How AI Will Change Product Management

The role of product management, especially for AI-based products, is changing a lot. Interestingly, a significant number of products are becoming "AI-based" products. You'll often see requests for a stronger technical background alongside traditional PM skills. It's not enough to just know the market and users anymore; product managers now need to understand things like algorithms, data pipelines, and machine learning. This isn't a small change; it's a real shift in what's required. It’s not about knowledge of a toll but the technology. I'm seeing this trend firsthand. Look at product manager job descriptions, and "understanding or working knowledge of AI" is becoming standard. We're also seeing more data scientists and AI engineers moving into product management. This isn't just a career switch; it's a sign that technical knowledge is crucial for building good AI products. For people without this background, it's a big challenge, requiring a lot of learning and a willingness to try new things. Being able to explain complex technical ideas in a way that users understand is now a must-have skill. The key to AI product management is balancing big ideas with what's actually possible. Without understanding AI's strengths and limitations, product managers can easily get swayed by marketing hype or struggle to create realistic roadmaps. It's the difference between a dream and a practical vision. Equally important is building strong communication with engineering teams, not just for technical alignment but for building trust. Don't believe the idea that you don't need technical skills in PM. This trend is only going to get stronger. It's better to adapt and learn than to struggle later. #ExperienceFromTheField #WrittenByHuman

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

AI didn’t just change how we build -  it changed what we need to build. What once required months of work and a team of engineers now takes hours and a few prompts. The cost of building has collapsed. But here’s the real question:  Does anyone actually need what you're building? As AI democratizes speed and scale, the real differentiator isn't velocity -  it's clarity. Knowing what to build, when, and why. These are the skills product managers and product leaders need to double down on: 1.  Financial & Market Fluency Understand the levers your customers care about. What are they solving for 𝘳𝘪𝘨𝘩𝘵 𝘯𝘰𝘸? How are macro shifts reshaping the problem space?     2. Discovery Mastery Dig deeper than feature requests. See the unspoken needs. Ask better questions and connect the dots others miss.     3. Hypothesis-Driven Mindset AI tools make testing faster and cheaper — use them. Explore bold bets without overcommitting. Ship learning, not just features.     4.  Strategic Prioritization Just because you 𝘤𝘢𝘯 build something doesn’t mean you should. Tie product bets to long-term outcomes. For platform PMs, that includes balancing internal vs. ecosystem value.     5. Relationship Building Talk to customers. Align with stakeholders. Influence across functions. Empathy and trust are still your sharpest tools.     6. Storytelling Your ability to shape a vision, influence decisions, and rally teams depends on how well you tell the story — especially in a world flooded with noise. In a recent mentoring session, someone asked me: “With AI evolving so fast, how do I stay relevant as a PM?” This is how. Use AI to accelerate execution -  but build your edge in the skills AI can’t replace. 👇 Which of these are you investing in this year? What else belongs on this list? #ProductLeadership #ProductStrategy #ProductManagement #AI #PlatformProducts #

Most people are talking about how AI speeds up product development. But that misses a more powerful and completely different benefit… Yes, as we shift to AI-native product teams AI will help accelerate existing product development cycles. But as Scott Belsky, CPO of Adobe and investor in Reforge highlighted in his newsletter (link in comments): “What makes this technology truly distinctive from other advances is its reasoning and imaginative capabilities (not taste-based imagination, but boundless directed exploration). What this technology really gives us is MORE CYCLES - more cycles to explore” Here is why this is so important… In today’s world, most product teams face pressure to ship often. Teams can’t afford to explore many solution paths. This creates three problems: 🚫 High-stakes decisions based on limited data 🫡 Solutions optimized for internal consensus rather than customer value ✂️ Innovative approaches killed by time constraints before they can prove themselves But with an expanded capacity to explore: ⤇ Multiple interface designs ⤇ More prototypes ⤇ Dozens of copy variations ⤇ Different GTM narratives ⤇ and more… What would have historically been impossible can now happen in parallel, increasing both the quantity and quality of product decisions. In other words, AI isn't just accelerating our existing processes; it's fundamentally changing how we discover and validate product opportunities. Shifting to AI-native product teams won’t be about accelerating existing processes, it will be about asking - "What’s possible now that wasn’t before?" More thoughts on AI-native product teams in the comments...⬇︎⬇︎

Everyone thinks AI will eliminate product manager jobs. They're wrong. Orgs will need more PMs, not fewer: It’s a counterintuitive truth. When building becomes easier, deciding what to build becomes harder. Think about YouTube. When publishing videos became simple, we didn't get fewer content creators - we got an explosion of content. Now the challenge isn't the technical barrier, it's deciding what's worth making. The same thing is happening with product development. AI can help you go from idea to prototype in a week instead of six months. But when you can build anything quickly, the real skill becomes knowing what's worth building. This is what product managers do: decide what to build. The bottleneck shifts from "can we build this?" to "should we build this?" That's not a job for AI - that's a job for more strategic PMs.

