AI Fluency for Product Management Professionals

🤖 How you can uplevel on AI/ML as a Product Manager: (from easy to hard) 0️⃣ Play with AI products and find ways to leverage AI into your daily tasks. As a Product Manager, you're probably more interested in effective applications of AI and less the nitty gritty technical details. Playing with AI products helps you build a sense of a good AI product experience vs a bad one. AI products I played with that impressed me: - Canva video generation for clips for my YouTube videos - Leveraging Teal to help rewrite my resume How I leverage AI into my daily tasks: - As a hobbyist translator, ChatGPT is my goto for language translation - I use ChatGPT to help me study for product design interviews by asking it to come up with multiple alternative solutions I've never thought of 1️⃣ Learn the basic building blocks so that you're using industry jargon correctly. SUPER EASY basic courses I’ve taken and enjoyed: - The basics & jargon you need to know: https://lnkd.in/ddN6FN4j (got this rec from Chantal Cox) - Prompt engineering fundamentals (1 hour): https://lnkd.in/dsecmjdB 2️⃣ Following AI creators and role models. Find role models you admire and want to learn from that also teach about AI. My current faves: - Tina Huang - Project ideas & how to leverage AI effectively (https://lnkd.in/dXabURpV) - Peter Yang - AI concept breakdowns, AI applications; he's currently running a 7-day intro to AI course through his newsletter (https://lnkd.in/dSD4hm5y) - Andrew Ng - This guy feels like the Godfather of AI yet explains everything so simply (https://lnkd.in/dkUnSrku) 3️⃣ Build personal projects based off your interests & hobbies. Tutorials I've personally used and recommend: Easy intro project ideas: Tina Huang (https://lnkd.in/dxs27pB6) Build an app with ChatGPT: Peter Yang (https://lnkd.in/dvqCFaMC) Build a chat bot: Andrew Ng (https://lnkd.in/dsecmjdB) 4️⃣ Iterate on this list. 🔄 If you have any recommendations of resources or how to more efficiently learn about AI/ML, I'm all ears! 👩🏻💻

Claire Vo and Joff Redfern are on the leading edge of 'AI for Product' globally. I spoke to hundreds of CPOs & PMs and realised they want to be more aggressive on AI and had so many unanswered questions. I couldn't think of two better product leaders to join us at Sauce's 'AI for Product' fireside in SF to answer these questions. Claire Vo is the CPO of LaunchDarkly ($3B), former CPO at Color & Optimizely, and building the PM AI CoPilot, ChatPRD. Joff Redfern is a Partner at Menlo Ventures, former CPO at Atlassian ($42B) and former VP Product at LinkedIn & Yahoo. 👇 Here are my top 6 lessons (full video in comments) 1. Upskill your product teams to be ‘GenAI fluent’ Claire: “Be aggressive with the tasks your teams offload to AI and normalise it as an executive. For example, on everything I generate with an AI tool, I put a prompt at the bottom and attribute it with an author to say ‘I’m the Chief Product Officer, I use AI tools and you should too'. Empower your team with budget and training.” 2. Embrace paradigm shifts by starting with a small team Joff: “At LinkedIn we took a small team and said ‘this is our mobile team.’ Then this gets moved to a platform team… then eventually it gets diffused throughout every team in the organisation. I see the same pattern emerging with AI. At Atlassian it started with a small team doing a spike which focused on learning and experimentation.” 3. AI enables you to achieve more with less Claire: “The cost of building is collapsing and speed of building is accelerating. Ask yourself - am I shipping as fast as I can? I’m building ChatPRD with 1.5 employees, I’m actually 0.5 of an employee as I do this at nights outside my day job as a CPO. I’m coding at nights and am support after 7pm… I truly believe there will be a 1 person, $5 billion company.” 4. Reimagine workflows from the ground up Joff: ”Many startups are trying to speed up one step of a workflow. But I think the better answer is to step back and ask - now that the marginal cost of reasoning with AI is trending to zero, what does the world look like if we were to reimagine what we’re doing today from the ground up?” 5. Use AI hack weeks to get leadership bought in Claire: “As the CPO of Color I’d run AI hack weeks every 6 weeks. I gave our exec teams pre-reading on how AI works, then they had to automate something (e.g. generate a product marketing video which our CCO had been waiting 6mo for previously). This opened their eyes to what’s possible, they understood the impact of AI.” 6. Generalists will win in the AI world Joff: "9 years ago I wrote an article called the 'PM craft triangle' which says a PM can be a General Manager, Scientist or Artist. It was difficult to be the best at all so I advised to go deep on being the best at one of these corners. But now AI allows you to be at the centre of the triangle and smooth your weaker corners with CoPilots. Generalist skill sets will do well in the AI world." Full video in comments 👇

