Gurvir S Kanwar

AI Success Starts with Data Quality and Governance A recent TechCrunch article reinforces something many of us driving digital transformation in large organizations have learned firsthand: better-quality data, not just more data, is the real driver of successful AI initiatives (https://lnkd.in/eHR9kcXH). Companies like iMerit are showing that domain-specific, expert-labeled data consistently outperforms massive, loosely curated datasets. But that precision doesn’t happen on its own. To build trusted, explainable, and scalable AI systems, enterprises must pair high-quality data with strong data governance practices: 🛡️ Ensure compliance and auditability 🔍 Maintain data lineage and integrity 🔄 Support continuous validation and improvement 🔗 Align data with business context and use case From my experience leading digital transformation, I’ve seen how data governance transforms AI from a prototype into a platform, one that can be measured, trusted, and scaled. As organizations race to adopt AI, success won’t be determined by model complexity, but by how well you manage the data that feeds it. #AI #DataQuality #DataGovernance #DigitalTransformation #EnterpriseAI #ResponsibleAI #TechLeadership

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Stargate seems to have it's second location, Foxconn's Ohio electric vehicle plant. Softbank led the sale in it's first big move in the AI project. SoftBank is looking at a number of sites to serve as a flagship for Stargate, weighing access to water, power and telecom infrastructure. SoftBank Chief Financial Officer, Yoshimitsu Goto, said they are having a slower than expected rollout trying to find locations that meet all involved parties' needs during an earnings call on Thursday. Stargate was unveiled back in January by President Trump with Softbank founder, Son, at his side. Since then, the project has stalled with risks stemming from US tariffs and cheaper AI alternatives, like Deepseek. The projected financials with these risks are making it hard to come up with the heavy ($100 billion) investment in AI infrastructure promised by Son. Goto stated SoftBank will begin concrete discussions about its first Stargate site soon. #ai #datacenter #future #technology #innovation #stargate

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30+ teams. One goal. None playing the same game. I recently stepped into a space where more than 30 teams contribute to the same outcome. On paper, we were “aligned.” In reality, each team was chasing a different metric. Then one purchase order caught our eyes. The delivery date was impossible. I pulled the thread with my team and partners and uncovered a breakdown that had been sitting under the surface for a long time, hidden in the noise of day-to-day operations. We are now working through the fix. The challenge is not about blame. It is about making the problem visible, making the first step easy to solve, and linking it to the next step so the system keeps flowing. I start small and go big. I am comfortable not knowing every detail upfront as long as I understand the big picture and build guardrails to keep us on course. You cannot fix a fragmented system overnight. But if you do not connect the pieces early, they will drift apart and take twice as long to bring back together. How do you fix a fragmented system : start small or try to solve it all at once?

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The Leadership Permit: The Hidden Cost of Delivery: https://lnkd.in/gjKC63UT Two kinds of delivery. Same metrics. Very different outcomes. Transactional: the project ships, the team returns to baseline. The leader is still the escalation point for every hard problem. Knowledge stays concentrated. Nothing transfers. Developmental: the project ships, and the team comes out of it more capable than when it started. The next project of similar complexity needs less senior involvement. The work built something that lasts. Most leaders who deliver transactionally aren't underperforming by conventional measures. They're often the most disciplined executors in the room. But over time they become the ceiling. Every hard decision routes through them because nobody around them has developed into independence. One question worth asking after every major delivery: What can this team now do that it couldn't do before? If the honest answer is "the same work, again" the delivery was transactional.

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The Org Chart is a lie. I have watched senior PMs walk into programs fully briefed on the reporting structure - titles, hierarchies, approval chains - and still get blindsided three months in. Because the person who killed their project had no official authority to do so on paper. Every organization has a Shadow Org Chart. It is the invisible network of influence that actually runs the company. The relationships nobody documented. The trust nobody announced. 🚩 If you are managing stakeholders based on titles alone, you are already behind. I spend my first 30 days on every program mapping three specific people. Not the org chart. These three. Swipe to see exactly who they are and how I find them. 👉 — — 𝐀𝐝𝐢𝐭𝐲𝐚 𝐑𝐚𝐧𝐢𝐧𝐠𝐚, 𝐏𝐠𝐌𝐏 | 𝐏𝐌𝐏  Senior Program Manager Delivering high-stakes programs at any scale. Follow for senior PM frameworks that protect budgets, timelines, and organizational goals.

