Benefits of AI for Product Managers

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 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

This seems to be on everyone’s mind: how to operationalize your product team around AI. Peter Yang and I recently chatted about this topic and here’s what I shared about how we are doing this at Duolingo. For improving our product: -Using AI to solve problems that weren’t solvable before. One of the problems we had been trying to solve for years was conversation practice. With our Max feature, Video Call, learners can now practice conversations with our character Lily. The conversations are also personalized to each learner’s proficiency level. -Prototyping with AI to speed up the product process. For example, for our Duolingo Chess, PMs vibe-coded with LLMs to quickly build a prototype. This decreased rounds of iteration, allowing our Engineers to start building the final product much sooner. -Integrating AI into our tooling to scale. This allowed us to go from 100 language courses in 12 years to nearly 150 new ones in the last 12 months. For increasing AI adoption: -Building with AI Slack channels. Created an AI Slack channel for people to show and tell and share prototypes and tips. -“AI Show and Tell” at All-Hands meetings. Added a five‑minute live demo slot in every all hands meeting for people to share updates on AI work. -FriAIdays. Protected a two‑hour block every Friday for hands-on experimentation and demos. -Function-specific AI working groups. Assembled a cross-functional group (Eng, PM, Design, etc.) to test new tools and share best practices with the rest of the org. -Company-wide AI hackathon. Scheduled a 3-day hackathon focused on using generative AI. Here are some of our favorite AI tools and how we are using them: -ChatGPT as a general assistant -Cursor or Replit for vibe coding or prototyping  -Granola or Fathom for taking meeting notes -Glean for internal company search #productmanagement #duolingo

Is product management dead? Far from it. It’s evolving. Recently, I’ve been asked by PMs whether the PM role is on its way out—sparked by Claire Vo’s talk at the Lenny & Friends Summit 2024. My interpretation of her main point: Product Management isn’t dying; for those who embrace the change it’s transforming into a role that’s more strategic, impactful, and, yes, even more fun. The status quo often bogs PMs down with busy work: writing status updates, aligning stakeholders, figuring out code ownership, or piecing together how products work today. While necessary, these tasks dilute a PM’s impact. With AI, a seismic shift is underway, enabling PMs to focus on higher-value work and organizational impact. Here are my thoughts on how: 1️⃣ From feature owners to organizational change agents AI turns PMs into organizational change agents—what I call "exponential opportunists,” not just Claire’s triple threats. Imagine customer success teams drafting specs that engineers can execute directly with minimal PM oversight. With AI, PMs amplify velocity across teams to build better products faster. 2️⃣ Sharper strategic focus Deeply understanding a market takes time, research, peer and customer conversations, etc. PMs often miss this while buried in busy work and as a result don’t look beyond their immediate area of ownership. This inhibits their companies and their career growth. It’s not just about what AI will take off their plates, but what it allows them to focus on––getting buy-in tactfully and uncovering the data AI doesn’t have access to. 3️⃣ Faster organizational alignment Discovery and alignment cycles can feel painstakingly slow. When building new products at DocuSign, pre-AI, manually consolidating feedback from sales, customers, and support took weeks. Now with AI, discovery can be done in days which gives PMs more time to drive broader organizational impact and build better products. For leaders, the best teams will reward PMs who embrace AI—not just for efficiency but to elevate how organizations operate and innovate. So no, PM isn’t dead—it’s more essential than ever. At Productboard, we’re using AI daily to gut-check ideas, prep for calls, draft specs, and more, freeing up PMs to polish requirements, collaborate with engineers and designers, and improve processes in the organization. What are your thoughts on the future of product management? #AIinProduct #ProductManagement

When I was working with Brandon Smith, he created the most impressive strategy document I had ever seen. It was full of data and a clear prioritization of customer problems. When I asked him how he created the document, his answer was using an LLM to sort through hundreds of Gong sales conversations to identify big themes in customer needs. He cut down months of manual sales call reviews and user surveys into days of research. Plus... ✅ AI helped him identify competitor strengths and weaknesses by scanning public data, giving him a quick gut-check before diving into deeper research. ✅ AI also helped him structure his strategy document, summarize key points, and the initial outline. However, AI isn’t a magic bullet. Brandon emphasizes that: ⚠️ PMs still need to talk to customers as Al can summarize, but it can't replace the intuition gained from real conversations, body language, and follow-up questions. ⚠️ Al can introduce bias as it selectively pulls insights, meaning PMs must guide the input and double-check findings. Al is a powerful augmentation to your work, if you learn how to use it 📼 Here’s the full conversation. https://lnkd.in/e3D4GeCG Repost ♻️to help your network learn about product management and AI!

