How to Become an AI Product Manager with No Experience

Your Roadmap and Resources to Break Into the Hottest and Fast Growing Area of Product Management: the Artificial Intelligence (AI) PM at companies like Nvidia, Meta, Microsoft, and OpenAI

Contrary to most people’s fears about AI, the most immediate short-term impact has been a huge increase of demand for AI product managers.

While core product management and tech roles have been slow to grow, AI and AI product manager roles have continued to grow - fast.

In the overall tech landscape, AI represents 20% of open jobs. And we’re seeing something similar in PM.

So if there’s an area of PM to consider focusing on, it’s AI. But there’s no really tactical roadmaps on how to get there.

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Introducing Dr. Nancy Li

I’ve teamed up with Dr. Nancy Li, who runs the PM Accelerator.

She’s running a free workshop on how to become an AI PM tomorrow September 25th. Sign up!

She also previously joined us for How to Really Succeed in the Product Management Interview Process in 2024, which many of you loved.

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Today’s Post

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1. The 7-Step Plan

   • Step 1 - Shape Your Content Diet

   • Step 2 - Take Relevant Courses

   • Step 3 - Gain Hands-on Experience is the Key

   • Step 4 - Structure Your Portfolio

   • Step 5 - Network

   • Step 6 - Snag Interviews

   • Step 7 - Ace the Homework and Interview

2. The Roadmap for Students

3. The Roadmap for PMs

   • Early-Career

   • Mid-Career

   • Leaders

4. Most Common Mistakes

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1. The 7-Step Plan

So many people ask us, “so what should I do next?” The roadmap is here, but the tactics step-wise can be confusing. So that’s the subject of today’s post.

Jump to the step where you are in the text to get more tactical on the next steps.

Step 1 - Familiarizing Yourself with the AI Landscape

AI is a million different concepts.

As an aspiring AI PM, you need to develop a broad understanding of the field without getting lost in the technical weeds.

It helps to have a good understanding of the terms and their history.

Let's break down how to approach this initial exploration. Our main recommendation is to shape your content diet.

Start with YouTube. It's a goldmine of accessible AI content.

Subscribe to the following channels to get steeped in AI culture:

1. Jeff Su: This Google PMM makes videos about how to use AI productively at work, like his recent guide to Perplexity.

2. Dwarkesh Patel: He is the most prepared podcast host in the industry. Check out his legendary conversation with OpenAI founder Ilya Sutskever.

3. Matt Wolfe: He has simple and digestible tutorials and breakdowns of AI news.

Next, dive into newsletters. These will help you learn what you need and keep you updated:

1. Everything you need to know about AI (for PMs and builders) in Product Growth

2. The Batch by DeepLearning.AI: Curated AI news with explanations from Andrew Ng.

3. Stratechery: While not AI-specific, the classic often covers AI in context.

Then, get onto Twitter (AKA X).

1. Santiago (@svpino): A computer scientists who teaches hard-core ML

2. Min (@minchoi): Covers all the latest AI news

3. Paul (@itsPaulAi): AI educator

4. Rowan (@rowancheung): AI news

5. Levels (@levelsio): Building products with AI

Finally, update your LinkedIn follows. We’re all on this platform, so you might as well get useful content while you’re there. Follows worth it are:

1. Zain Khan: AI news and updates

2. Allie K. Miller: Former Amazon AI creator

3. Ruben Hassid: AI comparisons and tutorials on the regular

Their posts will give you a pulse on the latest AI trends and debates.

By immersing yourself in this content, you'll start developing an intuition for AI's capabilities and limitations. This foundation will be crucial as you move forward in your AI PM journey.

As you consume this content, focus on understanding key concepts and their potential applications.

You don't need to grasp every technical detail, but you should start recognizing terms like machine learning, deep learning, neural networks, NLP, and computer vision.

Pay attention to AI's real-world applications. How are companies using AI to solve problems? What ethical concerns are being raised? Which industries are being disrupted?

To solidify your learning, try explaining AI concepts to non-technical friends. If you can break down complex ideas into simple terms, you're on the right track.

Remember, at this stage, breadth is more important than depth. You're building a mental map of the AI landscape, not becoming an AI researcher.

Your goal is to develop enough understanding to have meaningful conversations about AI and spot potential product opportunities.

Next up, we'll dive into more structured learning with online courses.

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Step 2 - Taking Relevant Courses

Now that you've reshaped your content diet, it's time to add some structure to your learning. Courses are like hiring a trainer at the gym.

