The app for independent voices
Using ChatGPT doesn’t make you an AI PM.
But becoming one isn’t rocket science.
Let’s start with the basics and discuss:
What is a Product Manager?
What is an AI-Powered PM?
What’s Special About AI PM?
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1. What is a Product Manager?
We often say that when the product fails, it's the PM's fault. But when it succeeds, it's thanks to the team effort.
Failure is something PMs deal with regularly. As Marty Cagan explains in Inspired:
„The first truth is that at least half of your ideas are just not going to work.”
Product management is, at its heart, about managing risk. While collaborating with others, the PM is especially responsible for two areas of risk:
Customer value: Do customers want this product or feature?
Business viability: Will it work for different parts of the business?
That's why critical activity for a PM is participating in Product Discovery. Its goal is to address key risks before implementation.
You can learn more from this free post: productcompass.pm/p/wha…
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2. What is an AI-Powered PM?
These terms are often confused:
AI-Powered PM: A PM who uses AI at their work.
AI PM: A PM who works on AI-powered products and features.
Here’s my strong opinion:
Soon, there will be no such thing as a PM who doesn’t use AI daily.
We should be living and breathing AI.
Not to replace our thinking.
But to boost our productivity and eliminate manual work.
Free resources to get you started:
GPT-4.1 Prompting Guide: cookbook.openai.com/exa…
Anthropic prompt engineering: docs.anthropic.com/en/d…
Prompt engineering by Google: cloud.google.com/discov…
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3. What’s Special About AI PM?
First, your product involves AI. And the role is a bit more technical.
You don’t need a CS degree.
But unlike traditional PMs who should be tech-literate, AI PMs must understand the technology and build AI intuition.
One critical new element: AI evals.
A robust AI evaluation system:
Answers the question: "Does this product actually work?"
Helps your team measure, iterate, and improve in quick cycles.
AI evals are closely tied to the PM’s core risk areas. As an AI PM, even if you’re supported by AI engineers, you should regularly:
Look at and label data
Perform basic error analysis
Experiment with prompts
That’s the best way to build intuition and take full ownership of the risks.
You can learn more about AI evals from this free post: productcompass.pm/p/ai-…
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Marty Cagan’s comment from LinkedIn: linkedin.com/posts/pawe…
Hope that helps!
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Enjoy this?
In my new post, you will find:
6 more resources to help AI-powered PMs
10 step-by-step guides to build an AI PM portfolio
Just published: productcompass.pm/p/wha…
