Amit Kumar Misra

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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"Forget dreaming about being a product manager" A mentee with a finance background came to me today, feeling frustrated. They'd been told their experience wasn't a fit for Product Management. This is BS. Register here to know why- https://lu.ma/mbx3m2hm In my career, I've seen firsthand that a finance background is a superpower for a PM. A great product leader understands the business as deeply as the product itself. I shared how their skills don't just transfer; they transform. Here’s the breakdown I gave them on how 10 finance skills are your PM gold: 1. Financial Forecasting: This becomes Product Roadmap Planning, where you forecast future needs and allocate resources to build the right features at the right time. 2. Risk Management: Your ability to mitigate financial risk translates directly to Prioritization, where you weigh the risks and opportunities of every feature. 3. Data Analysis: You live in spreadsheets. A PM uses that same skill to analyze User and Market Data to make informed product decisions. 4. Stakeholder Management: Managing expectations from investors is your norm. That's the core of Cross-Functional collaboration, where you align engineering, design, and business teams. 5. Strategic Planning: You create long-term financial strategies. This is the very foundation of Product Strategy, defining the vision and how it will achieve business goals. 6. P&L Management: You understand the levers that drive profitability. This becomes Monetization & Pricing, where you own your product's P&L. 7. Compelling Communication: You present complex data simply. As a PM, you use this skill to Pitch a Product Vision and persuade teams to buy in. 8. Attention to Detail: Meticulous work prevents financial errors. This applies directly to Defining Product Requirements, ensuring nothing is missed. 9. Competitive Analysis: You research other companies' financials. Now, you’ll apply that same sharp lens to Competitive Product Analysis, understanding what competitors are building and why. 10. Process Optimization: You find efficiencies in financial workflows. As a PM, you apply this to Optimizing the User Journey to reduce friction and improve engagement. Your background isn't a barrier—it's your secret weapon. Your Pivot Starts in 3 Days Don't let anyone tell you your skills don't matter. They do. I’m sharing all the tactical steps to make this pivot in my upcoming PM onboarding masterclass. Only 3 days to go! 🗓️ Date: July 19th 🔗 Register here: https://lu.ma/mbx3m2hm

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Had a good time speaking at the 10th Edition of Product Management Summit in Bangalore a few days back. My session for seasoned Product leaders covered how to measure success of any product, creating and analysing segmented retention curves, how to nurture a set of engaged (and loyal) power users, and how closely episodic NPS is correlated with customer retention for high repeat businesses. Also spoke about the link between product strategy, OKRs, and KPIs.

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The ₹100 Cr/month Problem No One Talks About in Product Teams.. There is a systemic, multi-crore drag on productivity hiding in plain sight within India's tech companies. It's a problem that revolves around a critical role: the data-driven Product Manager. The scale of this issue, and its financial impact, is far larger than most assume. How many Product Managers in India work with product usage data? You’d think it’s niche. It’s not. Let’s break it down: - Total PMs in India: Approximately 275,000 (Source: Industry reports from NASSCOM, Naukri, LinkedIn). - Mid-Senior Cohort (₹25–40LPA): An estimated 30-40% fall into this band, resulting in 82,500 to 110,000 PMs. - Working with Product Data: Say. 50-55% of these PMs work directly with product usage data. This narrows the pool to ~41,000 - 60,500. - Actively Driving Insights: Of those, roughly 50% are not just consuming reports, but are tasked with leading analysis and driving insights from raw data. This calculation places 20,000 to 30,000 Product Managers in India at the epicenter of a significant operational bottleneck. Now here’s the kicker!! Let's see time wasted buckets- On average, a data PM loses ~20-30 hours per month dealing with: - Excel + Manual Analysis Tasks: Cleaning exported CSVs,, pivoting/graphing repeatedly, version control issues, rebuilding old reports from scratch, - Technical Hurdles / Tooling Friction: Creating dashboards in tools, realigning them for newer hypotheses, learning macros/python or other syntax, - Coordination Time to Get Insights: Chasing data analysts for custom queries, re-explaining problem statements, follow-up meetings This translates to a national loss of 400,000 to 900,000 PM hours every single month!!! At an average hourly cost of ₹1,250 for a PM earning ₹30LPA, the monetary cost is alarming. India's tech ecosystem is losing between ₹50 Crore and ₹112.5 Crore every month in PM salaries spent waiting for insights, not acting on them... And we’re not even talking about the damage to the product momentum and strategic agility: decisions delayed, launches slowed, strategy blurred. If you're living this; I already feel your pain. #productmanagement #AIforPMs #insightops #buildfaster #dataproducts #SaaSIndia #nocodeanalytics #productthinking #productgrowth

