The AI-powered PM toolkit

The 10 best AI tools for product managers

Author

Tadhg O'Leary
March 06, 2025

There’s been a lot of noise recently that product management is dead. And that AI is the murderer.

This is dumb.

Maybe PM is dead if all you ever did was go to standup and create Jira tickets. But for good PMs, AI has the potential to massively change the role for the better. To eliminate tedious, unnecessary work and make us more effective in the work that matters.

So what does this AI-powered PM of the future have in their toolkit? What are the best AI products to level up your game?

Here are 10 of my favourites:

1. Perplexity - market & competitor research

Perplexity is my daily driver for research and discovery. Especially for broad market research. The Deep Research mode is a game-changer, and the ability to switch between different reasoning models (OpenAI, DeepSeek) allows you to get different “perspectives” on the same data. If you spend any amount of time researching markets, trends etc, the pro version is well worth the 20 quid a month.

(Pro tip: if you sign up to Lenny’s Newsletter you get a year of Perplexity pro for free)

Key use cases for PMs:

• Competitive research – Quickly get insights on what competitors are doing.

• Exploring new markets – Use Deep Research Mode to see trends across industries.

Potential drawbacks:

• Accuracy isn’t perfect. Perplexity hallucinates sometimes, especially on specific facts or figures.

• Best for exploratory research. It’s great for generating ideas but less reliable for exact data points.

2. ChatGPT Projects – AI as a thinking partner

This is the best way by far to use ChatGPT as a PM. You create a dedicated project for the product you manage. Give it specific instructions and relevant product info—customer feedback, your roadmap, metrics, goals. With that setup, ChatGPT becomes much more useful for helping you to think through product decisions. If you use AI for strategic work, this is the best way to do it.

Key Use Cases for PMs:

• Decision framing – Helps structure big product decisions and challenge assumptions.

• Creating structured thought processes – Helps document reasoning and identify faulty decision patterns over time.

Potential Drawbacks:

• AI can reinforce bias – Large language models tend to agree with you, which can lead to poor decision-making.

• Risk of outsourcing your thinking – AI should act as a thinking partner, not a mental crutch.

There are a lot of AI note-takers out there. Granola is the best I’ve used by far (and I’ve tried a lot). The key feature is templates—you can set different formats for different meetings (e.g., customer research vs. standups), and it will organize the notes accordingly. It’s a small thing, but it makes the output far more structured and usable.

Key Use Cases for PMs:

• Templates for meeting notes – Helps keep track of customer feedback, standups, and planning meetings.

• Chat with transcript - A very cool feature where you can ask questions about the meeting transcript (E.G. what did X say about Y).

Potential Drawbacks:

• Might miss key details – Granola does this the least but even the best AI note-takers can overlook important points sometimes.

Outset AI runs AI-led qualitative interviews. These are kind of like a survey, but instead of multiple-choice responses, AI conducts a conversation and extracts insights. I haven’t used this one personally yet, but the case studies are interesting, especially around time saved vs. traditional research. If surveys are already a key input in your research process, then I’d definitely give this a go.

Key use cases for PMs:

• Scaling user research – Conduct more customer interviews without manual effort.

• Identifying insights faster – AI extracts patterns from user responses.

Potential drawbacks:

• You don’t want to depend too much on AI research. Outset feels powerful, but there’s a risk that PMs lose their product sense if they’re not regularly talking to customers and users.

The market for AI feedback analysis is exploding and Kraftful seems to be leading the way. It pulls data from multiple feedback sources and uncovers trends, insights and themes. Manually sifting through feedback is an absolute pain, so if Kraftful does what it says on the tin, it should be a massive time-saver for PMs

Key use cases for PMs:

• Analysing user feedback at scale – Makes sense of large volumes of customer insights.

• Identifying feature requests and pain points – Surfaces common trends across users.

Potential drawbacks:

• Potential hallucinations – While Kraftful claims it doesn’t hallucinate, I’d still see it as a risk in adopting a tool like this.

• Summaries may miss key insights – Sometimes the most valuable insights are in the nuance of feedback.

6. Napkin AI – visuals from text

Uizard is useful for turning quick sketches into prototypes. You can draw something by hand, upload it, and Uizard converts it into a functional wireframe. If you work with designers (or need to communicate product ideas visually), this speeds up the process.

Very useful if you’ve got your design system in UIzard too - the prototypes generated can match the same design as your product.

Key use cases for PMs:

  • Rapid wireframing – Convert hand-drawn sketches into prototypes instantly.

  • Quick stakeholder alignment – Useful for showing concepts before formal design work starts.

Potential drawbacks:

  • Not the most user-friendly design tool. While powerful, it’s a bit tricky to use.

Another prototyping tool, but instead of sketches, Lovable generates prototypes that actually work i.e., it generates code, not just screens. This is the best AI coding tool for beginners. It’s unreal for testing ideas quickly before committing to full designs or development.

Key use cases for PMs:

  • Clickable prototypes – Useful for user testing before investing in full design work.

  • Fast product discovery – Iterate on multiple ideas in early-stage development.

Potential drawbacks:

  • PMs aren’t designers. While AI helps, UX design expertise is still crucial.

  • If you rely too much on high-fidelity prototypes, you might lock yourself into ideas too early. Paper sketches and low-fi mockups are still valuable.

Index is a roadmap and planning tool that integrates with Linear (a way better Jira). It helps link roadmaps to feedback, goals, and execution, making it easier to track priorities and changes over time. If your roadmap feels disconnected from actual decision-making, this might help.

Key use cases for PMs:

  • Linking roadmap to execution – Keeps roadmaps tied to actual feedback and priorities.

  • More structured planning – Helps teams visualise and organise initiatives.

Potential Drawbacks:

  • Potentially too flexible. Highly customizable tools can be misconfigured, leading to poor usability if not set up well.

Height is an AI-powered project management tool—essentially Jira, but with an AI agent built in. I haven’t used it yet, but the idea of AI handling more of the task tracking and progress updates is an interesting one. Could be worth exploring if you find traditional project management tools too manual.

Key use cases for PMs:

  • Automating project tracking – AI can assign tasks, track progress, and surface blockers.

  • Reducing manual updates – Less status reporting, more actual work.

Potential drawbacks:

  • Workflow accuracy. The big question is whether AI is accurately closing tickets and tracking progress or if it needs constant human oversight.

There you go. Sin é.

The 10 best AI tools for product managers.

Whether you actually use any of these tools or not, I hope the meta point of the post is clear:

AI is changing product management whether we like it or not. If you don’t embrace it, you’ll get left behind.

Reply

or to participate.