How AI Is Actually Enhancing the Role of the Product Manager, from Co-Pilots to Agents

In the age of artificial intelligence, a wide range of industries are being impacted. There is a great deal of speculation about the future and the impact AI will have on our lives and careers. While this revolution brings uncertainty to many areas, it also has the potential to enhance and transform our professions.

For product managers, it doesn’t replace the role, but it completely transforms it. And that’s where it gets interesting. ☕

Artificial intelligence is transforming product management—there’s no doubt about that. But to reduce this transformation to autonomous AI agents alone is to focus on the final chapter of a book that most teams are still in the middle of reading. The reality is more nuanced—and ultimately more accessible: the evolution of product management is unfolding on three distinct levels, and the vast majority of benefits are already available today, without having to configure a single agent.

AI increases PMs across three distinct levels

For many of us, using AI is a vague concept, ranging from a simple conversation with an LLM to designing autonomous agents within our field. Yet using AI in complex environments is actually quite simple—once you know where to look and how to adapt it.

First level: AI integrated into existing tools

As PMs, we use a wide variety of apps every day, most of which already have AI built in:

Amplitude automatically detects anomalies in product behavior and suggests insights even before you’ve asked the question.
Productboard aggregates feedback from multiple sources, identifies patterns, and suggests prioritization based on customer impact.
Mixpanel now lets you ask questions in natural language — “show me the signup funnel drop-off in France last week” — and generates the visualization instantly.
Notion transcribes your meetings into actionable summaries, creates tickets in your backlog, or generates reports based on them.

The list is long, and this level of improvement requires neither advanced configuration nor specialized technical skills. It can be implemented right away using tools that many teams already have in place, which often provide the full context of the team right from the start.

Second level: assistant co-pilots

The second most accessible level for PMs is “assistant co-pilots.” A virtual PM colleague who follows you everywhere and relieves you of the mental load so you can focus on what matters most, gain new perspectives, or get help with decision-making.

Otter.ai transcribes and summarizes user interviews in real time, freeing up the PM to truly listen rather than take notes.
AI Concept generates initial versions of PRDs from raw notes.
Reclaim.ai optimizes your schedule by prioritizing blocks of deep work within a calendar packed with agile rituals.

We’re not talking about autonomous agents just yet. These tools still require human input (or at least some action). They are simply out-of-the-box augmented assistants that draw on the context you provide. According to the AI PM Tools Directory, this is where most of the value available to the “general public” of PMs lies today.

Third level: autonomous agents—powerful, but still in their infancy

This is where we take things a step further and get closest to our “Jarvis”—if you’re a big Iron Man fan. The agents execute entire workflows based on defined triggers, without any human intervention. They plan, chain together actions, identify thematic clusters, and act on them…

It’s powerful and quite impressive to see in action. Yet few of us have actually implemented it. This is the domain of the most advanced teams. And yet it will be the next step for project managers to take, so they can begin delegating the most tedious tasks and focus on the core of our profession: decision-making and strategy.

So the right question isn’t “Should we adopt AI agents?” but “When will my team be ready for this level of autonomy?”

What this means in practical terms for everyday life

Regardless of the level of adoption, certain tasks are particularly well-suited for automation using AI—even at the most basic levels. Depending on the tools your teams use today, the following tasks can be implemented fairly easily and save time while delivering invaluable benefits.

Feedback and data analysis

This is undoubtedly the most well-known and widely used application in the product sphere at the moment. The ability to cross-reference support tickets, user interviews, reviews, and dates to identify pain points, insights, or the value proposition allows us to deliver value very quickly and in a highly targeted manner. But the goal isn’t speed for speed’s sake: it’s the ability to make better decisions with richer, fresher, and more representative data.

Documentation and specs: the thankless task that can finally be automated

This step is still a bit complex to implement at the moment, as the context (RAG) must be properly configured to minimize hallucinations or errors. Nevertheless, the time savings are enormous, not to mention the reduction in human error and the improved communication between teams, provided the agent is well-designed. NotionAI is currently the leading native tool for documentation, but all LLMs already offer significant value when it comes to drafting user guides or documentation.

Prioritization: a foundational tool, not a crystal ball

Prioritization frameworks (RICE, MoSCoW, etc.), which are based on teams’ partial insights, can suddenly be informed by aggregated data, thereby becoming more robust. Data and feedback can be quickly cross-referenced with OKRs to propose a prioritization that is not a substitute for judgment, but a working foundation that reduces blind spots and thus speeds up decision-making within teams.

What the Prime Minister Should Not Delegate

The beauty of our profession is that our tasks are varied and the topics can be quite diverse. But above all, we deal a lot with people. A field that will remain the exclusive domain of Homo sapiens for the next decade!

Our role is to drive the product vision by making strategic trade-offs. But no tool can resolve conflicting directions without human input, nor can it account for the internal political context, business imperatives, or the product’s founding vision. And even less so when we factor in empathy and a nuanced understanding of the organizational context we’re in, the company’s dynamics, and the alignment of stakeholders who undoubtedly have unspoken frustrations… That still falls within the realm of human judgment.

We must also mention the ultimate responsibility of the augmented PM. Co-pilots or agents make mistakes, misinterpret things, or overinterpret them. A PM who delegates without validating, iterating, and improving does not become more effective; instead, they create hidden risks.

The risk no one mentions: AI stack debt

The rapid adoption of AI tools poses a real organizational risk, one that is often overlooked in enthusiastic articles. Antonia Landi, a product ops consultant, calls it “stack debt”: by piling on new tools without verifying their integration into a coherent system, teams end up with a dozen overlapping subscriptions, data fragmented across platforms, and no one who has a handle on the whole picture. The rule to follow before adopting a new AI tool: what specific, measurable problem does it solve, and how does it integrate into existing workflows? AI is not a default solution to all inefficiencies—it’s a hypothesis to be validated like any other.

Speaking of risk, there is one risk that nobody really mentions. For many, the race for AI is a real race. Management is jumping on the bandwagon, treating it as the “default solution to all inefficiencies,” but without anyone having a full grasp of the big picture.

The integration of AI is just another option and should be treated like any other product. What problems will it solve, and which AI tools should be used? How do they fit into existing workflows, or how should workflows be adapted to accommodate the product?

Adopting AI gradually, rather than in leaps and bounds

The most effective approach is to integrate AI into our teams’ daily workflows and leverage existing products. These products already provide the necessary context, allowing us to quickly test adoption and assess the value these tools can deliver. Most product stacks already contain value that can be accessed with just a few clicks.

Once that’s done, you’ll be able to identify high-volume, repetitive tasks specific to each targeted role. Just a quick glance at your calendar will tell you where to start.

Start small: meeting minutes, reviewing your user stories, and developing value propositions based on user interviews. Once you’ve achieved satisfactory results, building agents will be just a minor part of the process.

The enhanced PM of 2025 isn’t the one with the most agents in production—it’s the one with a clear view of the entire spectrum, and that derives value from it at every level.