Insights from AI in Product Management

If you're a product manager and not using generative AI yet… you're falling behind. Over the past few months, I’ve been exploring how PMs across industries are adopting AI - not for hype, but to actually get things done faster and smarter. I got inputs from 300+ product managers and here’s what real product managers are using generative AI for: ↳ Summarizing customer feedback from surveys and reviews ↳ Writing better PRDs, FAQs, and user stories (yes, even from Figma screens!) ↳ Brainstorming product ideas and outlining go-to-market strategies ↳ Automating SQL queries and documentation ↳ Creating wireframes, mockups, and prototypes in minutes ↳ Preparing pitch decks, emails, and product update announcements ↳ Synthesizing competitor analysis and market research ↳ Managing team workflows and Slack/Notion chaos with AI agents From ChatGPT and Claude to Notion AI, Cursor, and Replit - PMs are building powerful workflows around AI. Some have even built their own agents for writing specs or organizing roadmap inputs. The goal? Free up time for deep thinking and high-impact decisions. This isn't about replacing PMs. It's about amplifying what we do best: understanding users, aligning teams, and shipping value. If you’re just starting out, begin small: ↳ Ask AI to rewrite an email ↳ Summarize a user interview ↳ Draft a product update from bullet points You’ll be surprised how quickly it becomes your second brain. Are you already using AI in your product workflow? Follow Lokesh Gupta for more such insights.

𝗔𝗜 𝗠𝗮𝗸𝗲𝘀 𝗚𝗿𝗲𝗮𝘁 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀 𝗚𝗿𝗲𝗮𝘁𝗲𝗿 - But It Won’t Save Poor Thinking AI won’t make you a better product manager. It 𝗮𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝘀 the skills you already have—or don’t. A great PM doesn’t start with prompts. They start with 𝗰𝗹𝗮𝗿𝗶𝘁𝘆: a real problem, a business need, and the thinking to connect the dots. But here’s the good news: If you’re already strategic, AI can make you 𝗳𝗮𝘀𝘁𝗲𝗿, 𝘀𝗵𝗮𝗿𝗽𝗲𝗿, 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲. Here are 𝟱 𝘄𝗮𝘆𝘀 𝗴𝗿𝗲𝗮𝘁 𝗣𝗠𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄—and how you can too: 1. 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗳𝗮𝘀𝘁𝗲𝗿 & 𝗱𝗲𝗲𝗽𝗲𝗿.    Great PMs understand their market →    Use AI to summarize earnings calls, analyze reviews, extract competitor positioning, or generate trend reports across industries in seconds.     2. 𝗕𝘂𝗶𝗹𝗱 𝘀𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗳𝗹𝘂𝗲𝗻𝗰𝘆.    Great PMs think like CFOs →    Use AI to break down unit economics, simulate pricing models, run revenue impact scenarios, or benchmark competitor pricing.     3. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗵𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀 𝗺𝗼𝗿𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆.    Great PMs don’t guess - they test →    Use AI to quickly draft multiple positioning statements, survey questions, or user interview scripts.    Ask AI: “𝘞𝘩𝘢𝘵 𝘢𝘴𝘴𝘶𝘮𝘱𝘵𝘪𝘰𝘯𝘴 𝘢𝘳𝘦 𝘸𝘦 𝘮𝘢𝘬𝘪𝘯𝘨—𝘢𝘯𝘥 𝘩𝘰𝘸 𝘤𝘢𝘯 𝘸𝘦 𝘵𝘦𝘴𝘵 𝘵𝘩𝘦𝘮?”     4. 𝗥𝗲𝗰𝗼𝗴𝗻𝗶𝘇𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗵𝗮𝘁 𝗱𝗿𝗶𝘃𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆.    Great PMs spot signals early →    Use AI to synthesize internal feedback, sales calls, support tickets, and roadmap themes to surface patterns others miss.     5. 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 & 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝗮𝘁 𝗹𝗶𝗴𝗵𝘁𝗻𝗶𝗻𝗴 𝘀𝗽𝗲𝗲𝗱.    Great PMs move ideas forward →    Use AI to generate mockups, create product briefs, or prep storytelling decks that get stakeholder buy-in faster.     AI won’t teach you product thinking. But if you’re already building that muscle, it will take you from good → great → unstoppable. 👇 Which of these are you already using - and what would you add? #ProductManagement  #StrategicThinking

