Euka AI: Direct Customer Feedback Drives Speed

Too many conversations around product management today (including this post) suffer from a tunnel view → “PM = engineer who can talk to users.” Yes, tech is important. Yes, user empathy matters. But product management is far more than engineering + customer conversations. What about: • Compliance & regulations • Marketing & positioning • Business & growth levers • Competitive analysis & market understanding A PM’s role is to be at the confluence of every department a product touches—not just engineering. I agree: knowing tech is a must-have skill. But let’s not assume a PM has to be an engineer. These are skills, not degrees. • Engineers can (and often do) learn business, growth, design, compliance, and customer empathy. • Similarly, non-engineers can (and do) learn coding, DBMS, DevOps—even without a CS degree. And in today’s era of AI + vibe coding tools (Cursor, Lovable, etc.), the barrier to learning tech has never been lower. If AI can help engineers step into business/product, why can’t it help PMs step into tech? Product management isn’t about being a mini-engineer—it’s about being the bridge across disciplines and ensuring the product thrives in the real world. If you’ve read this far, I’d love to hear your thoughts—agree, disagree, or challenge me in the comments.

Your competitors are testing features you'll never see coming. Last week, a product manager told me: "We spent 3 months building a feature, only to launch it the same week our biggest competitor announced the exact same thing." Here's what most product teams miss: While you're reading competitor blog posts, they're running SECRET A/B tests that could blindside your roadmap. The hidden reality:  → Netflix runs 1000+ simultaneous experiments → Airbnb tests features on just 5% of users for months → Your competitor's next big pivot is brewing in private beta What smart startups track:  ✅ Hidden UI variations (only certain users see them) ✅ New API endpoints (signal major features 3-6 months early) ✅ Job posting patterns (hiring ML engineers = algorithmic features coming) ✅ Code repository changes (the digital breadcrumbs) The game-changer: Instead of discovering competitor features 3 months AFTER they launch, discover them 3 months BEFORE. This isn't about copying. It's about strategic positioning in a market where product cycles are accelerating but intelligence gaps are widening. Question for product leaders: How much of your competitive strategy is based on what competitors WANT you to see vs. what they're actually building? #ProductStrategy #CompetitiveIntelligence #FeatureFlags #ProductManagement #StartupStrategy

We need to talk about the "jack-of-all-trades" Product Manager. That role is DEAD. The future belongs to the specialist. I'm speaking from experience. When I started at Grover, I owned three product areas. It was challenging but manageable. Fast forward four years, and my team and I own 26. Twenty. Six. It's impossible to go deep on any single one. My days are a blur of context switching, trying to keep up with everything at once. This isn't sustainable, and it's not how great products are built. The best products come from deep, focused expertise. They come from a PM who has the time to obsess over a single customer problem. With AI and new technologies automating our administrative tasks, we finally have that chance. The tools are here to free us from the "jack-of-all-trades" trap. The future of our craft isn't about managing more. It's about knowing more about what you manage. It's time to specialize.

Most product ideas fail. And that's *exactly* why you need experimentation. 🧪 Learn best practices from Ron Kohavi (ex-Microsoft, Amazon, Airbnb) on how to build a culture of experimentation that drives innovation: https://lnkd.in/eTT5Vruk

Working at Amazon, Twitch, and Pandora taught me a lot about a/b testing. These are my four favorite principles that shape how I drive experimentation-led growth: 1. You need a central testing infrastructure. At Pandora, dozens of teams were running experiments daily. Without a central system, we risked cross-contamination, false signals, and customer fatigue. Our data science org managed test, control, and holdout groups, with shared standards for design. The result was cleaner data, more confidence in outcomes, and a culture that taught everyone (myself included!) how to experiment the right way. 2. A good hypothesis is a bridge Too often, teams jump from strategy straight into tactics. A clear hypothesis (“IF we do X, THEN Y will happen”) turns inputs into something testable. It keeps execution tied to purpose, defines what success and failure look like, and ensures you’re measuring the right signals (not cherry-picking during the post-mortem). 3. Leaders must provide air cover. When my team was deep in experiments, peer-team managers sometimes asked "what is taking so long?". Checking boxes is tempting on the day to day. But which do you want to claim come performance review time? The long list of things you did? Or the impact you drove? Managers need to train their teams to have backbone and give them protection while they learn and iterate, because not every leader will get it. 4. It's not a thing you do. It's a way of being. Said another way, it's not just a task. It's a system. Experimentation shouldn't be an occasional project. It needs to be core to how you behave at work. It takes time to get comfortable and good at it, which means you need reps. Build it into your team process, surround it with mechanisms, and make it systematic. I'm sure there are so many more, so let me know what I'm missing! And enjoy this AI-generated photo of me with an even bigger nose than I already have (thanks, Gemini!) 🤣 #Testing #Experimentation #GrowthMarketing #ABTesting