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I recently realized that both people and AI respect a key leadership trait. The best leaders I know share that trait. They give crystal clear instructions. If I tell my team “make this better,” I will surely get confused looks and mediocre results. If I tell ChatGPT “make this better,” I get generic fluff. But when I say things like: - “I need a code review checklist tailored for React apps with security in mind. Here’s the current checklist we use.” - “Redesign this dashboard to reduce clutter and highlight metrics that changed significantly week-over-week — here’s our current one.” …both my team and AI deliver exactly what I need. Over the years, I’ve found that clarity isn’t just good leadership. It’s the only leadership that scales.

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Last month, Amazon launched Q Suite — an AI assistant. One of the features is that it can connect across multiple sources - email, docs, share point and calendars.  I tried it on one of my large cross-functional initiatives — with multiple teams, documents, and ongoing discussions. I simply asked: “Summarize all project updates, upcoming meetings, and key decisions from the last month.” And within seconds, it pulled everything together — - Read through all my emails on that topic and documents and gave a summary of what happened across these channels - The upcoming meetings on this topic from my calendar - A list of key decisions and owners from past discussions But here’s what I realized — The output quality depends on the input hygiene. So I made three changes: 1. Label emails correctly - in outlook you can add "categories" to label. 2. Folder discipline - one dedicated folder for each project. 3. Consistent naming - Put file names properly with the project or initiative name.

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The best satisfaction and growth path for a leader is when you mentor your team and equip them to move to the next level. If you fear your successor is going to replace you, it will only lead to harbouring insecurity, mismanagement and eventual downfall of you and your team.

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AI is reshaping the landscape, generating unprecedented wealth and opportunities. The surge in stock prices of tech giants like Nvidia, Meta, and Microsoft, coupled with the exponential growth of AI-related firms, is propelling individuals to new heights of financial success. The current AI wave is not just a trend; it's a seismic shift, dwarfing past tech revolutions in scale and impact. The evolution is palpable, reminiscent of the gold rush mentality, yet tempered with caution. Lessons from history are crucial as we navigate this era of wealth creation. The emphasis on prudent investment practices is paramount, especially to safeguard inexperienced investors from potential pitfalls. Unlike the dot-com era, today's AI startups have the luxury of staying private longer, shielded by robust financial backing from various sources like venture capital funds and tech investors. This extended period of private operation offers a protective shield, ensuring a more stable environment for both investors and startups alike. The concentration of this AI investment boom in Silicon Valley further solidifies its status as the tech hub of the world. The region serves as the epicenter of innovation, attracting substantial capital and talent, fueling the ongoing AI revolution. Read more about this transformative AI phenomenon: [Link to CNBC article](https://lnkd.in/gdZjaxQ3)

