Product Management Insights

Your MVP: Your Most Vulnerable Prototype! Ah, the MVP. No, it’s not your company’s Most Valuable Player. It’s the Minimum Viable Product, that scrappy little thing you send out into the world, hoping it survives, learns, & evolves into something people want to use. Think of it as your product’s awkward middle school phase—braces, bad haircuts, & all. & if you’re too proud of it? Reid Hoffman says it best: “If you're not embarrassed by the first version of your #product, you've launched too late.” Let’s clarify: Your MVP isn’t meant to win beauty contests or sit on a pedestal. It’s not meant to be perfect. It’s meant to hit the ground running, trip a few times, & figure out how to get better. SU research shows that companies obsessed with over-polishing their #products before launch often face higher failure rates. Why? Because they waste time-solving problems customers don’t care about while missing the chance to solve the ones they do. Imagine your MVP as a bicycle made of duct tape & hope. It might wobble, but it moves forward. Here’s what it doesn’t need to be: • A luxury car: No bells, no whistles, & no heated seats. • A spaceship: It’s not launching to Mars. It’s just crossing the street. Instead, it should do one thing well—not five things poorly, not ten things “meh.” Just one thing that solves a real problem for real people. Think about it: Instagram started as a check-in app called Burbn. Twitter was a podcast platform. Airbnb’s early website photos looked like a Craigslist ad gone wrong. But they all launched early, iterated fast, & learned from real users. That’s the point of an MVP—it’s not a finished product; it’s a feedback machine. According to a CB Insights study, 42% of startups fail because they don’t meet market needs. Launching an MVP helps you avoid this fate by putting your product in front of users who will tell you—sometimes brutally—what works & what doesn’t. 1. Focus on function: If your MVP is a chair, it must hold someone’s weight. Don’t worry about mahogany finishes or gold-plated legs. 2. Start ugly. Your first version will likely look like a potato with buttons. That’s okay; potatoes are versatile. 3. Gather feedback fast: Get your product to users who aren’t your mom. They’ll tell you what’s broken, confusing, or just plain bad. 4. Iterate ruthlessly: Treat feedback as gold, not glitter. Use it to improve, evolve, & adapt. Your MVP is a test, not a trophy. It’s the baby bird you push out of the nest to see if it can fly—or at least glide without face-planting. The goal isn’t to impress; it’s to learn, adapt, & grow. So, embrace the awkwardness, the embarrassment, & even the failures. They’re not just part of the process—they are the process. When someone asks why your product looks like it was made in someone’s garage, you can smile & say, “Because it was. But wait—this is only the beginning.” Now, go launch that potato with buttons! #startups #leanstartups #entrepreneurship

Too many product decisions still happen in silos. Strategy gets separated from delivery. Roadmaps drift away from outcomes. Backlogs turn into long wish lists. As a result, teams stay busy but create little value. While that's always been an issue, it is now more important than ever with AI. Without clear strategic directions, teams are at risk of building products that nobody wants or needs, that have the wrong features, and offer the wrong UX—at an ever-faster rate. Great products, however, aren’t built by separating strategy from execution. They’re created by connecting them. That’s exactly why I developed my product strategy model—a powerful way to link product vision, strategy, roadmap, and backlog. In my article, I describe the framework in its latest, revised version, and I explain how you can systematically connect four critical elements: → Product Vision ⭐️ → Product Strategy ♟️ → Product Roadmap 🎯 → Product Backlog 📦 Additionally, I discuss who should own the elements, how the product strategy relates to portfolio strategy and business strategy, and how you can apply the framework: ✅ Strategy means making deliberate choices—including what NOT to build. ✅ Outcome-based roadmaps create far more clarity than feature-based plans. ✅ Product teams work best when they own both strategy and execution. ✅ Strategy and execution must be closely aligned: strategy guides execution, and execution informs strategy. ✅ The best strategy is useless if it doesn’t shape day-to-day product decisions. I hope you'll find the article helpful. Let me know your thoughts and questions in the comments. #productmanagement #ProductStrategy #productvision #ProductRoadmap #productteam