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Zomato's AI-driven personalization engine processes over 2.1 billion data points daily to optimize menu positioning and pricing psychology (Zomato Annual Report, 2024).

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🚀 The Science behind Product Management 🚀 🧩 How to Start Optimizing Front-End User Interactions in B2B & B2C When companies ask me to improve revenue and profit, the conversation often jumps straight to ARR, MAU, or retention metrics. But here’s the thing: those are lagging indicators. If you want to move those numbers, you first need to understand and optimize the leading variables that directly shape user interactions. Here’s a simple guideline I use when starting with a new client — a set of checks that highlight the primary levers behind revenue and profit: 🔹 Strategic Goals Categorize goals across the full user journey. The final goal may be an order, but what comes before matters: in B2B it might be a company info form, in B2C a sign-up. 🔹 Traffic (B2C) | Leads (B2B) How much exposure is the company really getting? Track raw traffic/session numbers (B2C) or leads (B2B), broken down by channel. 🔹 Product/Service Availability For what % of sessions was the product actually available? If the shelves are empty, nothing else matters. 🔹 Price Distribution Are you behaving like a high-end boutique or a volume retailer? Understand sales patterns across price ranges. 🔹 Checkout Flow Conversion Map every step: a) Add to cart b) Checkout page lands c) Pay button pressed d) Successful payment (first attempt) e) Successful payment (2+ attempts) 👉 95% of companies I’ve worked with don’t have this fully mapped. 🔹 Average Order Value Your health check. Is the product-market fit drifting over time? 🔹 CRM Comms & Sessions How much incremental business does CRM drive? Compare cohorts who receive CRM touches vs. those who don’t. 🔹 Active Users What % of users buy again within 3/6/12 months? Are repeat purchases frequent or annual? 🔹 Visit Flow Patterns User behavior differs wildly by channel (e.g. affiliate traffic vs. organic vs. ChatGPT-driven). Map it. 🔹 Visit Value What % of visits don’t bounce and include at least one action? Optimizing this has massive downstream impact. 🔹 Platform Speed & Accuracy No brainer but overlooked: 99.99% uptime, lightning-fast, and (almost) error-free. 💡 The big picture: if you want to improve ARR, MAU, or any lagging KPI, start by diagnosing and improving these front-end interaction drivers. They’re where the leverage is. Curious — when you’ve worked on revenue optimization, which of these have you found to be the most impactful?

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One of the most underrated skills of a Product Manager is effective communication. And this extends far beyond stakeholder updates - it applies to everyone we work with 🗣️ This becomes even more critical in domains like payments. With rapid technical advancements, most large players today offer broadly comparable performance. Differentiation comes down to the revenue impact, fees, commissions & other charges; and these decisions are usually made by business stakeholders at the merchant’s (or client's) end, not by their engineering or product teams. No matter how passionate we are about our platform, a business leader is unlikely to engage with technical jargon. They understand: “Your business saved ₹2L this month because transactions were routed optimally” better than: “Our platform dynamically orchestrated traffic with 99.99% uptime.” Know your audience. Speak their language.