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With the advent of AI and the need to become a full stack PM, appreciation of engg systems and design will become even more important. However, most PMs don't get it. Even today in engineering focused rounds for PMs or when asked to solve a tech first product design problem/assignment, most PMs struggle - Eg. You have been asked to pick an API that you love and find ways to improve it? or you have been asked to improve a cloud based Integrated development environment (IDE) for developers? What do most of the PMs do? They immediately start throwing jargons - security, scalability, CRUD operations etc. or in the case of IDE for cloud developers will jump randomly to better code completion, debugging capabilities etc.? You are doing it all wrong. Here is the problem with most product managers - a. they get misled by the question just because it seems like a tech first product question b. most of them are not confident on tech parts in any case. Here is where you got tricked. Just because the product is tech first doesn't mean that your problem solving strategy changes. Ultimately, the product exists to solve a consumer problem and solve business goals for the organization. Let's say you have to improve an API for video conferencing The way you should go about solving this problem is looking from the lens of the consumer which just happens to be a developer in this case and visualize the process flow - refer my post in the featured section on how to visualize the process flow. So what are the steps - 1.Discovery - How does a developer discover the right API for his use case? Does he need a simple rest API or does he need more integrated support as a React SDK or an Android SDK and how easy it is for the user to find it. 2. Evaluation - What tooling has been provided to the developer to quickly evaluate the API for his use cases? Does the API come with preset defaults eg. I want to build an online classroom with the video API can I just use some presets which helps me quickly evaluate. 3. Onboarding - How do I quickly start? Are there quick start guides? Sample apps to play with. Documentation segmented by popular use cases 4. Implementation - How easy it is to get test access keys or test out a sample implementation. 5.Support - Integration support, test sandboxes, variety of presets to simulate high bandwidth, low bandwidth environments. So you see you should still follow the traditional structure of users, user journey, pain points & you will be able to come up with good solutions that 99% of PMs cannot. Stick to the process and you will be able to come up with good answers. Follow me for high quality content and practical insights https://lnkd.in/gHweK_q7 AND also here https://lnkd.in/ggUKftFq While you are at it grab my product assignments playbook, 19 real product cases, all successful submissions, real job offers, ~258 pages of content. ***Grab it now**** - https://lnkd.in/gHzyN-Xc #product #careers #interviews

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At the forefront of the AI-first technology era, a significant transformation is underway. Companies are increasingly turning to automation, leading to workforce reductions, while simultaneously engaging in a fierce competition for AI talent to propel AGI advancements, offering record-high salaries. The landscape reveals a crucial need to grasp the foundational pillars of creating competitive organisations, irrespective of the sector: 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗜𝗱𝗲𝗮𝘀 💡: These stem from exceptional minds, showcasing flashes of brilliance across generations. 𝗦𝘂𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 💸: Where innovative ideas thrive, financial backing naturally follows suit. 𝗘𝘅𝘁𝗿𝗮𝗼𝗿𝗱𝗶𝗻𝗮𝗿𝘆 𝗧𝗮𝗹𝗲𝗻𝘁 👨‍👩‍👧: Unlike ideas and money, 𝙚𝙭𝙩𝙧𝙖𝙤𝙧𝙙𝙞𝙣𝙖𝙧𝙮 𝙩𝙖𝙡𝙚𝙣𝙩 𝙧𝙚𝙢𝙖𝙞𝙣𝙨 𝙨𝙘𝙖𝙧𝙘𝙚, especially in the AI era focused on AGI development. 𝗜𝗻 𝘁𝗵𝗲 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗔𝗜–𝗱𝗿𝗶𝘃𝗲𝗻 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁, 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻𝘀 𝗺𝘂𝘀𝘁 𝗽𝗿𝗲𝗽𝗮𝗿𝗲 𝗳𝗼𝗿 𝗮 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮����𝘁 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻. 𝗧𝗵𝗲 𝗲𝗺𝗽𝗵𝗮𝘀𝗶𝘀 𝗼𝗻 𝘂𝗽𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗿𝗲𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗶𝗻 𝗔𝗜, 𝗔𝗴𝗲𝗻𝘁𝘀, 𝗮𝗻𝗱 𝗠𝗖𝗣𝘀 𝗶𝘀 𝗽𝗮𝗿𝗮𝗺𝗼𝘂𝗻𝘁, 𝗻𝗲𝗰𝗲𝘀𝘀𝗶𝘁𝗮𝘁𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹 𝘁𝗶𝗺𝗲 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁𝘀 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗶𝗻 𝘁𝗵𝗶𝘀 𝗲𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗹𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲. 𝗧𝗵𝗶𝘀 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗲𝗻𝗮𝗯𝗹𝗲𝘀 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗿𝗲𝗺𝗮𝗶𝗻 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗶𝗻 𝘁𝗵𝗲 𝘁𝗮𝗹𝗲𝗻𝘁 𝘄𝗮𝗿𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗶𝗻𝗰𝘂𝗿𝗿𝗶𝗻𝗴 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 𝗰𝗼𝘀𝘁𝘀. Let me know your thoughts. #upskilling #talentwars #business #strategy #ai #agents #talent

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