What if your features are great… and nobody cares? Big tech loves to sell features, sleek design, smarter AI, and everything Faster. But in places like Vietnam, I have seen brilliant products flop. Not because the tech was bad, but because it did not fit the culture. Take Uber in Vietnam as an example. It had all the shiny features, but it never clicked. Uber pushed cashless payments at a time when most people didn’t trust them or barely knew how they worked. Grab did the opposite. From day one, it accepted cash. It respected local habits. Then it doubled down with things people actually needed like GrabBike, GrabExpress, and GrabFood. While Uber tried to copy-paste a global model, Grab became part of dayly life. I’ve lived the same thing while building The Ring. We had to go beyond specs, reworking our software so it could fit any building and be truly uncloneable. That change did not come from whiteboards or roadmaps. It came from listening, talking, understanding what buildings and residents really needed. Because trust is not automatic. People want to know who’s behind the product, how it fits into their life, and whether it respects their habits. If it doesn’t, it won’t matter how advanced or shiny it is. So, still think features are enough?

Paridhi UPADHYAY is reimagining API product management through an AI-powered lens at apidays India 2025. As a Senior Product Manager at PayPal, she'll share how AI tools are transforming every stage of API development - from design and spec generation to developer feedback analysis. Discover practical strategies for leveraging AI to generate SDKs, automate testing, and uncover developer pain points with unprecedented precision. This isn't just incremental improvement; it's a fundamental shift in how we conceive and deliver API products. Want to peek into the future of API product management? Don't miss Paridhi's insights at #apidaysIndia https://lnkd.in/gZFEn_vv

✨ From Passenger to Learner: My Uber Experience ✨ Everyday experiences often teach us the most powerful lessons—and my recent time with Uber was exactly that. 💡 Here’s what stood out to me: Efficiency & Simplicity: In just a few taps, a complex logistics problem—matching riders and drivers across a massive city—is solved seamlessly. Data in Action: Surge pricing, real-time ETA, route optimization… it’s all about data science, analytics, and AI at scale. Customer-First Design: Every detail—from app navigation to payment flexibility—is built with the user in mind. Global Impact, Local Relevance: Whether it’s Toronto, Mumbai, or Sydney, Uber adapts to local culture, rules, and customer needs. As a Data & Business Analyst, I couldn’t help but think: the same principles apply in our work too— 📊 Simplify processes, ⚡ Make decisions data-driven, 🌍 And always keep the end user in focus. 🚖 Uber wasn’t just a ride for me; it was a reminder of how technology transforms lives, industries, and experiences.

As part of my Product Management journey at NextLeap, I worked on creating a Product Requirements Document (PRD) for a new tipping feature for Blinzedu. The goal? To design a seamless way for users to support delivery partners, while also driving adoption, retention, and partner trust. 💡 What this PRD highlights: Competitive Benchmarking → Studied Swiggy, Zepto & Blinkit to identify gaps & opportunities. User-Centric Design → Focused on lowering barriers to tipping (₹1 entry point), social proof nudges, and transparency. Metrics That Matter → Defined success in terms of adoption, average tip value, and partner earnings impact. Roadmap & Prioritization → Balanced speed-to-market (P0 features) with adoption boosters and future enhancements (like recurring tips). 📌 Why I’m sharing this: Writing PRDs isn’t just about documenting features. It’s about clarity in thinking, structured communication, and aligning engineers, designers, and stakeholders toward a common goal. This assignment gave me a chance to practice those skills hands-on — and I’d love to hear your feedback! #ProductManagement #PRD #ProductDesign #UserExperience #NextLeap

Focusing on evolving money movement #strategy, #SuperApps across Southeast Asia and Brazil have a lot to teach us about the value they create for customers, employees, and gig workers. As I researched meaningful #innovation, I uncovered how Grab has been punching above its weight for years — to the point where even Uber sold its Southeast Asia business to Grab in return for equity. My own takeaway is to share - Innovation is not just about “tracking emerging technology” & score brownie points with PoCs. It’s about “adding deeper value to products, consumers, and employees,” “understanding unit economics,” and “investing with local realities in mind.” From the research, what also stood out most is the concept of Jugaad (Frugal Innovation) — something our CEO, Jeremy Wilmot at Interac Corp. has been emphasizing with us for years (post link in comments). That #mindset inspired me to share these learnings: 👉 Jugaad at Scale (Frugal Innovation): How Grab Mapped the Unmappable to Thrive, Make Roads Safer, and Unlock New Revenue → From $200 cameras on scooters to AI-powered maps used by global tech giants, Grab’s frugal innovation shows how local constraints can spark global breakthroughs. What you will learn: 🚀 Introduction: Jugaad at Scale 🗺️ The Grab Story: “Mapping the Unmappable” GrabMaps 📸 Jugaad Move #1 & Move #2 💡 Innovative Results 🔑 What This Really Is: Jugaad in Action 🏛️ Broader Lessons for Builders (and Policymakers) 📖 Closing: The Jugaad Playbook Full article here 👉 - https://lnkd.in/gH6VZqUY