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Every PM and AI practitioner should read Aakash Gupta’s deep dive on Claude Code. (link in comments section). It perfectly captures where AI workflows are headed — away from chat, and towards context-driven, system-level execution. As a Product Manager working across multiple domains — from product strategy and prototyping to manufacturing analytics — I’ve been experimenting with similar workflows using Anthropic - Claude Code, Google - Gemini, NotebookLM, OpenAI- Codex, and other CLI tools. Here’s what my current setup looks like — and what I’m adding next. 🧠 𝗪𝗛𝗔𝗧 𝗜’𝗠 𝗔𝗟𝗥𝗘𝗔𝗗𝗬 𝗗𝗢𝗜𝗡𝗚: 1️⃣ Insights from Documents: -I use Gemini CLI to analyze PDFs, research papers, and even books — tagging insights with source references. -This helps me synthesize market research, competitive intelligence, and industry trends faster and with structured clarity. 2️⃣ Creating & Updating Product Documents: -Generate market research and build “living CSV datasets” that update with new insights -Using NotebookLM/OpenWhisper/superwhisper, I extract meeting summaries and transcripts from audio files. -These updates automatically flow into living product documents managed via Claude Code or Gemini CLI, ensuring my PRDs and strategy docs are always up to date with the latest stakeholder insights. 3️⃣ Prototyping with AI: -I leverage Claude or Gemini CLI within Microsoft VS Code to build quick prototypes. -Each prototype folder maintains its own context files, design notes, and alignment documents — bridging intent to implementation seamlessly. 4️⃣ Operational Intelligence Beyond Product I use AI-driven CLIs for my Manufacturing & Personal use: -Analyze sales, expenditure and manufacturing trends -Study spring design data and process challenges -Generate production inspection sheets -Summarize daily news articles stored in a dedicated “News Folder” -Structure my notes and reflections from varied fields into a cohesive, searchable personal knowledge system. Each of these tasks compounds context — building a continuously smarter workspace. ⚙️ 𝗪𝗛𝗔𝗧’𝗦 𝗡𝗘𝗫𝗧 𝗜𝗡 𝗠𝗬 𝗕𝗨𝗖𝗞𝗘𝗧 𝗟𝗜𝗦𝗧 Inspired by the Claude Code framework & beyond - here’s what I’m now implementing next to level up my system. 1️⃣ Reusable Commands 2️⃣ Parallel & Specialized Agents 3️⃣ MCP server Integrations ⚖️ 𝗪𝗛𝗬 𝗧𝗛𝗜𝗦 𝗠𝗔𝗧𝗧𝗘𝗥𝗦 Yes, privacy is a cost. But empowerment is the outcome. AI has become my second brain — not replacing my thought process, but extending it. And as I continue refining this workflow, the compounding effect is clear: Every new process adds context, every insight strengthens decisions, and every iteration improves the next. #ProductManagement #ClaudeCode #GeminiAI #NotebookLM #AIWorkflow #ProductGrowth #AITools #AIForPMs #Prototyping #AIExecution #AIinManufacturing #CLITools #ContextBasedAI #AIProductivity #FutureOfWork Techpix

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The role of product managers is evolving fast. I sat down with Saurabh Agarwal (VP Product Management - JioHotstar, ex-Google and LinkedIn) to decode what’s changing and what will not in the field of product management in the next few years. It was exciting to interact about his decade plus learnings on building products for 500 million+ Indians that most of us use daily along with uncovering how they came up with the ideation of the much revered Max-view feature in Hot-star, that went from idea to launch just in 6 weeks, as first ever vertical content consumption for live sports globally! Not just that, we covered the following elements in the conversation ✅ Engineer → PM → org-builder: how the scope and stakes grow ✅ Judgment vs data: when to trust instincts over dashboards ✅ The MaxView story: from insight → prototype → launch in ~6 weeks ✅ Product trade-offs at true billion+ internet scale ✅ Designing for India’s Next Half Billion (NHB) users ✅ Where AI is actually reshaping PM & design ✅ Why simplicity beats cleverness in execution He also dropped a bunch of timeless nuggets on velocity, taste, and building for constraints. Thanks to Rati and team at Product x People for the invite. Link to the full conversation in comments. #productmanagement

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10 posts to understand Product metrics [1] Product metrics - 4 questions to ask before solving https://lnkd.in/dQQGAeN3 [2] What is North Star Metric? And, how to identify NSM? https://lnkd.in/djj3msnQ [3] Uber's north star metric https://lnkd.in/diNwDaUd [4] Spotify's north star metric https://lnkd.in/di24HYt4 [5] Airbnb's north star metric https://lnkd.in/dvyVAvgg [6] 10 signup and Onboarding related metrics https://lnkd.in/di8_fgfP [7] 10 engagement and retention related metrics https://lnkd.in/dsbgeB_J [8] Top Revenue and Monetization related metrics https://lnkd.in/damSfjwU [9] Net Promoter Score https://lnkd.in/dezD-CZQ [10] Top Customer Satisfaction related metrics https://lnkd.in/djupfdur Liked this? Follow Vishal Bagla for more. #ProductManagement #ProductManagers #InterviewPrep #ProductAnalytics #Metrics

