Using ChatGPT doesn’t make you an AI PM. But becoming one isn’t rocket science. Let’s start with the basics and discuss: 1. What is a Product Manager? 2. What is an AI-Powered PM? 3. What’s Special About AI PM? -- 1. What is a Product Manager? We often say that when the product fails, it's the PM's fault. But when it succeeds, it's thanks to the team effort. Failure is something PMs deal with regularly. As Marty Cagan explains in Inspired: „𝑇𝘩𝑒 𝑓𝑖𝑟𝑠𝑡 𝑡𝑟𝑢𝑡𝘩 𝑖𝑠 𝑡𝘩𝑎𝑡 𝑎𝑡 𝑙𝑒𝑎𝑠𝑡 𝘩𝑎𝑙𝑓 𝑜𝑓 𝑦𝑜𝑢𝑟 𝑖𝑑𝑒𝑎𝑠 𝑎𝑟𝑒 𝑗𝑢𝑠𝑡 𝑛𝑜𝑡 𝑔𝑜𝑖𝑛𝑔 𝑡𝑜 𝑤𝑜𝑟𝑘.” Product management is, at its heart, about managing risk. While collaborating with others, the PM is especially responsible for two areas of risk: • Customer value: Do customers want this product or feature? • Business viability: Will it work for different parts of the business? That's why critical activity for a PM is participating in Product Discovery. Its goal is to address key risks before implementation. You can learn more about Product Discovery from this free post: https://lnkd.in/dNUB__n3 -- 2. What is an AI-Powered PM? These terms are often confused: • AI-Powered PM: A PM who uses AI at their work. • AI PM: A PM who works on AI-powered products and features. Here’s my strong opinion: Soon, there will be no such thing as a PM who doesn’t use AI daily. We should be living and breathing AI. Not to replace our thinking. But to boost our productivity and eliminate manual work. Free resources to get you started: • GPT-4.1 Prompting Guide: https://lnkd.in/dt8FxriE • Anthropic prompt engineering: https://lnkd.in/dc-kucif • Prompt engineering by Google: https://lnkd.in/dEU2Y_9v -- 3. What’s Special About AI PM? First, your product involves AI. And the role is a bit more technical. You don’t need a CS degree. But unlike traditional PMs who should be tech-literate, AI PMs must understand the tech and build AI intuition. A critical new element is AI evals. A robust AI evaluation system: • Answers the question: "Does this product actually work?" • Helps your team measure, iterate, and improve in quick cycles. Unlike unit tests, AI evals are closely tied to the PM’s core risk areas. And as an AI PM, even if you’re supported by others, you should regularly: • Look at and label data • Perform basic error analysis • Experiment with prompts That’s the best way to build intuition and take full ownership of the risks. You can learn more about AI evals from this free post: https://lnkd.in/dHUFrkVs --- Hope that helps. What are your thoughts? --- Enjoy this? In my new post, you will find: • 6 more resources to help AI-powered PMs • 10 step-by-step guides to build an AI PM portfolio (RAG, fine-tuning, MCP, agents, prototyping, etc.) No paywall. Some linked resources available with a free trial: https://lnkd.in/dBZUmDHq
Great post! As we embrace the era of AI in product management, it's crucial to emphasize the importance of ethical considerations and responsible AI use. AI PMs should not only focus on technical proficiency but also prioritize transparency, fairness, and accountability in AI-driven products. Building a diverse team with varied perspectives can further enhance the product's impact and inclusivity. Additionally, staying updated with the latest AI trends and fostering a culture of continuous learning will be key to thriving in this dynamic field. Looking forward to more insights and resources!
Thanks for the resources !! The next level of AI Product Managers, in my opinion, will be those who go beyond tooling and step up as strategic gatekeepers who are understanding not just model performance, but ethical risks, failure modes, and longterm business implications. As someone working across fintech and medtech, I've seen firsthand how AI fast fashion adoption should also have fine balance of domain relevant expertise and welldefined guardrails.
The distinction between using AI and building with AI is spot on. What makes AI PMs unique isn’t just the tech—it’s their comfort navigating ambiguity, evaluating outcomes that aren’t binary, and iterating on models that learn after deployment. AI product management blends experimentation with intuition in ways traditional PM frameworks never had to account for.
The job isn’t to “use AI” it’s to ship with clarity, confidence, and impact. As I see it, the future isn’t just about becoming an “AI PM” it’s about becoming a Product Intelligence PM. That means you don’t treat AI like a shiny toy. You use it as smart glue connecting what customers actually need, what product is building, and what drives revenue. That looks like: Surfacing deal blockers before they kill the pipeline Quantifying feature requests with real business impact Closing the loop between feedback, roadmap, and results You don’t need to be a data scientist. But you do need to be fluent in what matters.
This really resonates — especially how you frame the shift from tech literacy to AI intuition. The same is true for data teams: we often talk about “AI-powered” analysts or scientists, but it’s not just about tooling — it’s about how well someone can interpret, question, and challenge what AI produces. Without solid judgment and fundamentals, AI doesn’t just increase productivity — it increases the speed of confidently wrong conclusions. Whether in product or data, “AI-powered” should also mean “AI-aware"
Ignoring the first one (what a PM is—one would hope that our community had some clarity on that already), these are not definitions that match what I’ve often seen or heard over the past couple of years. What I’ve often seen is that an “AI PM” is a PM whose products are AI-powered or AI-related or have some AI component, and have performed the whole Product cycle with one or more of those AI components / features / functionalities, without necessarily being “more technical”—the point is still discovery, definition, strategy, and the tech side is still coming from engaging with engineers and engineering leaders. “AI-powered PM” isn’t something I’ve heard often (considering myself blessed for that), but it’d make me assume it’s a non-human PM, similar to an AI-powered support rep being essentially a chatbot.
I get where this is coming from, AI feels new and big and a little intimidating. But I’m not sure we need new job titles every time the toolkit evolves. When mobile took off, we didn’t start calling ourselves “iPhone PMs.” We just rolled up our sleeves and figured it out. Same with the web. Same with cloud. Same with AI. Good PMs don’t chase labels. They stay close to the problem, learn the tools, and keep shipping. Let’s not make it more complicated than it is.
Owning a calculator doesn’t make you a CFO 😉 For me, the future of product management is about tying every decision back to customer needs and business impact! That’s when AI stops being a tool and starts becoming part of the strategy. I’m seeing product leaders using Bagel AI shift from experimenting with AI to actually making it part of how they think - becoming more and more domain experts. Not just to move faster, but to bring real clarity to the hardest part of the job: deciding what to push forward and why it matters.