They give you structure and accountability.

The key here is to focus on courses that blend AI knowledge with product thinking. You're not aiming to become the next AI researcher at DeepMind. Instead, you're building the skills to translate AI capabilities into user value.

Start with these foundational courses:

1. AI for Everyone by Andrew Ng on Coursera: This is your AI 101. It's designed for non-technical folks and gives you a bird's eye view of AI.

2. Elements of AI by the University of Helsinki: Free, interactive, and surprisingly fun. It covers everything from AI ethics to neural networks.

3. Machine Learning Crash Course by Google: Because who better to learn from than the tech giant that's betting big on AI?

These courses will give you a solid foundation. They'll help you understand the key concepts, the potential of AI, and its limitations. But don't stop there.

Next, dive into these intermediate courses:

1. AI Product Management Specialization on Coursera: This is the holy grail for aspiring AI PMs. It covers everything from AI strategy to ethics.

2. AI-900: Microsoft Azure AI Fundamentals: Don't let the Microsoft branding fool you. This course offers practical insights into deploying AI solutions.

3. Andrej Karpath’s Neural Networks Zero to Hero course: The man is a legend and will help you build ChatGPT on your own

As you work through these courses, keep your PM hat on. Always ask yourself: "How could this technology solve real user problems?" or "What new product opportunities does this create?"

Remember, your goal isn't to become an AI engineer. You're building the skills to lead AI product initiatives, to communicate effectively with both technical teams and business stakeholders.

For those who want to go the extra mile, consider these advanced options:

1. CS50's Introduction to Artificial Intelligence with Python by Harvard: It's challenging, but it'll give you a deeper understanding of AI algorithms.

2. Deep Learning Specialization by deeplearning.ai: This dives deep into neural networks. It's like taking a peek under the hood of modern AI.

3. Natural Language Processing Specialization by deeplearning.ai: With the rise of ChatGPT, understanding NLP is becoming crucial for PMs.

As you embark on this learning journey, here are some pro tips to maximize your efforts:

1. Apply what you learn to real-world scenarios. See a problem at work? Brainstorm how AI could solve it.

2. Join online AI communities. Participate in discussions on forums like r/MachineLearning or AI Stack Exchange.

3. Don't just passively consume. Try to explain AI concepts to non-technical friends or colleagues. Teaching reinforces learning.

4. Keep a learning journal. Jot down key concepts, product ideas, or questions you want to explore further.

5. Don't feel pressured to complete every course. Focus on understanding core concepts and their practical applications.

By the end of this step, you should be comfortable discussing AI technologies, their potential applications, and their limitations. You'll be able to see beyond the hype and identify real opportunities for AI to create value.

A word of caution: Don't fall into the certification trap. While certs are okay, they're mostly a marketing tactic—not a golden ticket to AI PM land. Focus on understanding and applying concepts rather than collecting digital badges.

By the end of this step, you should be able to hold your own in a conversation about AI without feeling like you're speaking Klingon. You'll understand the basics of how AI systems work, their current capabilities and limitations, and the ethical considerations surrounding their use.

This was all prep work. Now let’s get into the most important step…

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Step 3 - Gaining Hands-on Experience is the Key

This is really important

We interviewed over 20 AI hiring managers at Google, Amazon, Meta to understand what they are looking for in candidates.

Their unanimous advice was clear. The best candidates are those who have experience in bringing products from 0 to 1, and understand the technical challenges of implementing AI solutions.

Therefore, the most effective way to impress AI hiring managers is to launch an AI product that achieves real user adoption.

If you look up AI product management job descriptions, it’s obvious that almost all AI PM jobs ask for prior hands-on AI PM experience.

Here’s an Uber AI PM:

Basic Qualifications

• 3 years of experience as a Product Manager delivering highly successful ML products. Experience building large-scale AI/ML infra or platform products is highly preferred.

And a Samsung Sr. AI PM:

Experience Requirements

• 10+ years industry experience of proven experience in building AI products.

• Proven experience in end-to-end product lifecycle (concept through deployment and maintenance) is required.

The JDs clearly asked for past experience in building and delivering AI/ML products. Therefore, taking courses is just step 2.

The best way to successfully break into AI Product Management is by launching an AI product. 

While this may seem like a high bar, especially for those who do not know how to code, the rapid advancement of Large Language Models (LLMs) has made it easier than ever.

Tools like Cursor and Claude Sonnet enable you to use low-code or no-code solutions to generate code and create products yourself.

Here’s exactly how to do it