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:

1. Collect user reviews from Reddit or the app store

2. Use Claude or GPT to summarize the top 3 pain points

3. Sketch a feature idea in Figma

4. Prototype it using Uizard or Lovable

5. 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:

1. Use GPT to brainstorm how AI can help (summarize lessons? auto-generate quizzes?)

2. Write user stories like: “As a student, I want a quiz after every module…”

3. 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:

1. What prediction do you want the model to make?

2. What data would help?

3. What’s the label the model should learn from?

4. How will you show predictions in the UI?

5. 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.