The demand for AI Product Managers (AI PMs) is skyrocketing, yet fewer than 1% of aspiring PMs successfully transition into this specialized role. Why the bottleneck? Because breaking into AI product management demands more than just technical know-how. It requires strategic action, hands-on learning, and insatiable curiosity.
In this recap of the Product Insider Podcast, featuring Dr. Nancy Li and Sher Hao, an AI PM at Google, we delve into Sher’s remarkable journey and uncover actionable tips to help you become an AI Product Manager—even without prior experience.
Why AI Product Management is Booming
Artificial Intelligence (AI) is no longer a futuristic concept; it’s integrated into our daily lives, from generating marketing content to powering smart assistants. Sher highlights how tools like Google Gemini AI Studio are making generative AI (GenAI) more accessible than ever before.
Despite this widespread adoption, there’s a significant supply-demand gap. Tech giants like Meta and OpenAI are fiercely competing for top-tier talent, offering compensation packages that can reach up to $100 million for elite AI researchers. So, how can you become a part of this high-impact and lucrative field?
Sher Hao’s Journey to AI PM at Google
Sher’s path to becoming an AI PM at Google is a testament to the power of strategic career navigation:
- She began as a Google APM (Associate Product Manager), initially contributing to Kaggle, a renowned community for data scientists and machine learning competitions.
- Later, she moved to Google Labs to play a pivotal role in launching Gemini AI Studio and developing tools for generative AI.
- Today, Sher leads initiatives focused on bringing cutting-edge AI tools into the hands of developers worldwide.
What Does an AI Product Manager Actually Do?
According to Sher, the responsibilities of an AI PM can vary significantly, often falling into three main categories:
- Model-focused PMs: These PMs collaborate closely with ML engineers to develop and fine-tune AI models.
- Platform PMs: Their role involves transforming AI research into accessible developer tools and APIs.
- Application PMs: These individuals focus on building end-user products powered by AI.
Sher emphasizes that she has experience in all three areas, underscoring that each offers unique rewards and requires a distinct skill set.
How to Break Into AI Product Management
Sher highlights two crucial pillars for aspiring AI PMs:
- Proximity to AI:Even if your current job title doesn’t include “AI,” actively seek out projects that involve machine learning (ML) or AI. Sher’s involvement with Kaggle, for instance, immersed her in data science even before her GenAI roles.Actionable Tip: Get hands-on! Build personal projects using readily available APIs from platforms like OpenAI, Gemini, and Claude.
- Network Strategically:Building a robust network is vital. Join study groups, participate in AI communities, and attend industry events. Connect with AI engineers, researchers, and fellow PMs on LinkedIn. Consider joining paper reading groups, AI bootcamps, or contributing to open-source collaborations.Sher wisely advises, “The best AI PMs I met didn’t just apply GenAI everywhere—they solved real problems using the right tools.”
How to Pick Your First AI Project
Ready to dive in? Sher suggests two effective approaches for selecting your initial AI project:
- Apply AI to your current work: Identify inefficiencies in your existing workflows and explore how AI could provide a solution.
- Build something for yourself: Think of a personal pain point or a creative idea. Examples include an AI bot for auto-replying to group messages or an AI-powered dating message assistant (yes, these real projects exist!).
Pro Tip: Don’t wait for a job offer. Start building real-life projects today. Many bootcamps even simulate team environments where PMs, engineers, and designers collaborate on AI solutions.
What AI Can’t Do (Yet)
Understanding the limitations of AI is as crucial as recognizing its potential:
- Generative AI is not yet reliable for high-stakes decision-making.
- AI still struggles with memory and long-context reasoning.
- In many real-world scenarios, traditional rule-based systems might be more effective.
Case Study: Sher’s nonprofit worked with Bay Area housing organizations to analyze complex local building codes. This proved to be an ideal use case for AI due to its strength in pattern recognition.
Using AI for Social Good: Hack for Social Impact
Sher co-founded Hack for Social Impact, a nonprofit dedicated to building technological solutions for underserved communities. Their impactful AI solutions include:
- A platform that uses ethical data analysis to help identify prisoners eligible for early release.
- A tool assisting housing developers in understanding intricate local building codes.
- One startup born from their hackathon even became a YC-backed company.
The core principle here is problem-first thinking. Don’t start with AI; begin with a real-world pain point and apply AI only if it’s the most suitable solution.
How to Keep Learning and Growing
To thrive in the dynamic field of AI PM, adopt a continuous learning approach:
Adopt a Growth Mindset:
- View life as an ongoing school: Learn from every challenge and experience.
- See every obstacle as an opportunity for personal and professional growth.
Surround Yourself with the Right People:
- You are the average of the five people you spend the most time with.
- Actively seek out communities focused on AI, product management growth, and innovation.
As Dr. Nancy aptly puts it, “Find people who challenge and inspire you. Learn with them, not just from them.”
Resources to Get You Started
- 🛠️ Google Gemini AI Studio: A fantastic playground to build and experiment with GenAI tools.
- 📚 Top 20 AI Product Ideas: Download free ideas (link in the original episode).
- 💻 AI PM Bootcamp: Build and launch a product in just 11 weeks.
- 🌐 HackForSocialImpact.com: Volunteer or find inspiration from impactful social AI projects.
- 🧠 Follow Sher Hao on LinkedIn for more insights.
Final Thoughts
Becoming an AI Product Manager isn’t exclusive to those with a PhD in Machine Learning. If you:
- Stay close to AI through personal or professional projects,
- Focus on solving real-world problems with a problem-first approach, and
- Grow alongside a community of like-minded learners,
…you can absolutely break into this exciting and impactful field.
Ready to build your first AI product?
👉 Check out PMAccelerator.io/AIPM for free resources, bootcamp programs, and live coaching to kickstart your journey!
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