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We’ve largely solved payments infrastructure in India. Ecommerce execution, however, still feels fragmented and unreliable in comparison. I’ve spent years watching infrastructure succeed where product innovation alone couldn’t. The pattern is consistent: fragmentation creates friction, standardisation creates scale. India’s ecommerce today feels a lot like digital payments did before UPI. A friend recently launched a brand and asked me, “Which payment gateway should I use?” That question made something clear. Payments aren’t the real problem. They’re just one step. Commerce breaks in everything that comes after. Selling direct today means juggling storefronts, logistics dashboards, payment systems, courier tools, tracking links, returns workflows, and manual customer communication. Seven dashboards. Constant re-entry. Systems that don’t talk. Each tool works on its own. The handoffs are what break. Demand exists. Execution collapses at scale. Marketplaces bundle everything but take margins and ownership. Direct commerce offers freedom, but turns founders into operations managers instead of brand builders. This is the exact problem UPI solved: fragmented systems, manual reconciliation, unpredictable outcomes. That’s when I started seeing Shiprocket as an infrastructure layer for ecommerce execution, similar in spirit to what UPI became for payments. The problem isn’t fully solved, but the shift from fragmented tools to shared execution rails has clearly begun. A single system where core ecommerce workflows connect automatically, without merchants stitching tools together. Integration happens behind the scenes, and intelligence increasingly guides decisions that were earlier driven by guesswork Over 4 lakh merchants now operate this way, selling directly without operational chaos. This is what infrastructure does. It makes the hard parts invisible so founders can focus on building brands, not managing dashboards. India didn’t scale payments by building more apps. It scaled by standardising execution. Ecommerce is finally starting to move in the same direction.

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You may like it or not, but politics are a part of the Product Management world. How can data-driven PMs drive their vision despite that? Let me tell you, initially in my career, I was almost allergic to politics. All I wanted to do was do my job to the best of my abilities and not care for other people's ambitions and egos. It took a little time until I fully understood that in most companies, there is simply no escaping this. If you want to be kept on the payroll and improve your product, you will have to be politically engaged on some level. So, how can we ensure our data-driven vision and discovery efforts are not trumped by someone else's personal goals? 𝟭) 𝗕𝘂𝗶𝗹𝗱 𝗦𝘁𝗿𝗼𝗻𝗴 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 Start with any PM should do, politics or otherwise. Be a charming, transparent, reliable, inspiring, and empathic leader and colleague. When people know and trust you, they're more likely to support your ideas and defend you from others' egoism. 𝟮) 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝗖𝗹𝗲𝗮𝗿𝗹𝘆 Present your data and insights in a way that resonates with different audiences. Also, aim to say the right stuff, supported by data, to the right crowd at the right time via the right medium. In that case, lots of "rights" make an even greater right! Sidenote: Less is more in terms of clear comms :) Also, always focus on demonstrating tangible value for the clients. That can help overcome political obstacles. 𝟯) 𝗔𝗹𝗶𝗴𝗻 𝘄𝗶𝘁𝗵 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗚𝗼𝗮𝗹𝘀 You may be sick and tired of politics, but at the same time have some pet projects you'd like to push despite weak data backing or poor alignment with current goals. Police yourself in this regard, and you will have fewer angles of potential political attack open for your "opposition". 𝟰) 𝗞𝗻𝗼𝘄 𝘄𝗵𝗲𝗻 𝘁𝗼 𝗹𝗲𝘁 𝗴𝗼 Sometimes, the best investment would be to simply do what you are asked. As long as it is sporadic and can win you allies long-term, it's a strategic move. Just keep the paper trail in case the request you took upon yourself backfires. 𝟱) 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗔𝗹𝗹𝗶𝗲𝘀 Once you build a set of allies and authority, use it to have a stronger voice in the room. In other words: Use politics to build the best product possible. Career growth and recognition will follow organically, and you don't need to change your career into a Product. 𝟲) 𝗗𝗼𝗻'𝘁 𝗹𝗲𝘁 𝗶𝘁 𝗴𝗲𝘁 𝘁𝗼 𝘆𝗼𝘂 You may execute the previous 5 points flawlessly and still feel like everything is playing to office politics. Just stay strong, don't let it get to you, and be a beacon of reason and sound decisions. Hopefully, you will lead by example! So, do you have to struggle with politics at work? Sound off in the comments! #productmanagement #productmanager #officepolitics P.S. To become a Product Manager who can navigate politics while staying data-driven, be sure to check out my courses on www.drbartpm.com :)