Here's your free guide to becoming an AI PM in 2025: Knowledge + roadmap from 100s of placements. Ankit Shukla shares everything he has learned as founder of HelloPM, including: 1. Why every PM will become an AI PM 2. The knowledge you need to know about AI 3. The roadmap you need to crack into the field Watch now: https://lnkd.in/eYY3H85T Available Everywhere: Substack: https://lnkd.in/eNPX_XMX Spotify: https://lnkd.in/eyt7agKj Apple: https://lnkd.in/eAEVwr3u This wouldn't have been possible without our sponsors: 1. AI Evals Course: $800 off - https://lnkd.in/ek9ixfDR 2. Maven: $100 off my list - http://maven.com/x/aakash 3. Product Faculty: $500 off - https://lnkd.in/ewuAKVUQ Here were my favorite takeaways: 1. Every PM will become an AI PM, or become obsolete. AI-enabled PMs are using ChatGPT and Lovable to 2-3x their productivity. You're competing against PMs leveraging AI for research to prototyping. 2. Applied AI PMs have 10x more opportunities than Core AI PMs. Most value gets captured in applications, not infrastructure. Think Notion AI built on GPT, not building GPT itself. Connect AI capabilities to real user problems. 3. Problem space beats solution space every single time. Too many wannabe AI PMs jump into neural networks and statistics. Start with user empathy, business challenges, workflow optimizations. AI comes after understanding the problem. 4. Contextualization is your secret weapon. Raw ChatGPT writes essays. Notion AI knows your calendar. The difference? Context. Master prompt engineering → RAG → fine-tuning. Most stop at prompts. 5. Evaluations separate real AI PMs. AI is non-deterministic. You can't ship and pray. Building evaluations for hallucinations, biases, and format compliance is now 50% of the PM job. 6. Marty Cagan's 4 risks still rules everything. Valuable + Viable + Usable + Feasible. AI PMs who focus only on Feasible burn out learning LLM architectures instead of solving real problems. 7. AI agents are productivity multipliers, not magic. Intelligence + Actions + Autonomy = Agent. Start simple: Gmail categorization → auto-reply with calendar link. Don't build Jarvis on day one. 8. Reverse engineer your way to expertise. Study Cursor, Grammarly, Perplexity. Break down user journeys, identify AI use cases, understand evaluation frameworks. Build your version for different verticals. The bottom line? AI PM salaries are 30-40% higher ($254K vs $190K in SF). Competition is brutal. Create a differentiated strategy for success. P.S. If you enjoyed this, check out the full podcast for more. You just can't miss his whiteboard session!

The WORST advice for breaking into AI product management? Build a model or a Gen AI app! 🚫 Here's why 👇 👉 Most advice ends at building. You miss the chance to demonstrate real AI PM skills. 👉 Tuning parameters on a model using pre-cleaned datasets showcases great skills - for an entry-level data scientist. 👉Even if you're filling gaps from a non-tech background, creating genAI and no-code apps is becoming so straightforward that it doesn't stand out much. So, what should you do instead? If a candidate did any two of the following, I'd be far more impressed than if they just built a classifier on toy datasets or a simple chatbot: 🤔 Identify a real-world problem that's ripe for an AI solution. Ensure it's something people would pay to solve. ♻ Use low/no-code tools to create a generative AI prototype. Put it in users' hands, gather feedback, and refine it. 🧐 Observe how your system fails on edge cases and its limitations. Understand what it takes to scale it. Discuss how you'd develop a more robust, testable, scalable, and secure system. 💲 Craft a business case for your decisions. How many users at what price point would make this a viable business? AI business cases have different cost breakdowns! 📈 Define what metrics and KPIs you would monitor for the product. What technical metrics (like latency or accuracy) would you track? How do you translate those into product and business metrics that matter to leadership? 🔭Consider the system's long-term operation - would it continuously learn from user feedback? Does it need periodic retraining? 📢 Keen to learn more about nailing that AI PM job with your product management skills? Check out my free webinar replay, "The Path to AI PM." It highlights the 6 key skills you need to develop & showcase to succeed as an AI PM - sure to accelerate your learning AND clarity on your path forward! (🔗 Link in comments!) #AICareers #ProductManagement #TechTalk #CareerGrowth #AIProjects

