Being a product manager is one of the toughest—and most rewarding—jobs in tech. You’re part strategist, part customer advocate, part cat-herder. You’re expected to see the big picture while sweating the small stuff, to lead without authority, and to keep everyone aligned when the roadmap shifts under your feet.

It’s no surprise that PMs often find themselves buried in busywork: leading meetings, transcribing conversations, updating tickets, writing specs, analyzing feedback. Necessary tasks, yes—but not always the highest leverage.

That’s why generative AI is such a big deal.

Generative AI is becoming the co-pilot every PM needs

From discovery to delivery, AI is starting to quietly—and quickly—reshape how product work gets done. It’s not just about speed or scaling teams. It’s about creating the space to do more of what matters: talking to customers, thinking strategically, and finding new opportunities.

This article is your guide to some key ways to use generative AI as a product manager—from making sense of user feedback to drafting specs, scoping features, and staying on top of what your team is building. Whether you’re already experimenting with tools like ChatGPT, Claude and Gemini or just starting to explore what’s possible, this piece will walk you through the key moments where AI can take the load off and help you focus on the work that actually moves the needle. Let’s dig in.

Customer Discovery, at Scale

Discovery is where everything starts—but let’s be honest, it’s rarely where we get to spend enough time. Interviews take time to schedule. Notes pile up. Synthesizing themes across dozens of conversations will always get squeezed when a launch date looms.

Now imagine offloading that synthesis to an AI platform. You upload a folder of interview transcripts. A few minutes later, it hands you a digest of themes, emerging patterns, and even customer quotes sorted by topic for you to inform your strategy with. 

Prompt:

Here are 15 user interview transcripts. Summarize the key pain points, identify common user goals and frustrations, and group the insights into 4–6 high-level themes with supporting quotes.

This is where the real value of generative AI for Product Managers comes into focus. Instead of spending hours combing through interviews, highlighting quotes, and manually tagging notes, PMs can now shift their energy to crafting smarter hypotheses and connecting the dots across research efforts.

Analyzing User Feedback

You’ve got thousands of pieces of feedback sitting in support tickets, reviews, surveys, or community threads. You know there’s signal in there—but good luck finding it while juggling everything else on your plate.

You can run that data through ChatGPT or Claude and get clustered themes, sentiment analysis, and issue prioritization in minutes.

Prompt:

Analyze these 3,000 customer support messages. Return the top five issues raised by paying customers in the last quarter, grouped by topic. Include sample comments, volume per topic, and average sentiment.

Instead of spending days manually tagging tickets, color-coding feedback themes in spreadsheets, and second-guessing which requests are the most urgent, you walk into your roadmap planning session with clarity. Your AI assistant has already sifted through thousands of support tickets, survey results, and reviews—grouping them into meaningful themes like onboarding friction, performance complaints, and missing features, complete with representative customer quotes.

Identifying Real Customer Problems

User feedback often tells you what’s happening—but not why. One of the hardest jobs in product is reframing user feedback into actionable problem statements.

AI for Product Managers can help here too. Imagine pasting in a stack of research notes and asking an agent to surface root problems instead of surface complaints.

Prompt:

Given these interview transcripts and bug reports, identify 3–5 underlying customer problems or unmet needs. For each, include a short summary, relevant user quotes, and the context in which the problem arises.

You might get back:

• “Users don’t understand the dashboard layout and feel lost during onboarding.”

• “Freelancers hesitate to use our invoicing tool because there’s no visible confirmation once a client pays.”

These insights give PMs the clarity and context needed to translate scattered user feedback into precise, compelling problem statements that their teams can rally around. Instead of vague goals like "improve onboarding," you get sharp, customer-backed narratives such as: "New users are abandoning the dashboard because they don’t immediately understand the value of saved views." This gives you the confidence to act on what matters instead of spinning cycles on assumptions or blindly executing on the loudest exec request.

Writing PRDs and User Stories in Minutes

Every PM has dreaded the blank PRD doc. But this is exactly where AI and specifically tools like Devplan stand out. Devplan can help product managers go from fuzzy ideas to structured plans—fast.

To get the best results, you want to give a bit of background about your company: who your users are, what your product does, and what your goals are. Then you start a project by describing what you're planning to build. Based on that context, AI tools can generate a draft PRD with all the key ingredients: user problems, goals, scope, success metrics, and open questions. You get to shape it, expand on it, and make it your own—but you’re never starting from zero.

Prompt example:

We're a B2B SaaS startup helping HR teams manage internal mobility. Create a product requirements document for a feature that lets employees browse open internal roles and express interest. Include user needs, business goals, user stories grouped by workflow, success metrics, and risks.

Devplan is purpose built for this task and takes things a step further. It can generate user stories, break them down by workflow, and even estimate effort all in a single place. You’re not jumping between docs, spreadsheets, and planning tools—you're reviewing a single, clear project definition with user stories and tasks that are ready to execute.

Click here to Devplan a try—it’s the fastest way to turn raw ideas into real plans.

Estimating Work and Mapping Roadmaps

Scoping and sequencing always sound straightforward in theory—but in practice, they’re loaded with complexity. PMs often have to translate high-level goals into project timelines without enough context from engineering or clarity on interdependencies. It’s a slow, error-prone process.

What if you could hand a complete PRD or feature list to an AI agent and get back a structured draft project plan—complete with engineering tasks grouped by milestone, effort estimates, and key dependencies flagged for review?

Prompt example:

Based on this PRD, break the work into engineering tasks. Group them into 2-week sprints with clear milestone definitions for a team of 5 developers, 2 frontend, 2 backend and 1 fullstack. Estimate the complexity of each item on a 1–5 scale. Flag flag any technical dependencies or cross-team collaboration needs.

Using ChatGPT or Claude for this kind of AI-assisted planning doesn’t replace your engineering lead—it gives them a head start. And for PMs, it means tighter planning cycles, earlier tradeoff conversations, and less time lost in vague estimations.

Monitoring Progress and Reporting Up

Weekly status updates are critical for keeping everyone informed—but they’re also a giant time suck. Most PMs spend hours digging through Jira tickets, Slack threads, and GitHub comments to stitch together what actually happened during the sprint.

With AI, a lot of that overhead goes away. You can download your last sprint of work, add it to ChatGPT and generate a stakeholder-ready update in seconds.

Prompt example:

Summarize this sprint’s progress using data from these Jira tickets and GitHub pull requests. Include completed items, in-progress work, delays, and any blockers. Format the output as a weekly stakeholder update.

This will give you a good starting point to get a clear, concise digest ready to share with your team or leadership without manually categorizing or chasing updates.

The Bigger Picture

AI won’t write your roadmap or talk to customers for you. But it will give you more space to do both, better.

It’s not about cutting corners. It’s about cutting noise. The meetings that didn’t need to happen. The documents that never got read. The updates that took three hours to write and five minutes to skim. Using specific Generative AI for Product Managers helps you focus on decisions that only a skilled human can make.

AI is already reshaping the craft. Not by replacing it—but by letting PMs get back to the part that matters: understanding problems, guiding teams, and building great things.

Try Devplan

Devplan brings together the most powerful AI agents for product managers in one place. It helps you spec faster, estimate better, and plan smarter.

Click here to try it and see what it can do for you.