Using AI in product management: how to build, align, and ship faster

The biggest obstacle to shipping great products is context. Insights from user research rarely survive the handoff to design intact, and by the time engineering gets involved, the original problem can look unrecognizable. AI in product management is changing that, with measurable results to show for it.

According to McKinsey, generative AI already boosts PM productivity by 40%. The teams driving those gains share one thing in common: they’re using AI to keep context intact from discovery through delivery. If you’re looking to do the same, this article covers how product teams are putting AI to work across research, prototyping, and stakeholder communication, with less context lost along the way.

Read on to learn:

• AI applications in product management
• How to use Figma Make, Figma Slides, and FigJam AI in your workflow
• An example AI PM workflow
• Tips on using AI in product management effectively
• Frequently asked questions about AI in product management

How can I use AI in product management?

There are two ways to think about AI in product management. The first is building AI-powered products. The second is using AI to do the job of PM better: synthesizing research faster, prototyping ideas earlier, and communicating strategy more clearly. Most PMs are doing both at once.

That’s where connected workflows make a difference. With Figma, ideation, prototyping, and stakeholder communication connect on one platform, so the context behind every decision travels with the work. David Kossnick, Head of AI Products at Figma, puts it this way: a good roadmap is a living document that evolves with the team rather than a fixed list of features to ship.

Here’s how that plays out across five core areas of the PM workflow.

Ideation

Early-stage product work is about asking better questions. AI helps structure that thinking before the conversation starts, whether you’re kicking off a new feature or getting a cross-functional team moving in the same direction.

In FigJam, PMs can prompt the AI to generate a kick-off or ideation board structure tailored to the session. Your team gets a working structure from the start, so they can dive straight into sharing their ideas.

When a team hits a wall, PMs can use Jambot to generate feature ideas, reframe a problem from a different angle, or surface icebreakers to get a quieter group talking. It keeps momentum when the room goes flat.

Discovery/synthesis

Sorting through user research can take up hours that could be spent making product decisions. AI helps product teams synthesize interview notes, identify patterns, and surface themes in a fraction of the time it would take manually.

In FigJam, that means a significantly shorter path from raw research to insights you can act on. After user interviews, PMs can paste notes as stickies onto a FigJam board, then prompt the AI to sort and summarize them. It can also analyze sentiment across feedback clusters, generate interview summaries, and build user flows.

The same capability extends into sprint planning and retrospectives. PMs can generate a retro or weekly sync template from a prompt, skipping setup to get straight to the conversation. For teams in the Microsoft ecosystem, the Copilot and FigJam integration lets M365 users pull documents directly into FigJam to generate structured diagrams.

How to run an AI-assisted research synthesis session in FigJam

The next time you’re staring down a pile of interview notes, try following these steps in FigJam:

1. Open a FigJam board and type a prompt like “Create a user research synthesis board.”
2. Drop in sticky notes from your raw interview notes.
3. Use “Sort stickies” to automatically cluster notes into themes.
4. Use “Summarize” to extract key takeaways and generate a shareable link for your team.
5. Copy the summary link and paste it into Figma Make as context for your prototype prompt.

Prototyping

AI has changed what it means to prototype. What used to take design resources and multiple rounds of back-and-forth can now happen in an afternoon, giving PMs a way to test assumptions before a design sprint starts.

Figma Make is one of the leading tools for AI prototyping for product managers, generating functional prototypes, Web apps, and interactive UI from natural language. What makes it particularly useful for PMs is that it can attach existing Figma design files as context, so prototypes use real tokens and components from day one.

That changes how PMs communicate ideas. You can describe a user flow in a prompt, get a clickable prototype, and test it with stakeholders before anyone’s committed to anything. Or build a functional edge-case screen to surface technical complexity ahead of sprint planning.

According to Vishal Kapoor, VP of Product at Affirm, product managers at the company have gone from reviewing designs in Figma to building prototypes directly in Figma Make—and those prototypes now serve as the foundation for their PRDs.

Alignment

Getting teams aligned on a product direction has always been more about clarity than consensus. AI helps PMs build that clarity faster, turning abstract strategies into concrete outcomes that stakeholders can respond to.

During roadmap reviews, PMs can embed a Figma Make prototype directly into a Figma Slides presentation so that stakeholders can interact with the feature. That shift from explaining to experiencing changes the quality of feedback. The same setup works for QBRs and OKR check-ins, where PMs are often presenting strategy alongside design work.

