The AI PM's Playbook: How Top Product Managers Are 10x-ing Their Impact in 2025
We've all been sold a million ways to use AI as a PM. Let's cut through the fluff with the top use cases, tools, and rules to improve your productivity at work without killing your reputation.
It’s the thing we all keep hearing about AI…
“All PMs need to become AI PMs.”
For my money, it’s a true statement. But with some caveats.
Let me explain.
The 3 Types of AI PMs
What do we mean by ‘AI PM’?
Actually there are three types of AI PMs:
AI-powered PM: This is every single PM, who will need to use AI to “work unfairly in this unfair job.”
AI PM: These are the specialists building core AI products at companies like OpenAI and Anthropic.
AI feature PM: These are PMs adding AI features to existing products, eg Notion’s AI writing assistance, Miro’s smart canvas
Not every PM will be type 2 or type 3 AI PMs.
But every PM needs to become type 1 - AI-powered. That’s the subject of today’s piece.
Today’s Deep Dive
I talked to 10+ folks on how they use AI as PMs. Here’s a concise, to the point guide to take you from “Just ChatGPT” to “AI-first” as a Product Manager (PM):
The 3 Rules to Using AI Right
Top 5 AI PM Use Cases
Common Mistakes
1. The 3 Rules to Using AI right
We’ve all been bombarbed with information about how to use AI. So I wanted to simplify it to the absolute core first principles.
And I’ve boiled them down to three key rules you must remember when using AI as a PM:
Rule 1 - Prompt skill is everything
Each tool has its own nuances for prompting. But generally, there’s a huge skill curve in prompting.
Just like everyone assumes they’re an above-average driver, everyone tends to assume they’re an above-average prompter.
In actuality, you probably have a long way to go as a prompter if you haven’t been practicing.
If you’re using ChatGPT o1 for instance, the optimal prompt structure looks like this from Dan Mac and retweeted by OpenAI’s President Greg Brockman:
Rule 2 - 20-60-20
You need to do the first 20% of the work in any PM text because the truth is: Claude doesn’t have access to your context as a PM.
So you need to brain-dump the relevant information into it, as the picture above alludes to.
You also need to do the last 20% of the work. That’s where you remove the traces of AI in the text. And you give it your additional unique human context.
That’s why the company has hired you after all! And not just created an AI agent.
Rule 3 - Revise till you get what you want
It’s rarely the case that the first output you get from an AI is exactly what you want. You generally want to iterate with it.
Provide very clear and specific feedback that it can easily incorporate. It’s usually in drafts 4-5 of a written output that things can be used.
The best PM I know treats AI like user research – multiple rounds of refinement. Her process:
Draft 1: Basic prompt
Draft 2: Add specific examples
Draft 3: Incorporate stakeholder constraints
Draft 4-5: Fine-tune tone and details
Now let’s get into the specifics…
2. Top 5 AI PM use cases
We are all getting sold a million use cases to use AI as an AI-powered PM.
For my money, these are the 5 that really matter:
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