I’ve been testing AI, shadowing PMs, and talking to hundreds of product managers about how they’re using AI in their workflows. Here’s what I’ve found. — 𝗧𝗛𝗘 𝗨𝗦𝗘 𝗖𝗔𝗦𝗘 𝗙𝗔𝗟𝗟𝗔𝗖𝗬 Most PMs think they know how to use AI. They can ask ChatGPT to draft emails, create summaries, or brainstorm features. But where they struggle is knowing how to integrate AI in a way that truly transforms their workflows. Last month, I shadowed a PM at a FAANG company working on a new feature spec. Their first AI prompt? Beautifully crafted but completely off the mark for their use case. The result? Wasted time, resources, and momentum. What matters isn’t just using AI. It’s using it the right way. — 𝗧𝗛𝗘 𝗡𝗘𝗘𝗗 𝗙𝗢𝗥 𝗔𝗜-𝗣𝗢𝗪𝗘𝗥𝗘𝗗 𝗣𝗠𝘀 Remember the classic PM nightmare? The clock’s ticking, it’s 4 PM, and your VP just asked for a detailed PRD — due first thing tomorrow. Well, it used to be a hurdle, but today it’s not, thanks to AI. That's why, AI is no longer optional for PMs. It’s the difference between: → Struggling with last-minute PRDs Or having an AI help you write one in 20 minutes → Spending hours on competitor research Or letting AI pull insights in 30 minutes → Losing hours prototyping manually Or iterating design ideas in real-time with AI tools The PMs who figure this out are going to 10x their impact. And those who don’t will fall behind. — 𝗪𝗛𝗔𝗧’𝗦 𝗜𝗡 𝗧𝗛𝗘 𝗡𝗘𝗪𝗦𝗟𝗘𝗧𝗧𝗘𝗥 𝗣𝗜𝗘𝗖𝗘 This is the exact focus of this week's deep dive: → The 3 Rules of Using AI Right → Top 5 AI Use Cases That Actually Save Time → The Mistakes Most PMs Make (and how to avoid them) Don’t miss it: https://lnkd.in/er5E5Buf

Steve Jobs once observed that the disease of big companies is their ability to confuse process for content. He warned that organizations eventually favor process because more people excel at process than content creation, leading companies to become fixated on the means rather than the ends. Process is the "how" — the frameworks, meetings, documentation, workflows, and operational mechanics of getting work done. Content is the "what" — the actual products, features, and experiences that deliver value to customers. It's the creative output that matters. With the rise of AI, this insight has become more profound and urgent than ever before. AI excels at exactly what our organizations have spent decades optimizing: executing processes, following rules, and automating repetitive tasks. As these capabilities are increasingly handled by AI, what remains uniquely valuable is human creativity, insight, and vision—the very "content" that Jobs spoke about. Yet here's the paradox: Just as human creativity becomes our most critical differentiator, our organizations continue pushing us toward process orientation. Product teams spend their days in roadmap reviews, status updates, and framework applications rather than in creative exploration and customer discovery. We're strengthening the very muscle that AI is rapidly making obsolete while neglecting the creative capacity that makes us irreplaceable. Consider the iPhone. It didn't emerge from a perfect roadmap review or a flawless OKR execution. It came from Jobs' obsession with the content—the experience, the interface, the feeling of holding the internet in your hand. He famously bypassed normal processes, creating a secretive, content-focused team that prioritized the creative vision over established procedures. The most successful AI products aren't emerging from perfect PRD templates or flawlessly executed OKR processes. They're coming from teams that give themselves permission to explore, create, and iterate rapidly—teams that prioritize content over comfort. For AI product leaders, this means: 1. Automating process work. Use AI to handle the processes that consume your creative energy. Let it draft your status reports, summarize meetings, and track metrics so you can focus on the creative work only humans can do. 2. Creating space for genuine creativity. Carve out significant time for exploration, ideation, and customer interaction. Your most valuable contribution isn't managing process—it's discovering the unexpected insights that lead to breakthrough products. 3. Rewarding content over process excellence. In a world where AI can execute processes flawlessly, we need to shift our reward systems toward valuing creative output, novel insights, and customer impact. As AI increasingly handles the how, humans must focus on the what and why. The companies that thrive will be those that use AI to handle process work while unleashing human creativity to focus on content—the true source of value.