Communication

Clear communication moves product decisions forward. AI helps PMs spend less time formatting decks and more time crafting the narrative, whether that’s a stakeholder update, a sprint review, or an async brief for a distributed team.

Figma Slides lets PMs create and present directly within Figma, embedding live design files, FigJam boards, and Figma Make prototypes into presentations without switching tools. For distributed or cross-timezone teams, sharing a deck as a link means stakeholders can review everything they need on their own time, with full context already attached.

Example AI PM workflow

Let’s say you wrap up a round of user interviews with pages of notes and a tight deadline. Here’s what an AI-assisted workflow can look like in practice:

1. Paste your notes into FigJam and prompt the AI to sort and summarize.
2. Prompt Figma Make, using the themes from step 1.
3. Generate a clickable prototype built on your design system.
4. Embed the prototype into a Figma Slides deck for stakeholder review.

The throughline is traceability. Every decision connects back to the original research, making it easier to explain trade-offs and keep the team aligned.

More PMs are adopting this type of work. In fact, weekly active users of Figma Make grew over 70% in 2025, reflecting a broader shift toward prototyping earlier and communicating work more collaboratively.

Tips on using AI in product management

Getting the most out of AI in your PM workflow takes some intentionality. The PMs seeing the best results are deliberate about when and how they use it. Here are a few tips worth keeping in mind:

• Stay flexible with your planning. Shorter cycles work better than long fixed roadmaps. Anchor your team around the problems you’re solving, and leave room to incorporate new information as it comes in.
• Test assumptions with prototypes. Get something clickable in front of stakeholders before you lock scope or resourcing. The earlier you can make an idea tangible, the faster you build conviction around the right direction.
• Invest in shared context. AI tools synthesize what you give them. Before solutions start taking shape, make sure everyone on the team is working from the same customer research, business goals, and constraints.
• Keep humans in the loop. Holly Li, PM at Figma, says prototyping frees up time for the part of the job that requires human instinct: making quality calls with confidence.
• Treat prompting as a craft. The quality of your output depends on how well you frame the input. Use LLMs to help draft and refine prompts before bringing them into tools like Figma Make.
• Define success beyond shipping. Think about adoption, user trust, and output quality from the start. Build in feedback loops early so you can monitor and iterate as the product evolves.

AI in product management FAQ

Keep reading for answers to frequently asked questions about AI for product management.

Will AI replace product managers?

AI is changing what product management looks like, but it’s not replacing the people who do it. The skills that define great PMs—judgment, empathy, and the ability to navigate trade-offs—aren’t things AI can replicate. What AI does is handle the more repetitive parts of the workflow, freeing PMs up to focus on the decisions that require human instinct.

What skills do PMs need to know to work with AI?

The fundamentals still matter: user empathy, strategic thinking, and clear communication. On top of that, PMs working with AI benefit from developing prompting skills, AI literacy, and workflow design skills.

How do PMs use AI in discovery?

Discovery is one of the highest-leverage places to use AI. PMs use it to synthesize interview notes, spot patterns across feedback, analyze sentiment, and surface themes in less time than it’d take to do so manually. The right AI tools for product discovery can make this process significantly more efficient.

How does AI help with PRDs and roadmaps?

AI speeds up the drafting process for product requirements documents and roadmaps by turning ideas and research notes into structured starting points. PMs can use AI to generate an initial draft and refine from there. Specifically for roadmaps, AI helps PMs pressure-test priorities and identify gaps before sharing them with the broader team.

What does AI-first product management mean?

AI-first product management means building products where AI is central to the user experience, not just a feature tacked on. It requires PMs to think differently about how products behave, since AI systems are probabilistic rather than deterministic, and outputs can vary. Success metrics shift too, moving beyond feature completion toward output quality, user trust, and adoption over time.

Use Figma to start building, not just planning

The ones getting the most out of AI in product management are using it to stay closer to the work, from early research through to stakeholder review. Figma brings that entire workflow into one connected place, so context travels with every decision you make.

Here’s how to put it all together:

• Synthesize research and align your team around the right problems with FigJam
• Bridge the gap between prototypes and production with Dev Mode
• Explore product roadmap templates to get a head start on planning and synthesis
• Present strategy and design work with Figma Slides, embedding live prototypes directly in your deck