You don't need to know how to code to become an AI PM. But you do need these 2 skills: 1. Product sense 2. Technical fluency (a.k.a. enough understanding of AI concepts to make smart decisions) Great AI PMs start with the problem. They know how to: • Identify which problems are worth solving with AI • Collaborate closely with engineers on technical solutions • Own the user experience with a high bar for quality and usability • Anticipate edge cases and design graceful failure modes that protect users Sure, knowing code helps, but it’s not a requirement. What matters most is your ability to translate between business goals and technical realities. You need to grasp: • Which AI tools and techniques apply best • How model performance translates to business impact • What “good” looks like from both an evaluation and user experience perspective When the AI system stumbles, you’re ready with fallback strategies that keep the user experience smooth and trustworthy. If you’re focused on problem-solving, collaboration, and user-centric design, you’re already on the right path to being an effective AI PM. The next step is developing your technical fluency. And learning how to interpret what an AI model can do, what it can’t, and how it maps to real business challenges. ♻️ Share this to help other PMs transitioning into AI. Follow me for practical insights on leading AI product innovation and building impact-driven AI products.

With the constant stream of AI updates, announcements, analyses, and 'cheat sheets,' it's easy to feel overwhelmed and confused. The fear of missing out on AI adoption is real. Here's the thing, though - AI adoption does not require you to be an AI expert. It does, however, require you to have a deep understanding of your processes and your domain. Here is a framework that I've been following to adopt AI in my role: a) Understand your work success metrics thoroughly. What are the key goals and KPIs you need to hit to keep moving forward? For example, in sales, closing deals is probably the most important KPI. b) Identify some of the biggest challenges you face that prevent you from achieving those goals. Write these down for clarity. E.g., preparing well for a 30-minute prospect meeting. c) Get granular, break down those challenges further, and identify the core issues. d) Once you break down the challenges, create hypotheses about where AI can help, e.g., prospect and persona research and their role in the company's growth. e) Once you've reached this point, experiment with tools (like GPT, Gemini, Claude, etc.) to get the best possible output for the challenges you identified. This will require some prompting and tweaking. f) Repeat this step across multiple instances to see the correlation. Observe and, if possible, quantify the impact. g) Finally, collate the results and create a map of areas where AI can have the most impact in your role. Very highly likely that you don't need every new tool/feature out there to get there. Focus on the outcome, and the tool will follow.