While building Planbow, I realized that a product manager needs market insights more than marketing and sales teams, and that’s the biggest reason a modern PM should be equipped with AI superpowers. Let’s understand why: Matching the speed of development- As we are seeing development co-pilots, low-code and no-code tools are ready with their disruptive capabilities and now building software is possible in weeks. Matching this agility with conventional product management will become the bottle-neck. Data-Driven Decisions- A product manager needs to make decisions based on ever-changing market dynamics, customer behavior, and competitor strategies. AI helps in gathering and analyzing vast amounts of data quickly, providing actionable insights that go beyond traditional research methods. Predicting Trends- AI can analyze historical data and predict future trends, enabling product managers to stay ahead of the curve. This is crucial for crafting features and strategies that resonate with future market needs, not just current demands. Customer Insights- Understanding customer pain points and preferences is key to successful product development. AI-powered tools can analyze customer feedback, reviews, and behavior in real-time, helping PMs refine the product roadmap. Efficiency in Execution- AI can automate repetitive tasks like A/B testing, performance tracking, and even certain design decisions, allowing product managers to focus on strategic initiatives that drive growth. Personalization- In today’s competitive landscape, personalization is everything. AI allows product managers to create highly personalized user experiences based on data, ensuring that the product remains relevant to diverse user segments. In short, AI empowers product managers to make smarter, faster, and more precise decisions, ensuring that their product stays competitive and innovative in a constantly evolving market.

The best product managers of 2025 won't be the ones with the best intuition. They'll be the ones who learned to orchestrate human creativity with AI capabilities. Kevin Thomas and I have been tracking this shift across dozens of product teams. He's leading AI integration at IBM, while I'm seeing the ripple effects across the broader PM community. In this post, we’re sharing our findings. We're in the middle of the most significant skill evolution product management has seen since the move from feature factories to outcome-driven teams. 𝗧𝗵𝗲 𝗱𝗮𝘁𝗮 𝗶𝘀 𝗰𝗼𝗺𝗽𝗲𝗹𝗹𝗶𝗻𝗴: 🔹Teams using AI for insights are 3x faster at identifying user problems 🔹AI-assisted prioritization correlates with 40% better feature success rates 🔹Predictive user research is replacing reactive surveys at leading companies This shift isn't about replacing human judgment but augmenting it with capabilities we never had before. We broke down 7 specific areas where this transformation is happening currently. Swipe through to see which changes are already impacting your daily work (and which ones you should prepare for next). 👇

We are in a pivotal moment for product managers. Just as "mobile-first" reshaped how we designed and delivered products over the past decade, we are now in the AI-first era; one that is fundamentally altering the product management landscape. But here's the thing, many companies are still approaching AI as a bolt-on. They are adding chatbots, AI-powered search, or co-pilots to enhance customer experiences. These are valuable, but they often don't push the true capabilities of what is possible. The few companies that will define the next decade are going deeper. They are not just adding AI features, they are rearchitecting their core systems to be AI-native. They are making AI the engine that powers decision-making, automation, and user experiences from the ground up. These companies are not just AI-enhanced, they are AI-first. As product managers, we cannot afford to be on the sidelines. We need to shift our mindset: ✅ Instead of asking, "Where can we add AI?", ask "What would this product look like if AI was at the center?" ✅ Move from feature roadmaps to intelligence roadmaps. ✅ Partner deeply with ML, data, and infra teams early in the lifecycle. ✅ Design UX that adapts to dynamic, personalized, and probabilistic outputs. ✅ Understand how to validate and measure the performance of AI systems, not just usability. ✅ Build for edge cases, bias, explainability, and continuous learning loops. AI is not just a technology trend, it is becoming the foundation of modern software frameworks. And companies know this. In the coming months and years, hiring managers won't just look for PMs who "understand AI". They will seek product leaders who can ship differentiated AI-native products, those who deeply understand what's uniquely possible because of AI. So if you are in product or are thinking of transitioning to product, ask yourself: 🔹 Are you treating AI as an enhancement or as a core capability? 🔹 Are you up-skilling fast enough to lead in this new wave? 🔹 Are your roadmaps AI-enhanced or AI-first? Because the next generation of technology builders are not just building better UX, they are building smarter systems. And they will win not just by shipping faster, but by shipping products that learn and evolve rapidly using AI. This is the most important shift in product management since mobile. Let us not miss it. What is your team doing to go AI-first?

