15 Best AI Tools Every Product Team Should Use in 2025

In 2025, AI has become an indispensable part of modern product teams. Whether you’re working in a startup or an enterprise, artificial intelligence can enhance speed, creativity, and decision-making across the entire product lifecycle. But with so many tools on the market, it can be hard to know where to start.

This comprehensive guide breaks down the 15 most useful AI tools for product managers, designers, developers, and marketers — with real-world applications, strengths, and how they fit into your workflow.

1. Athenic AI – Product Insight Automation

What it does: Athenic AI integrates with your internal tools (Slack, Jira, Zendesk, etc.) to turn unstructured data into actionable product insights. It uses AI to summarize customer pain points, detect common themes in tickets, and highlight trends that matter for product planning.

Use case: You can get weekly auto-generated reports that summarize what's working and what's not across your product lines, saving hours of manual analysis and spreadsheet digging.

Why it matters: Athenic helps product managers make smarter roadmap decisions based on real data, not assumptions.

2. Productboard with AI

What it does: Productboard is a powerful product management tool that recently integrated AI capabilities. These include auto-categorizing customer feedback, scoring feature ideas based on urgency, and surfacing insights aligned to strategic goals.

Use case: Instead of manually tagging and prioritizing feature requests, Productboard AI does it for you. It ensures that your roadmap reflects real customer needs.

Why it matters: It bridges the gap between customer voice and strategic execution — helping teams scale their discovery process.

3. Notion AI

What it does: Notion AI transforms how product teams write and organize content. From auto-generating product briefs to rewriting meeting notes in professional tone, it helps document work faster and more effectively.

Use case: PMs use Notion AI to write user stories, summarize customer interviews, and instantly draft PRDs based on a few bullet points.

Why it matters: Teams can communicate more clearly, reduce writing time, and keep documentation up to date — all in the same tool they already use.

4. LinearB

What it does: LinearB is an AI-powered engineering metrics platform that tracks development velocity, deployment health, and bottlenecks across sprints.

Use case: Product managers use it to keep track of how fast features are shipping, identify risks before they delay releases, and measure team efficiency with real data.

Why it matters: Transparency between product and engineering means better sprint planning, less finger-pointing, and more realistic roadmaps.

5. ChatGPT

What it does: ChatGPT, by OpenAI, is one of the most flexible tools for product teams. It can write content, generate ideas, simulate user personas, translate customer feedback, and more — all from a simple chat prompt.

Use case: PMs use ChatGPT to rewrite onboarding messages, validate feature names, or even simulate interviews with target users.

Why it matters: It works as a 24/7 assistant for writing, ideation, and quick validation — saving hours each week.

6. Figma AI (Plugins like Magician, Automator)

What it does: Figma’s plugin ecosystem includes AI tools that help generate layouts, icons, color palettes, and UI suggestions in real-time.

Use case: Designers and PMs can use plugins like Magician to generate placeholder copy or Automator to create components without touching code.

Why it matters: Speeds up early design sprints and allows teams to iterate faster, especially during MVP phases.

7. Janitor AI

What it does: Janitor AI allows you to simulate and chat with fictional characters or user personas. It's often used for roleplay scenarios, but it's increasingly being used in UX and product testing to emulate user conversations.

Use case: You can roleplay as a frustrated user trying to use a new feature, helping uncover issues in onboarding or UX flows. Learn more in our Janitor AI guide.

Why it matters: Helps product teams build empathy and test edge cases in ways traditional feedback can’t.

8. Talkie AI

What it does: Talkie AI creates highly realistic AI-powered conversations. It’s similar to Janitor AI, but optimized for real-time speech simulation and chat UX testing.

Use case: Use Talkie to simulate customer support flows or chatbot interactions for your product. Explore it in our Talkie AI guide.

Why it matters: Great for testing tone, structure, and UX in AI-driven apps or onboarding bots.

9. Character AI

What it does: Character AI lets you create and interact with custom personalities. It's used for testing narrative UX, building assistant bots, or experimenting with conversational flows.

Use case: Use it to model different user personas — like "power user", "first-timer", or "skeptical reviewer." Read our Character AI tutorial.

Why it matters: Helps simulate audience response and tailor product tone to different demographics.

10. Fireflies AI

What it does: Fireflies records and summarizes meetings automatically using AI. It creates transcripts, highlights key moments, and tags follow-ups.

Use case: PMs can review stakeholder meetings without watching the whole video. Designers can find exact user quotes from interviews.

Why it matters: No more missing details — and no need to assign a full-time note-taker for every call.

11. Miro AI

What it does: Miro is a visual collaboration platform, and its AI features now suggest mind maps, frameworks, and structure for product planning.

Use case: Run a remote design sprint with AI-suggested templates and quick sticky note clustering to organize research findings.

Why it matters: Speeds up brainstorming sessions, especially with distributed teams.

12. Airfocus AI Assist

What it does: Airfocus helps prioritize features, and its AI assistant provides context-based scoring recommendations and prioritization clarity.

Use case: Input product objectives, and Airfocus AI will suggest which backlog items align best — ideal for strategic planning and C-level updates.

Why it matters: Reduces bias in decision-making and offers a more transparent prioritization process.

13. Hotjar AI

What it does: Hotjar is known for behavior tracking (heatmaps, recordings). With AI, it now interprets user sessions automatically to surface friction points.

Use case: Instead of watching 50 user recordings, Hotjar AI shows the top 3 pain points — and even explains what may be causing them.

Why it matters: Makes qualitative UX data scalable and instantly actionable.

14. Descript

What it does: Descript is a video and audio editor powered by AI. It lets product teams create voiceovers, tutorials, and promotional videos with minimal effort.

Use case: Record a voice track, clean it up, and generate a product walkthrough in under 20 minutes — without hiring a video editor.

Why it matters: Makes professional-grade product marketing assets accessible to any team.

15. Kling AI

What it does: Kling AI uses generative AI to create stunning product photography from simple images or text prompts. It simulates lighting, shadows, and studio settings automatically.

Use case: Launching a new product and need 10 shots in different environments? Upload the item once and let Kling create them. Full review here: Kling AI review.

Why it matters: Levels the playing field for small teams and startups needing high-quality visuals without a studio.

Final Thoughts

AI is no longer optional — it’s a force multiplier for product teams. Whether you’re prioritizing features, planning UX flows, or launching a new product, these tools can save time, improve decisions, and drive better results across the board.

🔗 Also read: Will AI Replace Product Designers? to explore the creative vs. machine debate.