𝗨𝗻𝗰𝗵𝗮𝗿𝘁𝗲𝗱 𝗧𝗲𝗿𝗿𝗶𝘁𝗼𝗿𝘆: 𝗪𝗵𝘆 𝘁𝗵𝗲 𝗡𝗲𝘅𝘁 𝗧𝗵𝗿𝗲𝗲 𝗬𝗲𝗮𝗿𝘀 𝗪𝗶𝗹𝗹 𝗥𝗲𝘄𝗿𝗶𝘁𝗲 𝘁𝗵𝗲 𝗥𝘂𝗹𝗲𝘀 Artificial Intelligence is reshaping business, work, and daily life at an unprecedented pace. Here's how to navigate the coming changes: 𝗧𝗵𝗲 𝗦𝗰𝗮𝗹𝗲 𝗪𝗲'𝘃𝗲 𝗡𝗲𝘃𝗲𝗿 𝗦𝗲𝗲𝗻 📈 Generative AI usage has erupted from novelty to necessity in just 30 months. 📈 OpenAI processes 2.5 billion prompts daily or 29,000 every second. 📈 ChatGPT's mobile-only user base now exceeds 540 million monthly active users, and is still climbing. 📈 No previous technology (electricity, internet, smartphones) scaled this quickly. And, AI is improving daily making it even more useful while even more users adopt it. 𝗧𝗵𝗲 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 🚀 Adoption: Each new AI release spreads instantly, compounding usage worldwide. 🚀 Capability: Today's "wow" demo feels quaint after just six weeks. 🚀 Measurement: What counts more—prompts, tokens, agents, or the invisible tasks completed overnight? 🚀 Anyone predicting three years out is either guessing or selling. But the near-term picture is sharpening as we get closer, and it's transformative. 𝗙𝗶𝘃𝗲 𝗠𝗼𝘃𝗲𝘀 𝘁𝗼 𝗠𝗮𝗸𝗲 𝗡𝗼𝘄 1. Run a Personal "Task Audit"   - Track everything you do for a week.   - Identify text, code, image, language, or data-heavy tasks.   - These are prime candidates for AI delegation. 2. Instrument Your Workflows   - Capture time saved, error rates, and decision latency.   - You can't prove improvement without measurement. - Business leaders want to hear about AI benefits in business language 3. Build an "AI Fluency Hour"   - Block 60 minutes a week for team experiments.   - Share both wins and failures openly.   - Shared fluency and best practices beats siloed "centers of excellence." 4. Draft Lightweight Guardrail Policies   - Don't wait for perfect governance.   - Set three clear red lines (e.g., no confidential data in AI, require human review for client work, always attribute sources).   - Update monthly as you learn. 5. Bet on Optionality   - Stay flexible: capital, tools, vendors, even models.   - In uncertainty, adaptation is your best asset. 𝗕𝘂𝗰𝗸𝗹𝗲 𝗨𝗽 The terrain will keep shifting, but progress won't wait. Those who learn and act now will have the best chances to compete and thrive in the expanding age of intelligence. I feel incredibly fortunate to be alive in this moment, as universal access to intelligence changes everything across business, careers, and society itself. 🚀 #AI #DigitalTransformation #Leadership #FutureOfWork

Product managers need to transition from 'Understand customer problems -> find a solution' to '(Understand customer problems -> find a solution) + (Understand what AI can do -> solve a real customer problem)' PMs were always encouraged to understand user problems first and then build a solution. With increasing innovation in GenAI and LLMs, this thought process needs to be evolved. Rather than just understanding customer problems, they now need to also understand what the technology is capable of. Users will only convey the problems that they think can be solved by technology and most users can't imagine what's possible with AI today. I kind of discouraged PMs in the past to get excited about a technology while discussing roadmap, always pushed them to focus on customer problems first. The tables have turned now :)

Building an AI-First Culture: 6 Core Principles for Real Change How do you really build AI First Culture? Transforming into an AI-first organization isn’t just about adopting new tech - it's about reimagining how we think, work, and lead. Here are my six key principles which are helping us and can help any team foster an AI-first culture: 1. Mindset Reset, Not Just Tech Shift  Embrace AI as a mindset change, not just another tool. Don’t wait for perfect conditions, action and experimentation drive learning and create competitive advantage. Curiosity, fast feedback, and adaptable teams are your best strategies.    2. Human-AI Pairing at Every Level  Break out of rigid structures. Rethink work as a mix of automation (where AI takes over the repetitive), augmentation (where humans and AI collaborate), and autonomous decision-making (with humans as ultimate stewards). Design for agility and continuous improvement. Think about every single workflows which can change. Design, Research, Reporting, Content Orchestration across various touch-points, hiring, L/D and so many others.   3. Anchor on Business Impact - Orchestrating Business Value is key for success. Align AI initiatives to clear business outcomes - think Impact, not just technical achievement. Focus on tangible ROI, using metrics that measure real value like efficiency, cost savings, and better decisions.    4. Build AI Fluency Organization-Wide  AI know-how is a must-have for everyone. Recognize your team’s learning needs- engage early adopters, support the cautious, and never dismiss skeptics. Tailor your approach to each group and move everyone forward intentionally.    5. Embed AI Deeply for Compounding Benefits  Get AI into your core workflows and products early. The sooner you start, the more benefits compound over time in automation, building stronger products, and delivering customer experiences.    6. Understand AI’s Limits—And Check Its Work  Large language models are powerful but not infallible. Don’t blindly accept their output. Use them as tools, provide clear context, and always verify their work. Leverage the right tools to boost accuracy. Data governance is key here.   At its core, AI adoption thrives on purposeful action, strategic experimentation, and a relentless focus on people. The organizations that lead in this era will be those that move fast, measure impact, and keep humans at the center of every transformation.    #AIFirst #Leadership #DigitalTransformation #FutureOfWork