One of the most thought-provoking take on AI product development from Kevin Weil, CPO at OpenAI. He joined us at #MicrosoftPMCon yesterday and shared hard-earned wisdom from the front lines of building ChatGPT and other cutting-edge AI products. Here are 5 insights that stuck with me—and how I’m planning to apply them in my own work: 🧠 1. Start with deep intuition, then validate with data. Most PMs are taught to lead with data. Kevin flipped the script. When the frontier is unknown, instinct and imagination come first—data comes second. → I'm learning to trust my gut more in v1, especially when shaping ambiguous AI-powered community experience. 🏗️ 2. Avoid “waterfalling” your way into irrelevance. Many teams overplan and overpolish their launches. But in AI, where the pace is relentless, speed beats perfection. Kevin’s advice: “If it’s not a little embarrassing, you shipped too late.” → I’m pushing myself to test earlier, even with imperfect UIs or hacky demos. 🤝 3. Let customer pull guide the roadmap. AI can do so many things—it’s tempting to build for everything. But the best teams watch for pull: where users naturally find value and beg for more. → I’ve shifted from “what can we build” to “what are people already trying to do with this?” 🧱 4. Reinvent interfaces from the ground up. Kevin challenged us to stop slapping AI into existing UI patterns. The next generation of products will require new mental models—more fluid, conversational, and anticipatory. → That means rethinking onboarding, success metrics, and even the role of PMs in this new era. 🪄 5. AI will reward curiosity and scrappiness. Kevin said it best: “The most successful people in AI are the ones willing to try weird things fast.” That stuck with me. It’s not about being the smartest in the room. It’s about being the one who’s willing to learn out loud, build publicly, and stay playful. Most underrated takeaway? ✨ The best PMs in AI aren’t afraid to look a little silly while they’re learning. Curious—what’s the most unconventional lesson you’ve learned while building with AI? I’d love to hear it. —  👋 Hi! I’m Shyvee, I share insights on AI, product making, and the future of work. Subscribe for AI insights, programs, and an invitation to our AI Enthusiast Community: https://lnkd.in/eR2ebrEM #ProductManagement #AI #MicrosoftLife

AI will eliminate the need for product managers. 🥴 Close, but not correct. Product management hinges on judgment, empathy, and strategy— areas AI can’t replace. Rather than replacing product managers, AI will remove the constraints that have always made the "do it all" product manager an impossible myth. You’re expected to be strategic, fast, and technical, but there’s never enough time. AI can finally level that playing field. Two fundamental constraints have always limited product managers: 1. The skill gap - developing expertise across design, business, and technology 2. Time constraints - not enough hours to execute well across all areas While AI won't magically close the skills gap (we'll still spend careers developing expertise), it dramatically changes the time equation. Competitive analysis that took 8 hours now takes 30 minutes. Here are 3 ways AI transforms product management: 1. Speed to insights: Research and analysis now happen at hyperspeed. Yesterday I synthesized notes and recordings from 4 hours of customer interviews in 30 minutes— previously a half-day’s work. 2. Prototype-to-production acceleration: Vibe coding lets us test ideas quickly, collecting user feedback faster and communicating more effectively with engineering. 3. Automated product analytics: Soon, AI will create dashboards and reporting on product outcomes without us having to put it all together manually. We’ve never had enough hours to live up to the ideal product management described in the books. While expectations for product remain astronomically high, AI gives us the ability to increase our output and maybe (finally) meet that bar. Read my full article diving into how AI will make product management faster and where product ops plays a role. Link down below.

“AI will destroy product managers” well…We’ve built 50+ AI solutions for huge companies, and that’s how I know product management is now MORE valuable with AI, not less. Think of it this way. Thanks to AI, the cost of adding a feature is close to 0. Cursor / Claude / GPT makes it incredibly easy. And when it costs 0 to add features, people will add everything in the world. They'll add every single feature possible, and the user experience will be horrible. Because yes, technically, you can have a HubSpot that also is a Monday that also is a Figma that is also a QuickBooks. What user wants that experience, though? The product manager’s job is to understand what the minimal use case actually is. It’s about whether the feature is necessary, not just if it’s cheap to build. That’s why we stopped doing purely cost-based analysis for our clients; we're doing value based consulting, based on the Return of Investment of the entire product (not a feature.)  When anything is possible, you need taste to evaluate whether anything should exist. On software teams, the person with that taste is the product manager.