AI Product Manager - Real Role or Just Hype?
Let's peel this onion
The term "AI Product Manager" is everywhere, sparking debate across the product management LinkedIn bubble-o-sphere. Does it indeed describe a specialization or differentiated set of skills and knowledge? Or is it just the latest buzzword in the tech space that drools over new labels? Does managing an AI product fundamentally differ from managing any other tech product? I don't know so let's dig in and find out!
What Is Meant by "AI Product Manager"?
Through a review of some of the online literature, I found the following themes:
An AI PM can focus on user-facing AI-powered applications OR the underlying infrastructure. The vast majority of AI PMs would be focused on the experience delivered by AI and so that is the role that is most often referenced publicly. (SVPG).
The AI PM is a clear specialization requiring unique knowledge in AI/ML concepts, data strategy, and ethics, distinct from generalist PM roles (Product School, ELVTR UK Interview, O'Reilly Radar).
The AI PM acts as a bridge between technical data science/ML teams and broader business goals, translating needs and capabilities in both directions (Data Science PM).
The AI PM isn't a new role but the natural evolution of product management adapting to a powerful new technology, much like the adaptation to mobile (Product Compass, GoPractice 2022, ZDNet 2018). Some critique the title as potentially unnecessary hype given the role is not new. (Eric Sandosham on Medium).
Where's the Conflict?
The core conflict lies in whether this constitutes a distinct, specialized role or is simply the ongoing evolution of the PM skillset. However, I think there is some consensus on the following points:
Enhanced Skills Needed: Managing AI products does require competencies beyond traditional PM, particularly around data, AI/ML concepts, and ethics.
Data is Central: A deep understanding of data strategy, quality, bias, and lifecycle is non-negotiable.
AI/ML Literacy is Key: PMs need a functional grasp of AI/ML concepts and workflows.
Ethics are Magnified: The ethical considerations (bias, fairness, transparency) are significantly heightened.
Focusing on Competencies
Given the debate, perhaps the most useful definition focuses on the job to be done and the skills required, rather than getting bogged down in the title's semantics:
An AI Product Manager (or a PM managing AI products) is responsible for products where AI/ML is a core enabling technology or provides significant user-facing features. This requires enhanced competencies in: (1) Deep Data Acumen, (2) AI/ML Literacy, (3) Navigating Probabilistic Systems, (4) Heightened Ethical Oversight, and (5) Specialized Cross-Functional Communication with data science/ML teams.
This definition acknowledges the need for specialized skills without rigidly insisting on a separate job title in all cases.
How Does "AI PM" Compare to Other PM Titles?
Is "AI" like "Technical" or like "Fintech"?
Functional/Skill-Based (like Technical PM, Data PM): The "AI" prefix signals a need for specialized technical knowledge and skills related to the AI/ML domain. It describes how the PM works and what specific knowledge they need. This seems to be the closest fit.
Industry/Domain-Based (like Fintech PM, Healthtech PM): "AI" is a technology that cuts across industries. An AI PM can work in Fintech, Healthtech, etc. The title denotes technological expertise, not market expertise. However, an AI PM can work directly on a product that is AI, which makes AI an industry and a technology. Oh boy.
But, when we boil it down to the most common usage of the term, "AI PM" functions more like a functional or skill-based specialization, similar to Technical PM or Data PM, indicating specific competencies are required.
Does the Type of AI Product Matter?
Yes, indeed. The demands on the PM change depending on how AI is used:
Core AI Products (AI is the Product - e.g., Foundational Models): The PM role here often overlaps heavily with Data Science. Deeper technical understanding of models and infrastructure is usually required. This is where some say the AI PM title makes the most sense. Some say this is the only role where the title makes sense.
AI-Powered Products (AI drives the Experience - e.g., Autonomous Systems): This is where the specialized "AI PM" competencies (managing probabilistic UX, data strategy for personalization, ethics, explainability) are most critical and distinct. The title here passes the smell test for most people.
AI-Leveraged Features (AI enhances the Product - e.g., Chatbots, Customer Service Agents): Here, the need is more for broad AI literacy within the standard PM role. A dedicated "AI PM" title is less likely necessary; the standard PM needs to understand the feature's capabilities and limitations. Often, this is what is meant by “AI PM” when referenced on social media. And there’s the rub!
Distinct Specialization Now, Evolving Landscape Later?
So, is AI Product Manager a distinct role?
Putting PMs at OpenAI, Anthropic, etc. aside, who clearly are AI PMs, yes it is.
But not for as many roles as the thought leaders say! The way (as of June 2025) AI is being added as a prefix to PM is mostly hype.
Where it can be used legitimately and more widely is as a distinct specialization, particularly for AI-Powered Products. The unique combination of required competencies (data acumen, AI literacy, managing uncertainty, ethics, specialized communication) and the specific challenges involved justify this specialized focus and title.
However, it's also an evolution. Basic AI literacy is rapidly becoming essential for many product managers, especially in tech. As AI tools mature and knowledge spreads, the sharp distinction might blur over time, with these competencies becoming more integrated into the standard PM toolkit. But not yet.
Ultimately, the focus should be on the skills, not just the title. Whether labeled "AI PM" or not, effectively guiding the development of valuable, usable, feasible, and responsible AI products requires a specific, enhanced set of competencies.