In the opening of his influential and critically acclaimed book, Inspired, Marty Cagan reflects on the excitement of working on AI projects in the 1980s (!), at a time when the promise of AI to revolutionize software felt just within reach. Yet, despite the cutting-edge technology, many of those early efforts fell short of success—not because the AI itself was lacking, but because the teams didn’t fully understand what their customers truly needed.
Fast-forward to today, AI is even more prevalent and promising: The tools are more powerful, the expectations higher, and the opportunities are massive. In fact, it’s safe to say that every product manager is now an AI product manager (PM)—whether they’re directly developing AI features or figuring out how to use AI to transform their product, roadmap, and user experience.
AI’s role in product management
The product manager role has always been about navigating complexity and uncertainty. But now, AI introduces a whole new level of unpredictability, experimentation, and rapid iteration. PMs must adapt to a new way of thinking where success is measured not by building a list of predefined features, but by continuously testing, learning, and evolving. If you’re a product manager, you’ve probably felt this shift, whether it’s been explicitly communicated or subtly woven into how your team operates. Your job has always been about shaping the future, but now that future isn’t a decade away—it’s in the next six months.
There are two sides to AI’s role in product management. One is using AI to supercharge your own workflows, for example by summarizing feedback, analyzing data, drafting specs, or brainstorming ideas. The other is delivering AI-powered features to your users.
Interestingly, these two are deeply connected. The more PMs use AI in their own work, the more they develop intuition for what’s useful, what’s broken, and where real value lies. First-hand experience with flaky tools or magical moments can inspire new product ideas—or warn of hidden risks. It’s a feedback loop that makes the product manager both the builder and the test subject.
Let’s walk through some examples for each element of AI’s role in product management.
Using AI to supercharge PM workflows
The first dimension of AI in product management is using AI tools to enhance your own effectiveness. Here’s what that can look like:
Analyzing customer needs
AI can digest massive volumes of customer signals (e.g. support tickets, feedback, surveys, call transcripts, product analytics) and surface patterns that would be nearly impossible to spot manually. Topic modeling, sentiment analysis, and clustering techniques can reveal recurring pain points, underserved personas, or emerging use cases. Instead of relying on a few anecdotes or gut feel, AI makes it easier for PMs to ground their product thinking in data that reflects the full customer voice.
Pendo pro tip: With Listen Explore, an AI agent helps sift through multiple types of feedback to generate insights automatically. Additionally, behavioral insights reveal trends in user behavior with recommended steps to improve your user experience.
Roadmapping and prioritization
When it comes to deciding what to build, AI can help foster deeper clarity. For example, some tools can use historical patterns to forecast the likely impact of features on KPIs like retention or conversion. Others can simulate tradeoffs across engineering capacity, user segments, and business goals. This doesn’t replace product intuition, but rather augments it—helping product managers push past loud opinions or legacy plans and make decisions that align with real value. It also creates a more credible foundation for cross-functional alignment.
Improving experimentation
AI can dramatically reduce the friction in running and learning from experiments. It can automate test setup, help target the right user segments, detect statistical significance in real time, and even generate ideas for what to test next. Some tools can even simulate user behavior or predict likely outcomes before a single line of code is written. The result: more iteration, tighter feedback loops, and faster learning cycles.
Pendo pro tip: Use the AI writing assistant to speed up in-app guide creation and test different user onboarding flows or feature walkthroughs more quickly.
Prototyping and ideation
Finally, AI opens new doors in product exploration. Product teams can prototype ideas with AI tools—generating mockups, copy, or logic flows with just a prompt—and test product concepts without writing code. It’s also effective to use AI as a sparring partner in brainstorming—not to replace your creativity, but to expand it. This accelerates discovery and brings more ideas to the surface, faster.
Delivering AI-powered product experiences
The second dimension of AI in a PM’s role is building products that include AI as a core part of the user experience. This requires a different set of skills, including understanding how models work, navigating uncertainty, and translating technical capabilities into user value.
When building AI features, product managers should remain focused on delivering meaningful value to users. That value often comes in three forms:
1. Automating repetitive tasks
One of the clearest ways to improve user experience with AI is by eliminating tedious, manual work. Generative AI can help users draft content, summarize information, categorize items, auto-fill forms, and more. These small moments of automation can add up to significant time savings and user satisfaction, especially in complex workflows where friction slows users down.
2. Enhancing decision making
AI has the potential to shift your product from a passive tool into an active partner. Instead of just displaying data, AI can highlight insights, make personalized recommendations, and suggest next-best actions. In more advanced cases, agentic capabilities can even take those actions on the user’s behalf. Done well, this helps users feel smarter, faster, and more confident—with less effort.
3. Personalizing the product experience
AI enables real-time, adaptive personalization far beyond static user segments. By analyzing behavior, preferences, and context, your product can dynamically tailor what users see—from content to UI flows—so they reach value faster. Generative AI models also make it easier to personalize the way your product communicates by generating copy, tooltips, or even onboarding flows from simple prompts.
Pro tips for product managers embracing AI
Since every product manager is now an AI product manager, here are some tips for navigating this new reality:
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- Start small, but start now. You don’t need a full AI team to deliver value. Identify one pain point you can automate, personalize, or enhance and then build from there.
- Use AI as a thinking partner. Tools like ChatGPT or Claude can help you brainstorm ideas, refine messaging, analyze feedback, or simulate user personas. The more you practice, the better you’ll get at prompting and evaluating results.
- Keep a human in the loop. AI is powerful, but it’s not infallible. Always test outputs with real users and watch for edge cases, hallucinations, or bias in your data.
- Stay curious. The AI landscape is evolving fast. Make time each month to try new tools, read about emerging capabilities, and think about how they could reshape your product roadmap.
The PM role is evolving—again
AI isn’t just another trend. It’s reshaping how products are built, how PMs work, and how users interact with software. Whether you’re just starting to experiment or already deep into building AI features, the key is to stay adaptable, curious, and relentlessly focused on delivering real value. Because in this new era, the best PMs won’t just use AI—they’ll shape what it becomes.
Want to dig deeper into AI for product management use cases and best practices? Take the AI for Product Management Course—it’s free and packed with hands-on guidance.
