The AI Product Manager

Undoubtedly, 2025 is the breakout year for AI in Product Management. As tech investments shift toward AI and away from traditional Web/App products, everyone seems to be coming to the same realization that this is an “Adapt or Die” moment. You’re either going to rise with the tide of AI products or be crushed underneath it.

But what exactly is AI Product Management? After having several conversations on the topic, I get the distinct impression that we’re not all talking about the same thing. And so I wanted to use this article to propose a few definitions that might reduce confusion.

The term “AI PM” is analogous to “Mobile PM” in 2010. It was used to indicate specialized knowledge about mobile app use cases, UI patterns, and app store marketplace dynamics, but the core value proposition was still that of a Product Manager fundamentally — to ensure value creation for users and ensure product-market fit for what was being built.

The AI PM moniker is similar — it suggests specialized knowledge about a new capability and the related use cases it enables, but at the end of the day it is still Product Management at its core, and the primary focus should be ensuring customer value is created.

What an AI PM Is NOT

Perhaps the biggest confusion that I’m hearing in conversations is that someone is an “AI Product Manager” if they’re using an LLM to assist with research or writing their requirements. While this is an important step forward in productivity, using AI does not mean you are managing an AI Product.

Another misconception is that AI Product Management is an entirely new set of skills such as vibe-coding a prototype or running model Evals and this usurps traditional Product work such as market research and understanding customer needs. While it’s definitely helpful to tinker and understand new capabilities, there is a danger of getting swept up in shiny new things at the expense of the core value that Product brings to the table, which has always been product-market fit.

Types of AI PMs

In the diagram above, I describe three different layers of AI knowledge and how they relate to AI Product Management. I’ve heard these used interchangeably as different definitions of an AI PM, and so I want to disambiguate the topic. The three types are:

i. AI Experience PM — This is someone who manages a product that leverages AI to provide a new tool or experience for users. This could be anything from traditional ML-powered features like onsite search and personalized recommendations, to AI-native UI patterns. The PM superpower here is mastering all the use cases that AI unlocks and cataloging examples of well-executed AI products through market and customer research, then bringing those applicable use cases to market and testing your way into product-market fit (PMF). Like traditional consumer products, success for AI experiences is measured using outcome metrics (eg adoption, engagement, retention).

ii. AI Capability PM — Capabilities are more technical by their nature and require going a layer deeper in the AI knowledge stack. When a company creates their own in-house AI capability, Product is often working directly with the technical team, which necessitates a deeper understanding of AI/ML concepts. It’s worth noting that this is very similar to existing Technical PM roles. In fact, Technical PMs have worked on traditional ML capabilities for decades (eg search, recommender systems, price prediction and spam detection capabilities). Internal capabilities typically track output metrics for measuring success when they’re detached from the user experience (eg Precision, Recall, F1).

iii. Full-Stack AI PM — The last type of AI PM is someone who can do all of these things — they use AI tools (AI enabled), identify AI-native use cases and UI patterns (AI experiences), and work with technical teams to build internal models (AI capabilities). While it is less common that someone has full-stack AI knowledge, it can be ideal when they do, because it allows the PM to work across the full-stack and shepherd an end-to-end strategy that directly connects user outcomes and capabilities. In time, this depth of knowledge will become more common.

So, What Should I Learn?

Every Product Manager should be well-versed in AI-enabled tools by now such as using LLMs and diffusion models to assist in research, writing, and diagram creation — this is “table stakes” in 2025. Beyond that, here’s what matters for managing AI products:

For an AI Experiences PM — the most important knowledge is going to be the AI-powered use cases and AI-native UI patterns. The PM needs to know what’s possible and how to map that to customer problems and value creation. I recommend going a layer deeper though, to build a foundational knowledge of what AI is and how it works, in order to build a strong intuitive sense for what’s possible. Andrew Ng has a popular course on Coursera called AI for Everyone, which is a great place to start.

For AI Capability PMs — you’ll want to build a deeper understanding of AI/ML for this one. You don’t need to be a Data Scientist, write Python or do Linear Algebra, but you will be actively working with Data Scientists, Data Engineers and ML Engineers, and you’ll need to be able to speak this language. For example, what are the model types, core concepts like vector embeddings and vector search? You’ll also want to deeply understand concepts like precision and recall, since these output metrics are often how AI capabilities are measured. The Machine Learning Specialization (also Coursera) is a great resource for learning this level of knowledge.

You’ll probably also want to familiarize yourself with available in-house data that can be used in these models, who the stewards are of that data, and applicable governance and policies (eg PII, GDPR, CCPA), since 1P data is the backbone of in-house AI Capability creation.

Conclusion

To close, AI is undoubtedly an important innovation on the digital product landscape and it mandates that we all learn the foundations to ride the next wave of product development, or be crushed underneath it. But it’s important to not lose sight of the North Star for Product Management, which still and always will be — product market fit (creating value for users/customers).