Shiva Raaj Kotini

Jeff Bezos always speaks LAST in meetings. Why? I learned this during my time at Amazon. Jeff realized that when he spoke first, even his most senior leaders started to hold back their ideas. They would think "hmm, maybe Jeff's right" and not share their perspective. It's one of the simplest but most brilliant principles that you know Jeff brought into the culture at Amazon when I was there, which was all about giving people a voice and making them feel heard. It's still my # 1 principle that great employee experience programs are built on. Too many organizations accidentally silence new ideas from junior or front line team members because they maintain a traditional hierarchy model at work. Ex: the more senior you are? The more valuable your ideas are. ✋🛑 Employee experience is built on 2 way conversations. Not a 1 way top down broadcast from the C suite. During a working session at a conference I attended recently, somebody asked the group I was facilitating, “How should we be thinking about employee experiences that actually scale across different locations, roles, and cultures? Should they be global and equal across the entire org regardless of location or specific to each region/area/ or even down to the department / team level?" My learnings building and launching a global Employee Experience at Amazon. If you were starting from zero, I'd focus on these 3X areas to start: 1️⃣ Start with a global framework, not global rules Kent Energy Services acquired SNC Lavalin's Oil & Gas business, they spent 12 weeks listening to all their employees before creating any programs. The team ran a listening campaign in which they heard from every single one of their employees, holding workshops and surveys. The result? Four core beliefs decided entirely by employees, not executives. 2️⃣ Appoint local employee experience champions Designate regional employee experience leaders who understand the cultural nuances. Their goal is to challenge you and help translate what engagement actually menas in THEIR market. Your "best practices" may not be relevant or engaging the same way globally. Trust your people on the ground to tell you. 3️⃣ Set unified global goals with localized execution Job satisfaction, retention, mobility, and inclusion metrics work globally. But how you drive them needs to be local. Your people closest to the work have the best solutions. That's why you need to empower regions to create their own initiatives that align with and ladder up to your global goals WHILE fitting their unique culture P.S. What would you add?

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Launching: Chat with Priyank (beta) today on https://priyank.tech A few weeks ago, I launched priyank.tech as a single home for my work, projects, and notes. Today I’m adding a faster way to get to the signal: Skip the scrolling, ask.  “Chat with Priyank” answers questions about my projects, views, and skills by pulling the most relevant context and giving you a concise, useful response. What’s under the hood (and why): - Human‑verified access - Conversations start only after the system knows it’s interacting with a person, not a bot. - Scope & guardrails - It only answers questions about me. When a query is out of scope or uncertain, it responds with a polite “I don’t know.” - Context engineering first (no RAG, yet!) - An analysis by industry veterans suggested that GPT-5 class models can correctly use ~85% of the provided context. I want to see how far careful, prompt, and context design can go before adding retrieval. I’ll evaluate this over the next few days and decide before graduating from beta. - Prompt caching enabled - To keep costs under control (and responses snappy), repeat prompts and shared context are cached. - Early memory layer - A lightweight short-term memory layer powers more personal, consistent answers across sessions. Try it on: https://priyank.tech   If it’s your first visit, do subscribe to my newsletter—I’ll share build notes, decisions, and lessons learned there. Starter prompts: “Tell me about your AI and Generative AI expertise “How can you help me become an AI first organization?” Feedback is welcome, especially edge cases and failure modes.  #AI #product #buildinpublic #LLM

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As product manager roles are transmutating from a pure manager type role to a PM engineer type role, here's the checklist for you - 1. Everyday play and experiment with different AI tools, internally make a list on which model to use for what purpose (this step is critical, as this compounds). 2. Never use AI for the initial draft when you are writing something. Writing brings you detailed clarity and in this AI age, clarity is king. Finish up your draft with AI, but start manually. 3. Always pay and use the frontier model. Don't cut corners here. The output of an Opus 4.6 is drastically different from sonnet when it comes to complex tasks. Having said that, develop a mental map on which model to use for what complexity of tasks. Otherwise you will just burn tokens for simple tasks. 4. When learning or understanding a concept, be deliberate and progressive when using the AI tools as well. For example, start with ChatGPT, take that output, understand it, add your own thoughts and then go to claude. Again point number 3, do apples to apples. If you are using the free tiers, treat all the free tiers as equals. 5. Learn to write agent native specs. Agent native specs are living docs which live in the repo, which the agent has direct access to. Take care of the smaller nuances. For example - If your team is using a context window of 100k models, then don't write specs which are too large. Eventually the agent will run out of context and start hallucinating. 6. When you have developed a strong intuition about points 1-5, then and only then build. The world is moving away from a PRD driven build environment to an agent spec driven build environment. Internalise that change. Change your workflow to adapt to it. 7. Build prototypes with the speed of thought, but remember these are prototypes and not actual production code. The prototype to production code journey is a bit far along. But that doesn't mean, you don't build, build your ideas quickly. Again the idea here is for you to develop enough intuition and judgement so that you can keep up with the agent. 8. As we are shifting from "vibe coding" to "agentic engineering", please remember this shift is going to be chaotic. Remember the world required 1-2 yrs to fully understand what actually is an "agent". So don't get overwhelmed, don't get any FOMO. Remember there's still a lot of time for the world to make this transition. All you can do is prepare better. Finally, anyone who is transitioning from a traditional PM to an AI native PM builder, remember, "chaos is a ladder". Be curious, be eager to learn, be adaptable, be flexible. Spend a lot of your time with the AI tools, stress test them, understand their limits, understand their context window and memory. This is in no way any exhaustive list (in fact I would argue this is a beginners list). But as you climb each stair in this ladder, you will develop enough intuition to move even further.

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I just read “Enterprise AI’s Reality Check” by Kapil Venkatachalam at FTV Capital, and it's a breath of fresh air amidst the AI hype. Kapil cuts right to it: amid billions in infrastructure bets and LLM growth, the real opportunities lie deeper—at the application layer, where AI directly powers business value. From personalized assistants adding real revenue at companies like Toast, Taco Bell, and Bank of America, to startups like Orbus Software, Windward, Arden Insurance, and Kore.ai, AI is starting to deliver beyond pilot illusions.. If you're focused on bridging the gap between AI experimentation and business outcomes, this is a must-read! #aiforreal #AIAdoption #AIAdoption #AIApplications #DataGovernance #AIInfrastructure #AIAtScale https://lnkd.in/ggzyBexW

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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 see a massive mistake right now. People evaluating India using a Silicon Valley lens. Same benchmarks. Same expectations. Same pattern recognition. It doesn’t work.. In India Distribution looks different. Consumer behavior moves faster. Trust is built differently. And the best founders? They’re not copying US companies. They’re building for how India actually operates. If you’re waiting for it to look familiar. You already missed the deal. 🇮🇳🚀

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