Remember "10x Engineers"? Meet the "10x Product Manager" Using AI 🚀 We've all heard the meme about mythical 10x engineers who outperform their peers by an order of magnitude. But what if I told you AI is democratizing the 10x multiplier for product managers? Unlike the exclusive "born-not-made" 10x engineer concept, thoughtful AI collaboration gives ANY product manager the potential to achieve exponential productivity gains. Here's how I'm seeing this transformation happen: The 10x PM × AI Collaboration Model: 1️⃣ Collaborative Problem Definition - Skip the prerequisite of having a perfectly formed problem statement. Work WITH AI to recognize patterns in user feedback and market data, generating multiple problem framings to test. 2️⃣ Strategic Prompt Engineering - Not just "writing good prompts" but strategically providing context, constraints, and success criteria that enable AI to deliver truly valuable outputs. 3️⃣ Enhanced Research Capabilities - Synthesize market trends, competitive intelligence, and user research in minutes rather than days, surfacing insights that would otherwise remain buried in mountains of data. 4️⃣ Rapid Ideation & Validation - Generate 10x more potential solutions and immediately stress test them against different user personas, market conditions, and technical constraints before a single line of code is written. 5️⃣ Rigorous Idea Stress Testing - Pressure test assumptions by having AI play devil's advocate, identify potential failure modes, and surface edge cases that human bias might overlook. 6️⃣ Context-Aware Documentation - Transform scattered thoughts into coherent documents, roadmaps, and communications that reflect your company's unique voice and strategic priorities. The key difference? Unlike the lone-wolf 10x engineer, the 10x PM views AI as an extension of their uniquely human capabilities - amplifying empathy, strategic thinking, and stakeholder management while reducing cognitive load. Let me be clear: It won't be AI taking product management jobs—it will be product managers who master AI taking the jobs of those who don't. The winners in this new landscape will be those who proactively leverage these tools to dramatically uplevel their work and deliver outsized impact. The competitive advantage gap is widening daily. Which side of it will you be on? What are your thoughts? Are you using AI to become a 10x product manager? What approaches have you found most valuable?

Some key takeaways from Lenny Rachitsky for #AI #Product #Managers by Aman Khan Director of Product LLM at Arize AI and was previously at Spotify Cruise Zipline and Apple 🍎: 1. There are three main types of AI product managers: a. AI Platform PMs, who build tools for AI engineers b. AI Product PMs, who build products centered around AI c. AI-powered PMs, who use AI tools to enhance their work 2. Practical ways PMs can use AI tools: a. Generate UI mockups and prototypes quickly b. Analyze customer feedback and transcripts at scale c. Create data visualizations and reports d. Draft product requirements documents and other artifacts 3. Develop specific metrics for your AI initiatives, even if they don’t directly correlate to revenue right away. Track how many prototypes you create and how many ideas are generated from hackathons, and gather feedback on usability. This will help you gauge effectiveness and iterate on your approach. 4. Just as Betty Crocker found success by letting users add eggs to their cake mix, your AI tools should foster a sense of ownership. Avoid full automation; instead, integrate features that allow users to actively participate in the experience. 5. Top AI PMs stand out by: a. Focusing on solving customer problems rather than just implementing trendy AI features b. Thinking critically about the right interface for AI in their products (not always a chatbot) c. Balancing short-term deliverables with long-term exploration of new technologies 6. To thrive as an individual-contributor PM: a. Bring high energy to your work, even when facing uncertainty b. Be comfortable with “wandering” to find new directions for your product c. Use AI tools to amplify your abilities and find signal through noise d. Maintain a sense of fun and curiosity in your work

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

The most valuable thing AI gives us isn't automation. It's time. Time to think. To explore. To work on the complex, ambiguous problems that machines can't solve. I've been watching product teams transform how they work with AI, and the pattern is clear. The winners aren't just using AI to work faster, they're using it to work on entirely different problems. Take user research. Instead of spending hours synthesizing interview notes, AI handles that grunt work in minutes. But here's what matters: those saved hours get redirected toward identifying the deeper patterns, the unspoken customer needs, and the strategic opportunities that require human insight. The same goes for competitive analysis, market research, and basic reporting. AI can pull the data and create the summaries. But connecting those insights to your product vision? Understanding what they mean for your roadmap? That's where strategic product managers become irreplaceable. This isn't about AI replacing PMs. It's about AI clearing the path for the work only humans can do: the messy, creative, strategic thinking that drives real business impact. What would you do with 10 more hours a week to focus on the problems that actually matter? PS: throwback to the Product Weekend in NYC 2 months ago where we discussed this topic in depth.

“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.