🚀 The craft of product management is changing. Fast. A few days ago, I sat down with Vimarsh Puneet. What started as a quick 5-minute chat turned into one of the most eye-opening demos I’ve seen in a while. Vimarsh showed me how AI is reshaping the day-to-day work of a PM — not by adding more tools, but by helping us think and build differently. Imagine this 👇 You describe the intent of a project — say, “I want to analyze customer pain points, personas, and competitive solutions for an on-prem AI agent.” In seconds, an AI agent spins up an entire directory structure with: - competitive analysis, - segmentation, - customer data sheets — all in markdown, ready for you to iterate with. Need to analyze adoption trends or update features in Aha!? The agent does it. Want to ideate on next steps? You can literally have a conversation with your tool. It’s VS Code + Copilot + Claude Sonnet 4 + MCP integration, all working together to 10x what a PM can do. What struck me most wasn’t the automation — it was the mindset shift. PMs aren’t just writing specs anymore. They’re writing intent — and the AI builds the spec. This is what I mean when I talk about bringing a founder mindset into product management. A future where PMs lead small “teams” of AI agents, each contributing analysis, insights, and ideas — so we can focus on vision, creativity, and customer empathy. If you’re a PM today, this is your moment to reimagine your craft. AI isn’t replacing what we do — it’s expanding how far we can go. 🎥 Here’s the full 5-minute conversation with Vimarsh.

Review: AI for Product Managers by Valerio Zanini [ https://lnkd.in/gcbNjfNa ] AI has quietly become the newest “table-stakes” PM skill—like analytics, experimentation, or SQL once did. Not because every product needs a model, but because every roadmap now intersects with AI expectations: leadership wants “AI everywhere,” customers expect ChatGPT-level experiences, and competitors ship faster using GenAI-assisted workflows. That’s why Valerio Zanini’s AI for Product Managers landed well for me: it’s not trying to turn PMs into ML engineers. It’s trying to make PMs dangerous enough—able to reason about what AI can (and cannot) do, how AI changes product risk, and how to ship AI features without burning trust. What I appreciated is that the book consistently reframes AI as a product design and risk-management problem, not a model-selection beauty contest. Zanini’s thesis is simple: PMs live in the gap between AI hype and AI reality, and the job is to translate across users, engineers, executives, and constraints. Key Insights & Takeaways 1) PMs aren’t building models—you’re building the product around a probabilistic engine 2) A simple value test for AI ideas: Accelerate, Expand, Simplify 3) The “defensibility” question: where in the AI stack do you create value? 4) Data is product strategy (and most failures are data failures) Critical Analysis Strengths * Clarity without condescension: It’s written for practicing PMs; you’re not expected to become an ML specialist. * Practical scaffolding: quizzes, worksheets, and prompt templates (especially in the “AI for PM work” sections) make it actionable rather than theoretical. * Product-risk realism: hallucinations/bias/brittleness are treated as core product risks, not edge cases. * Strong strategic framing: AI-ready vs AI-first is explained as a real business distinction, not a marketing slogan. Limitations / where I wanted more * More depth on Evals and operationalization would help teams already shipping AI at scale (though the book does cover Evals, monitoring, and retraining). * Model/provider landscape moves fast. Any book risks “tool references aging,” so readers should treat specific model mentions as examples, not prescriptions. (The strategic principles hold up well.) I recommend this book to PMs who want to (1) understand AI without drowning in math, (2) ship AI features responsibly, and (3) build a shared mental model across product, design, and engineering. Rating: 4.7 / 5