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PRD templates for product managers. Bookmark these A Product Requirements Document (PRD) is one of the most essential documents in product work. It sets out the plans for a team that is ready to move from ideation to implementation of a feature or product. There are many different approaches to creating a PRD. They mainly differ in the level of detail and length of the document. There is no universal solution, but you can choose a template from our collection that intuitively seems most suitable depending on the complexity of the project, the number of participants and the specifics of your company’s processes. https://lnkd.in/e9PeqsPF

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We (HelloPM) are soon releasing another interesting conversation on the best product cultures with Anuj Rathi sharing learnings from his times at Swiggy . The full episode will be out soon. Here’s a sneak peak. Ankit Shukla Bhavishya Bharadwaj #productmanagement #culture #leadership

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Honored to see Practical AI for Business Leaders, Product Managers, and Entrepreneurs—co-authored with Alfred Essa —featured among these incredible reads for product managers navigating AI and emerging tech in 2025. 📘 This list is a goldmine for anyone building in the age of GenAI. Big thanks to the creators of this roundup for curating such a thoughtful collection! 💡 Whether you’re an aspiring AI PM or a seasoned leader sharpening your edge, these 12 books offer practical wisdom, strategic insight, and real-world application. 🔗 Check out the full list in the original post below. 📚 And if you’ve read something game-changing that’s not on this list—drop it in the comments! #AI #ProductManagement #GenAI #EmergingTech #Leadership #PMBooks #AIProductManager

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99% of PMs think product metrics are easy. Most of them have memorised the ubiquitous AARRR (Acquisition, Activation, Revenue, Retention, Referral) framework or jargons such as north star metric and feel they are ready for the real world. 🤣 Here are some real world scenarios to think about 🤔 1. You launch a new feature on your e-commerce funnel let’s say a new rank ordering of your search results and you see material improvement in visit (session) level conversion but not much in visitor (user) level conversion. So did you truly deliver a win or not? 2. You launched a new look and feel (simplified UI) of the home page of the app. You delivered some significant improvement in reducing bounce on the home page but search results to successful order conversion reduced. So did you deliver a win or not? 3. You managed to bump up the average transaction value in your booking funnel by 10% without impacting funnel conversion but your D90 retention is showing some dip? So did you deliver a win or not? Oh none of the product influencers told you about such scenarios. 😂 Welcome to the real world. Most of these metrics are intertwined. 99% of the PMs share shortsighted metrics and claim them as wins w/o any thought and consideration to more deliberate analysis and second order effects. Don’t be a wannabe, peel the layers and understand whether you are actually delivering wins or just showboating. PS: Clueless? Need help in understanding all this? Follow me for high quality content https://lnkd.in/gHweK_q7 or https://lnkd.in/ggUKftFq Also grab my product assignments playbook, 12 real product cases, all successful submissions, real job offers, ~200 pages of content. **Grab it now** - https://lnkd.in/gHzyN-Xc #productmanagement #analytics #realworld

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There are so many AI Tool for Product Managers that can be used for increasing their day to day productivity. If you are a PM, what is your biggest pain point even after having so many AI ⚒️ . Is there a tool you desire for? Which one the below use case resonates: 🕹️Determine the right use case ♟️How to determine the AI first strategy for my portfolio 💹Market Research including competitive analysis 🧐Analysis of user feedback 🚦Prioritisation and Roadmap 📄writing PRD Please share your comments and thoughts . Also add if I have missed anything #aitools #productmanager

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