Snigdha Gupta

Today, I tuned into another session of The Product Folks #Mosaic AI Buildathon, which featured Madhu Gurumurthy (Head of Product, Gemini @ Google). He discussed how product teams are evolving and what skillsets product leaders need to adopt moving forward. Madhu started by outlining what great teams often share: 1. Technical PMs with strong design sense (they know what good design looks like) 2. Engineering leads with good product and design sense 3. Designers with product sense and understanding of technical possibilities 4. Product leaders who hold enough context to make quick decisions Having all four on one team is rare, but when it happens, the team operates as one unit. The more cross-functional awareness each role has, the better the team performs. The reality, he said, is that most teams still operate with hard-ish handoffs and try to compensate for this with standups, syncs, alignment meetings, documentation, and layers of leaders - an approach that is bloated, inefficient and slow. He believes AI will reshape this dynamic, giving rise to a new “builder” archetype - people with judgment across product, design, and engineering who can leverage AI to prototype and iterate quickly. Instead of a PRD-heavy process taking 4–6 weeks, builders can create a prototype in a single day. The team can then debate it, have the builder make refinements, and hand it off to engineering to begin the build phase. He pointed out that Spotify has already introduced prototyping into their PM interviews. Madhu sees the future as one where big companies run smaller, flatter teams shipping more products, and startups thrive with small teams and solo founders. Engineering will continue to be super important. Those with strong product/design sense will have an advantage because they can own things end-to-end. His advice: • Start building in your own time. • Manage your information diet (learn from the frontier, not from hype.) • Read papers and grasp the salient points to sharpen your intuition. • Study good AI products. Focus on what they do well, what prior constructs for that category they are challenging, and what product patterns they are inventing. • Learn to build evals. Madhu emphasized that what will remain constant is the hard work required to build high-quality, successful products (ie. having a vision, identifying what user and market problems it is solving for, and how.) Thanks to Madhu and the TPF team for another great session. Really enjoying the insightful conversations around these topics. #CSstudent #ProductManagement #AI #ProductBuilder #CareerAdvice #MosaicByTPF

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I constantly oscillate between sharing in-progress work and completed work. This means I'm long overdue to share some exciting updates on Wonderment Delivery Promise, our pre-purchase ETAs product. Wonderment now supports Rules for transit estimations and fulfillment estimations to allow you to account for per-product ETA customizations (like embroidery and engraving), warehouse delays, or other events that would impact coming estimates. Here's a quick demo of how you can use them for communicating pre-orders, backorders, and delays: https://lnkd.in/eb88vmcu

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I love Shreyas Doshi's message on how AI is reshaping product teams into small groups of "𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗺𝗮𝗸𝗲𝗿𝘀" who can think, build, and create without the heavy layers of coordination and reporting. Instead of separate PM, design, and engineering roles, the future leans toward hybrid builders who use AI to move faster and work more directly on the product itself. The core lesson for product leaders is clear: the role must evolve from managing processes and people to actively shaping products. Leaders need to get closer to the work, strengthen their ability to create clarity, and guide teams through insight and creativity rather than coordination. In a world where AI handles reporting and operational overhead, the product leaders who thrive will be the ones who stay hands-on, think deeply, and help their teams build with speed and intention. Check out this video to learn more. https://lnkd.in/dbCHX4-s

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Highly recommend “World-class Product Sense in Practice” by Shreyas Doshi What stood out for me is how practical the course is. It clearly breaks product management down into the skills that actually matter in the real world—not frameworks for the sake of frameworks. For me, it felt like I finally had permission to lean into my natural strengths, especially cognitive empathy, and use them deliberately in product decisions. It was also a good reminder that some concepts I learned in B-school—like differentiation—are still deeply relevant. At the same time, the course taught me fresh ways of thinking: a lens I can run every product decision through to quickly see what works. Link: https://lnkd.in/ghuqDkSH

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Just completed the Product Discovery Certification Cours course by Pendo.io (hashtag#MindtheProduct), Here are my key learning: * Product discovery is the crucial process of deeply understanding customer problems and validating solutions before development. It strategically mitigates key risks, ensuring features drive real customer value and positive business impact * Product managers face a unique challenge: They must design features that drive customer value and advance key business goals * Beware of Cognitive Biases: These systematic errors in thinking can misalign what you build with customer needs or business goals * Examples include: * Confirmation bias: Seeking feedback that confirms your existing views * Sunk cost fallacy: Continuing a project due to prior investment, even if it's failing * Authority bias: Giving unmerited weight to opinions of those in authority * Product Discovery is Crucial: It's the process of understanding customer problems and needs, then validating or rejecting solutions before development. This helps eliminate four big risks: value, usability, feasibility, and business viability * Effective Discovery Drives Business Impact: It leads to positive outcomes like customer retention, revenue growth, reduced costs, and risk mitigation * Embrace a Data-Driven Approach: Product discovery relies on four key types of data: Research, Customer Feedback, Product Analytics, and Testing * AI Optimizes Discovery: AI tools can accelerate the process, include more inputs, and make discovery more continuous * The 6-Part Product Discovery Framework guides the process: * Define the outcome * Understand users’ problems * Identify the problem to solve * Explore possible solutions * Test and validate solutions * Close the loop * The four collaborative tools and visualizations used in product discovery to better understand users, ideate solutions, and manage risks are: * Empathy Map * User Journey Map * Opportunity Solution Tree * Assumption Mapping #productdiscovery #AIinProductDiscovery #continuouslearning

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Harsh but the truth. Domain knowledge may help you win in the short term, true product skills help you win in the long-term.

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