Reforge nails something most AI-and-PM content misses: AI doesn't replace product managers. It eliminates the low-value work and exposes who was actually good at the job all along. I've been in product, operations, and client implementations in financial services for over two decades now. The parts of my job that AI is accelerating are real: drafting specs and documentation faster than ever, surfacing patterns across thousands of client interactions, compressing research that used to take weeks into hours. But it goes well beyond execution. I can pressure-test an idea against market data in minutes. Run competitive analysis across dozens of platforms before a single stakeholder meeting. Prototype a concept, get it in front of users, and validate assumptions before my team writes a line of code. The ideation-to-validation loop that used to take a quarter now takes days. That's not incremental. That's a fundamentally different way of building product. But the parts that actually determine whether a product succeeds? Those haven't changed one bit. Knowing which feature to kill even when your biggest client is screaming for it. Sensing that an implementation is going sideways three weeks before anyone raises a flag. Sitting in a room with commercial, engineering, operations, and sales, all pulling in different directions, and finding the one path that actually moves the product forward. No model does that. Not yet. Maybe not ever. After two decades of building products in operationally complex segments, I can tell you this: the best PMs were never the ones who wrote the most detailed PRDs or ran the tightest sprint ceremonies. They were the ones who could walk into ambiguity, make a call with incomplete information, and own the outcome either way. AI makes that skill more valuable, not less. When AI handles the busywork and supercharges your research, there's nowhere left to hide. Your judgment is the product. The PMs who will struggle aren't the ones who lack AI skills. They're the ones who built their entire careers on coordination and documentation instead of judgment and taste. AI just removed the cover. And honestly, that's a good thing for the profession. #ProductManagement #AI #FinancialServices https://lnkd.in/ea3bejPU

I think Product Management has changed more in the last year than in the previous 10 years combined. Tasks that used to take hours and even days can now be done in minutes. Or even completely automated. Here are a few real world examples in our little team - 1) For customer feedback, the team has been using GitHub Copilot in agent mode against feedback datasets to analyze feedback at scale—getting to insights in minutes that used to take hours of manual KQL and verbatim reading. 2) On prototyping, Claude Code and the Figma MCP have made it possible to go from concept to interactive prototype without lengthy spec handoffs, with one key finding along the way: describing the user experience you want produces far better AI-generated code than describing the implementation. 3) On bug fixing, Copilot in VS Code and AzureDevOps has enabled the team to take bugs or UX tweaks that surface in meetings and turn  them into working PRs the same day—without pulling an engineer off their work. 4) And on collaboration, the team has been experimenting with AI-native prototype-first working environments where prompts, PRDs, and technical specs can be generated and iterated in real time across PM, Design, and Engineering. They are not just "AI projects" anymore. They are part of the core PM workflow now. Just like writing a .docx PRD was in the past.

Some think AI will replace product managers. They’re wrong—it’s about to make them more important than ever.   Tools like Claude Cowork and Claude Code mark a fundamental shift—AI is no longer just answering questions, it is executing workflows to deliver results.   What this means for product managers: • The value of a PM shifts to what AI cannot do well: deciding which problems matter, defining outcomes, setting trust boundaries, and orchestrating how humans and agents work together. • The product is no longer just an interface—it is a system of work.   How PMs can work more effectively: • Focus less on writing specs and more on designing workflows. • Define clear outcomes, guardrails, and human-AI handoffs. • Think in loops (capture → act → learn), not features. • Act as the “agent manager”—directing AI agents toward outcomes that actually matter.   Bottom line: As AI commoditizes execution, product management becomes the function that turns capability into value.   #AI #ProductManagement #Leadership #Innovation #AITransformation