Becoming an AI Product Manager - Where to start?

Introductions
1

Hi All,

I am a technology Product Manger (PM) who has limited technology skills but strong Product Management skills (15 years experience in technology products).

A lot of the roles that I am look at in market are “AI Product Managers”.

I am looking for some guidance from the community on where is the best place to start to become and “AI Product Manager” (through training courses at this stage) as I believe it will position me well for future roles.

A lot of the courses that I have seen have been focused on using AI to be a better PM, where as I am interested in Product Management of products that are AI solutions.

Happy to get into the details and expand my technology knowledge, but don’t really know where to start…

Any help or guidance would be much appreciated.

Cheers.

2

Hello there, have you reviewed this one from IBM on Coursera?

Take a quick look around the objectives on Course 9 and 10 and see if this course is right for your career path.

Hope this helps. :slight_smile:

3

That’s great.

Thank you. I will have a look at this course and see if it will help!

1 Like
4

First, it’s good to have some experience in the subject matter you’re going to manage. You don’t have to be an expert, but you should be familiar with the tools and methods.

That means attending some courses, before you start identifying the management opportunities.

5

I think this is sound advice.

Do you have any courses that you would recommend for a baseline understanding of the technology for AI products?

6

What type of AI products are you specifically referring to?

The “Generative AI” products like chat bots and intelligent agents? They’re all the rage currently.

Or the traditional process of creating models of complex data sets, in order to create predictive numerical results?

7

I’d say the latter.
The former seems more about leveraging existing tools already on the market, whereas I’m more interested in developing new tools and using models to support decision-making.

8

Recommend:

  • Attend an introductory course in Python programming.
  • Attend the Machine Learning Specialization.
  • Attend the Deep Learning Specialization. This covers some basic generative AI methods.
1 Like
9

Hello Jfazz,

Nice to meet you. We’re both sharing the same goal in this very competitive job market. I’d like to share with you the roadmap ChatGPT has helped me in fleshing out since I have prior experience in PM alone.

I have marked :white_check_mark: tasks I have completed. :orange_circle: means it’s still in progress. Disclaimer: I am not 100% sure if this is the right plan but at least it got me started.

And if ever you need someone to collaborate with, feel free to reach out since I am also new to this. It would be great to bump heads with like minded talents.

To everyone, if ever you have recommendations, I’d be happy to hear them.

Month 1-2: AI Fundamentals & Understanding the AI Product Lifecycle

Goals: Build a strong conceptual foundation and understand how AI products differ from traditional ones.

Topics to Cover

  • What is AI, machine learning, and deep learning?
  • Types of machine learning (supervised, unsupervised, reinforcement learning)
  • AI development lifecycle: Data collection, model training, evaluation, deployment, and monitoring
  • Common AI applications and their business impact (NLP, computer vision, recommendation systems)

Resources:

  • Courses:
    • AI for Everyone by Andrew Ng (Coursera) – Essential intro to AI for non-technical professionals :white_check_mark:
    • Machine Learning Crash Course by Google – Focus on the basics of machine learning concepts :orange_circle:
  • Books:
    • Prediction Machines by Ajay Agrawal – Business-focused AI insights
    • AI Superpowers by Kai-Fu Lee – Overview of AI trends and global competition

Activities:

  • Read case studies on AI products in your target industry.
  • Attend webinars or conferences on AI product management.

Month 3-4: Data Literacy & Metrics for AI Products

Goals: Develop the ability to work with data, understand AI-specific metrics, and manage data privacy risks.

Topics to Cover

  • Data basics: What is structured vs. unstructured data?
  • Introduction to SQL for querying data
  • AI product metrics: Accuracy, precision, recall, F1 score, latency, and model drift
  • Data privacy and governance (GDPR, CCPA)

Resources:

  • Courses:
    • Introduction to Data Science by IBM (Coursera) – Basic data concepts and hands-on exercises
    • SQL for Data Analysis by Mode Analytics – Free SQL tutorials
  • Books:
    • Data Science for Business by Foster Provost – Practical data concepts for business professionals

Activities:

  • Create sample SQL queries to explore datasets (Kaggle is a great resource for datasets).
  • Learn how to define success metrics for AI products using simple examples.

Month 5: AI Product Strategy & Collaboration with AI Teams

Goals: Understand how to define AI use cases, align business goals with technical capabilities, and collaborate with AI teams effectively.

Topics to Cover

  • Defining the right AI use cases and user problems
  • Balancing business needs and technical feasibility
  • Cross-functional collaboration with data scientists and engineers
  • Basics of MLOps (CI/CD for machine learning models)

Resources:

  • Courses:
    • AI Product Management Specialization by Duke University (Coursera) – Detailed on strategy, ethics, and collaboration :white_check_mark:
    • MLOps Fundamentals by Google Cloud – Learn how to deploy and manage ML models in production
  • Books:
    • Lean AI by Lomit Patel – Practical guide to scaling AI products quickly

Activities:

  • Practice creating product requirements for an AI feature (e.g., a recommendation system).
  • Join product management communities focused on AI (e.g., AI Product Managers on Slack).

Cheers,
Bernadette Q.