Feeding the AI Brain
Ross is clear on this point though, the tools are only as good as the ‘brain’ you feed them. Without a strong foundation of product vision, strategy, objectives, ethical boundaries and a sense of organisational priorities, the AI becomes little more than a fast, confident parrot. It mimics insight without necessarily delivering wisdom.
This is where intention comes in, as efficiency alone is not a worthy end state. It is only useful insofar as it creates space for deeper effectiveness. Sharper decisions, clearer trade-offs, and more meaningful outcomes. In theory, saving seven hours a week should create more time for Product Managers to engage in the kind of strategic work we so often lament is missing.
We still hear the stories today that talking to customers is often lacking, resolving stakeholder tensions continues to be a struggle, aligning teams around a shared purpose that often doesn’t exist.
But in practice, that space is not always filled with intention. Sometimes it is filled with more rituals, more outputs, more Slack threads. AI can take the busywork off your plate, but only you can choose to replace that with better thinking rather than more noise.
Product Ops in an Evolving Structure
There is also a growing industry trend toward leaner product structures, where smaller teams of people are orchestrated by a single senior product leader working across multiple empowered (that being; aligned and autonomous) squads.
Ross agreed with this direction, noting that it aligns with what he has observed firsthand. In this structure the role of Product Ops, which I’ve long argued could be a key differentiator, could expand significantly. This would not be as a support function but as a strategic capability that enables insight and clarity at scale.
Meanwhile the middle layers, people managing five or six Product Managers each, may disappear. Equally many junior Product Manager roles could give way to fewer, more senior individual contributors with stronger decision-making skills and cross-functional fluency.
This shift reflects a return to the original promise of Agile in my opinion; autonomous, cross-functional teams aligned around outcomes. In many companies, product management became a bottleneck rather than an enabler of that autonomy.
Tools like the ones Ross has built may begin to rebalance that, by surfacing insights directly to Designers, Engineers, and Data Analysts, and allowing Product Managers to focus on the hard problems of clarifying direction, making big bets, and telling the story of what matters and why.
Bringing Back the Product Craft
But again the tools do not do this for us, they merely remove excuses. With AI surfacing the data and summarising the options, Product Managers can no longer say they were too busy to consider strategy. The work becomes more exposed and, for some, more uncomfortable.
Strategic clarity can no longer be deferred, judgment must be exercised, trade-offs must be made and some will thrive in this environment. Others may struggle, especially if their prior value was built on managing process rather than driving impact.
The conversation also made clear that AI is not a replacement for product craft. Empathy, storytelling, strategic tension, and stakeholder alignment remain very human competencies and are deeply contextual ones.
These are not areas where AI is currently strong, nor should we want it to be. In fact, the rise of AI may force us to recommit to these human elements with even greater urgency, now that the operational burdens have started to lift.
Ultimately, the future of product management in an AI-infused world depends less on the technology itself and more on how we choose to wield it. “AI is like fire; it can burn or it can cook” as Ross put it perfectly.
It is our responsibility to decide whether we use these tools to merely move faster or to move better. Efficiency gains can be attractive, but it is effectiveness that delivers value to customers and impact to businesses.
As product leaders, the responsibility is not simply to adopt these tools, but to guide their implementation with clarity, ethics, and a relentless focus on delivering meaningful outcomes.
If we get that right, the promise of AI is better products, built with purpose, not just faster output.