What are the different types of AI Product Managers. Are we going to see a role that's similar to a Technical Product Manager focused towards the data science team?

AI/ML by definition requires a decent foundational understanding for AI/ML/Deep Learning Concepts, trends in industry, tools and methodologies to be able to work with engineering in defining solutions to customer /user problems. For the forseeable future, I would say that most AI/ML product managers would likely function as both technical as well as regular product managers to blend user journeys into concrete AL/ML solutions. 

PMs are generally categorised into B2C (Consumer), B2B (Enterprise), Platform, and Product. PMs role is generally 2 of these 4 things. Within this one can also be generalist PM vs domain PM vs growth PM vs scale PM etc. AI PM is the same. One additional categorey can be about building ML Ops platform but I am not convinced one needs a PM for it, or can't be fit into one of the 4 categories described above.

PMs working on Alexa can be AI PMs, but not necessarily TPMs.

I have seen 4 different types of AI Product Managers:

1. Product focused: Infuse intelligence into products (e.g., search, personalization)

2. Platform focused: Infrastructure & tooling for data scientists and ML engineers to manager ML lifecycle

3. Business/Operations focused: ML to improve product adoption/operations (e.g., customer churn prediction) or business outcomes (e.g., revenue forecasting). This role typically works with data science teams that work on internal optimizations leveraging ML.

4. Research focused: Bringing AI research breakthroughs to market

If you are interested in learning more, watch my session on AI/ML Product Management: https://www.linkedin.com/video/event/urn:li:ugcPost:6929801568753971200/

I think at an even higher level we will actually see at least 3 different approaches to AI product management even in the organization structure itself (not necessarily mutually exclusive):

• Dedicated AI PMs: Absolutely, more and more companies with dedicated AI product managers

  • Possibly with a further separation in focus areas between internal tools vs. customer-facing AI features

  • And/or different focus areas along the AI stack e.g. infra, MLOps, model-level performance vs. more customer-facing at the application level

• Hybrid PMs: Other companies may try to integrate "AI product management" as a skill that you can train your PMs on and that they should all have. Rather than a focus area for certain PMs.

• Empowering R&D: Other companies may give more ownership to R&D leaders and push them to be more business-centric in their thinking. Essentially taking on part of the role of an AI PM in other companies.

Which approaches gain more popularity or not will have massive second-order effects on what skills PMs and R&D teams will need to hone to be competitive on the job market, which profiles become in higher demand, relative size and influence of those personas inside the organization etc.

I think this is hard to say, but if I had to guess I think this does evolve into a specialized function. At Barracuda we had a platform PM function and I was lucky enough to work with a phenomenal PM who owned threat detection efficacy. He interfaced with our data scientists and ML engineers on a daily basis, and had to be very comfortable with concepts like precision and recall, model retraining, model ops, etc.

That said, the LLM cloud providers have made it so easy to consume third party models via API there's a level of abstraction that makes specialized important, but less so than if you have an in house ML engineering team. So I'd imagine there's plenty opportunity for specialized PMs and generalists to play in this field.

While I don't believe there are a lot of different types of AI Product Managers, in some large organizations I see two types of AI Product Managers - 1) application focused and 2) AI foundation focused. It also varies based on the size and maturity of the organization. The Application focused AI PM is thinking a lot about how can you leverage the AI to deliver the best customer value, which customer problems can be solved better if we applied AI and then focus on the adoption of AI. The Foundations AI PMs role is focused on working much more closely with the data science teams to innovate, decide what types of AI models to leverage, ensure that the processes we leverage for AI are compliant, build data anonymization pipelines, data acquisition and labeling tasks are performed and the infrastructure is scalable in a cost affective way.

In smaller organization if you join as a product manager, it is highly likely you are the only Product manager doing all of the things. Hence, these lines get blurred in different organizations.