The 4 Types of AI Product Managers
Explore the evolving role of AI product managers
Not all AI Product Managers do the same job.
Some product managers use AI to move faster in their own work. Some add smart features to old products. Others build entire apps where AI is the product. And a few work behind the scenes — setting up the infrastructure that powers all of this.
That’s why “AI PM” can mean very different things depending on the job.
In this post, we’ll break down the four types of AI Product Managers — with clear examples and real tools across product phases like research, prototyping, analytics, and more.
1. AI-Powered PM – Uses AI to work smarter
These PMs don’t build AI products. Instead, they use AI tools to speed up their own work — from research and prototyping to writing specs and tracking metrics.
Tools They Might Use
Research & insights: Claude, Perplexity, ChatGPT
Prototyping: Uizard, Lovable, Figma + GPT
Analytics: GPT + Google Sheets, PostHog, custom GPT scripts
Example
A PM working on a food delivery app downloads 500 Play Store reviews. She pastes them into Claude to find recurring pain points: “late delivery,” “missing items,” “no order status.”
Now she knows what to prioritize — and it only took 15 minutes.
She opens Uizard to mock up a new live tracking feature, with GPT suggesting the screen copy. Later, she analyzes how ticket volume changes weekly by using GPT on top of exported support CSVs.
Exercise for You
Pick a product you use often — maybe Spotify, Swiggy, or a learning app. Then:
Collect user reviews from Reddit or the app store
Use Claude or GPT to summarize the top 3 pain points
Sketch a feature idea in Figma
Prototype it using Uizard or Lovable
Write a short spec using GPT
Time yourself: try finishing it all in under 2 hours.
2. AI Feature PM – Adds AI Into existing Products
These PMs work on regular apps — marketplaces, HR tools, CRMs. But they improve them by adding AI features that personalize, automate, or extract insights.
They don’t own the whole product. But they’re responsible for how well the AI-powered feature works and how users adopt it.
Tools They Might Use
Models: GPT-4, Whisper, Claude, open-source LLMs
Evaluation: Mixpanel, PostHog, A/B testing tools
APIs & infrastructure: OpenAI, Pinecone, Hugging Face
Example
A PM at a CRM company adds a feature that auto-generates call notes after sales calls.
They use Whisper to transcribe audio and GPT-4 to summarize it.
To evaluate the feature, they:
Compare AI vs human-written notes for 200 calls
Track usage metrics in Mixpanel: Who’s enabling the feature? Are they editing the notes?
Another example: A PM at an HR tech startup ships an “AI resume matcher” that surfaces the top 5 applicants based on job requirements. It’s powered by embedding similarity + classification models.
Exercise for You
Say you’re working on a learning app, and users struggle to revise past lessons.
Try this:
Use GPT to brainstorm how AI can help (summarize lessons? auto-generate quizzes?)
Write user stories like: “As a student, I want a quiz after every module…”
Define success metrics: quiz completion rates, retention, feedback on usefulness
This isn’t about building a full product — it’s about using AI to augment what already exists.
3. AI-First PM – Builds products where AI is the Product
This is the deep end.
AI-first PMs build apps that don’t work without AI. Think grammar correctors, deck generators, or chat-based travel advisors. The core value prop is AI.
You’re not just launching features — you’re shipping predictions, improving models, and balancing UX with uncertainty.
What Makes This Different
You define problems in terms of data and outcomes
You work with models, not just wireframes
You ship in iterations, not “launch and forget”
UX is important, but model performance = product quality
Example: TripGuard
TripGuard helps travel agents predict trip problems — driver delays, hotel no-shows, missing documents — before they happen.
As PM, you don’t just manage alerts. You:
Identify which prediction to make (e.g., “Will the driver be late?”)
Figure out what data helps the model (trip type, driver history, weather)
Define the label: past trip issues
Design how to present risk scores to users
Create a feedback loop: agents confirm/correct alerts to retrain the model
This isn’t a travel app with AI added. It’s an AI app that solves travel problems.
Exercise for You
You're PM for a new travel app. It should flag trips that are likely to go wrong.
Questions to answer:
What prediction do you want the model to make?
What data would help?
What’s the label the model should learn from?
How will you show predictions in the UI?
How will users give feedback to improve it?
This is how AI-first PMs think — in loops of data, predictions, and behavior change.
4. Core AI PM – Manages infrastructure behind the scenes
Core AI PMs don’t work on the front-end. They handle the plumbing of AI.
Their users are engineers, data scientists, and other PMs. Their products are tools, pipelines, dashboards, and policies that make AI development reliable, scalable, and compliant.
You’ll find them in companies where every team uses AI — and someone needs to keep it all running safely.
What They Manage
Data labeling systems
Model testing dashboards (bias, hallucination, cost, latency)
Deployment pipelines
Infrastructure scaling (GPUs, cloud workloads)
Privacy and compliance workflows
Example
At a large e-commerce company, every vertical uses AI — fraud, search, personalization.
A Core AI PM builds:
A model performance dashboard that tracks accuracy, latency, and fairness across 8 teams
A central data labeling policy
A rollout protocol to test new models in shadow mode before full deployment
They don’t touch the customer UI. But every customer-facing AI experience depends on their work.
Wrapping It Up
Not every AI PM does the same job — and that’s the point.
AI-Powered PM: Uses AI to boost their own workflow
AI Feature PM: Adds smart features to existing products
AI-First PM: Builds apps where AI is the core engine
Core AI PM: Builds the systems behind every AI feature
No path is better than the other. Choose based on:
What stage your company is in
Your personal interests (UX vs systems vs models)
How close you want to be to end-users vs data infrastructure
As AI becomes more widespread, these roles will only grow in importance. The key is not to chase the trend, but to find where you fit best — and go deep.
Very well written with good and simple examples! Thanks for sharing.