Ever felt like your product team is doing everything right yet not making the impact you expected? 🤔 After working with countless teams across industries, I've noticed a recurring issue. Teams excel at the fundamentals like customer discovery and experimentation, but struggle to connect their efforts to meaningful outcomes. This disconnect is often due to what I call the "missing middle." The "missing middle" is the gap between a company's high-level business vision and the day-to-day execution of product teams. Even skilled product managers using the best practices hit a roadblock when strategy isn't clearly articulated or aligned across the organization. Here are 5 warning signs that your organization may have a strategy gap: 1. Teams can’t connect their work to company goals. If asking how current projects align with business objectives gets you blank stares, there's a disconnect. 2. Decisions based on opinion, not evidence. Phrases like "I think users would prefer..." pop up without supporting data, leading to decisions made in a strategic vacuum. 3. Building without validating opportunities. Rushing into solutions without first assessing their value is a telltale sign of missing strategic guidance. 4. Departments with different objectives. If marketing, sales, and product teams are all heading in different directions, alignment is lacking. 5. Constant changes from executives. Frequent shifts in direction suggest that strategy isn’t clear or compelling, causing executives to react rather than lead. I recently worked with a financial services company that faced this exact challenge. Their product teams were skilled, yet business metrics stagnated. By helping them establish a strategic framework, we connected customer problems to business outcomes. The result? A 3x increase in business impact within two quarters. The key wasn't changing how they built products, but rather focusing on which opportunities to tackle and why. If your company faces similar challenges, you're not alone. Understanding and crafting a robust product strategy is crucial, yet many leaders lack the support needed to master this skill. To address this, I'm excited to introduce our "Mastering Product Strategy" course at Product Institute. This program will equip you to bridge the "missing middle" by developing strategies rooted in customer insights and business realities. You'll learn to communicate effectively, create feedback loops, and guide product decisions toward achieving real outcomes. Ready to transform your approach to product strategy? Join us and start achieving the impact you envision for your organization!

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

Most "product strategies" die because they try to be everything to everyone. I’ve seen a lot of such strategy docs and decks. But they are often just prioritized roadmaps or broad themes that sound good. Real strategy is hard. But also much more valuable. Here’s what I’ve learned it actually takes to develop a great product strategy: ➡️ A deep understanding of the landscape Industry shifts. Market dynamics. Tech trends. Customer context. Product realities. Not just what users say but the actual Jobs to Be Done, desired outcomes, and unmet needs. Where are they stuck? What’s changing around them? What’s at stake? ➡️ A clear point of view What do we believe about this space that others don’t? What’s the differentiated bet we’re making? ➡️ Hard decisions If there aren’t tradeoffs, it’s not a strategy — it’s a wish list. A good strategy helps you say “no,” even when it’s hard. Especially then. ➡️ Alignment to company strategy A product strategy can’t live in a vacuum. It has to be anchored in — and help accelerate — your company’s broader strategy. Otherwise, you risk building a great product that wins the wrong game. ➡️ Outcomes that connect the dots There should be a visible line from customer value → business impact → the bets you’re making. People need to see how their work ladders up or it won’t stick. And here’s the part most people skip: You have to test and evolve your strategy. The best strategies aren’t static. They’re living systems. You put the strategy into motion, watch what happens, and adapt. Just like product, strategy needs feedback loops. Inspired by "Smart Business", I try to ask: -Are we learning from how the strategy is performing in the market? -What feedback are customers and teams giving us — directly or indirectly? -Where are we seeing traction? Where is reality pushing back? -Are decisions getting easier — or harder? I’ve spent years learning how to craft product strategy that’s clear, actionable, and durable. It’s both an art and a science and it gets sharper every time you put it to work. It’s a skill you build — through practice, reflection, and iteration. Next, I will write about where, who from and how I learned product strategy #productstrategy #productleadership #platformthinking #enterpriseproducts

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.

𝗧𝗵𝗲 𝗦𝗵𝗮𝗽𝗲 𝗼𝗳 𝗠𝗩𝗣 𝗛𝗮𝘀 𝗖𝗵𝗮𝗻𝗴𝗲𝗱: 𝗪𝗵𝘆 “𝗕𝗮𝘀𝗶𝗰” 𝗗𝗼𝗲𝘀𝗻’𝘁 𝗖𝘂𝘁 𝗜𝘁 𝗔𝗻𝘆𝗺𝗼𝗿𝗲 Today, launching a 2-pager app or a basic prototype and calling it an MVP no longer impresses users, investors, or the market. The battleground has evolved, and so must your approach. 𝗪𝗵𝗮𝘁’𝘀 𝗖𝗵𝗮𝗻𝗴𝗲𝗱? • Building is no longer hard or expensive • Speed of ideation and iteration has exploded • User expectations are higher than ever 𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝗠𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝗙𝗼𝘂𝗻𝗱𝗲𝗿𝘀 𝗮𝗻𝗱 𝗕𝘂𝗶𝗹𝗱𝗲𝗿𝘀 • Your MVP must look like a real product not a sketchpad. • It needs to demonstrate clear value, not just potential. • Visual polish, intuitive flows, and real use cases aren’t “nice to haves” anymore they’re basic entry requirements. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗩𝗮𝗹𝘂𝗲 𝗼𝗳 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗠𝗩𝗣 𝗥𝗶𝗴𝗵𝘁 𝗧𝗼𝗱𝗮𝘆 • Faster Investor Interest: A well-shaped MVP that looks and feels market-ready builds confidence. It says, “We’re not just validating we’re ready to grow.” • Higher Conversion from Early Users: A polished MVP gets better feedback, more engaged users, and faster learning cycles shortening the path to product-market fit. • Competitive Differentiation: In a saturated landscape, standing out early with a well-executed MVP signals seriousness, craftsmanship, and intentcrucial traits that separate winning startups from the rest.

Soon your user's AI will negotiate with your product's AI. Most PMs haven't written that PRD yet: Here's what's coming: Your user asks their personal AI assistant to find the best savings rate. The assistant calls five banks' AI agents simultaneously. Compares offers. Negotiates terms. Reports back. The user never opened an app. Never visited a website. Never saw your UI. Sinch predicts this drives an explosion in conversation volume. For PMs, this changes everything: You're no longer designing for humans navigating screens. You're designing for AI agents making API calls on behalf of humans. Questions for your next PRD: → What's your product's AI-to-AI interface? → What information do you expose to other agents? → What actions can external agents take? → How do you build trust signals for machine-to-machine? This is the next platform shift. Mobile changed product management. Agent-to-agent communication will too. Sinch's full 2026 predictions: https://lnkd.in/g9fskkS7 Are you building for this future?

We keep hearing about ‘AI Product Managers.’ But is that actually a thing? We love adding labels to product managers—data PM, mobile PM, IoT PM… and now, AI PM. I’ve always believed these labels can help because they signal real specialization. For example, I’ve long advocated for the IoT Product Manager role. Not because PMs need to solder an Arduino or configure devices themselves, but because they need enough technical understanding to turn complex technology into business value. At its core, the PM job hasn’t changed: we're here to create products that deliver meaningful financial returns. But AI is different. 🤖 It’s not a niche technology like IoT or mobile. AI is becoming a layer in every product. And the PMs who will thrive in this era are the ones who understand AI well enough to imagine new solutions, shape good strategies, and guide their teams toward real outcomes. That doesn’t mean PMs should spend mornings vibe coding and afternoons building AI prototypes alone. It doesn’t mean replacing designers or relying on synthetic user personas as shortcuts. Those are tools, interesting, useful, but not the point. ➡️ The real shift is this: PMs need a working understanding of how AI actually works, what it can and can’t do, and how it changes user expectations. With conversational interfaces like ChatGPT, people now expect answers, not long workflows. They expect outcomes, not just information. That’s a massive change in how value is delivered, and PMs must be ready to design for it. So no, I don’t think “AI Product Manager” will last as a category. The role won’t split into AI vs. non-AI. Instead, every PM will need to level up. That includes getting more technical - enough to talk fluently with engineering, evaluate AI performance, deal with non-determinism, and make smart calls about value vs. cost. In short: Product management isn’t being replaced. But it is being reshaped. And the PMs who embrace this shift will lead the next decade of innovation. PS - I’m curious, which AI skills are making the biggest difference for your team?

𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘀𝗵𝗼𝘂𝗹𝗱𝗻’𝘁 𝗯𝗲 𝗮 𝗴𝘂𝗲𝘀𝘀𝗶𝗻𝗴 𝗴𝗮𝗺𝗲. 𝗦𝗼 𝘄𝗵𝘆 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗳𝗲𝗲𝗹 𝗹𝗶𝗸𝗲 𝗼𝗻𝗲 𝗶𝗻 𝘀𝗼 𝗺𝗮𝗻𝘆 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀? Some product leaders build it in isolation and hand it down like a mandate. Others leave it entirely to bottom-up input with no north star. Both approaches fail. One loses touch with reality. The other turns into a wishlist with no direction. So - what actually works? The best product strategies I’ve seen are 𝗰𝗼-𝗰𝗿𝗲𝗮𝘁𝗲𝗱 - but anchored in a clear company direction. And I don’t mean a vague mission statement. I mean actual clarity on: • What problem are we solving? • For whom? • What kind of company are we building? • And how fast do we need to grow to win? Once that’s clear, product strategy becomes a 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻 𝗹𝗮𝘆𝗲𝗿. It helps answer: → What do we build? → What do we prioritize? → What do we say no to? Great strategy doesn’t just define features. It makes 𝗯𝗲𝘁𝘀. It sets 𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻. And it 𝗳𝗿𝗮𝗺𝗲𝘀 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. It’s built on: • Customer insight • Market understanding • Internal constraints • And most importantly - 𝘁𝗿𝗮𝗱𝗲𝗼𝗳𝗳𝘀 It’s also shaped by context: In 𝗕𝟮𝗕, it’s about ROI and enabling sales. In 𝗕𝟮𝗖, it’s iteration and behavior change. In 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀, it’s extensibility and governance. And in 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀, it’s about balancing ecosystems of customers, partners, and devs. And it evolves: • Early stage → Find the wedge • Growth stage → Scale across segments • Enterprise → Defend and optimize The best strategies? They’re clear enough to empower teams to decide without constant top-down guidance. So if you’re leading product: Don’t start by asking, “What should we build?” Start with: “What does winning look like - and how does product get us there?” Then invite your teams to build 𝘸𝘪𝘵𝘩𝘪𝘯 that vision - not outside it. 👇 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘁𝗼𝗱𝗮𝘆?