Is your AI strategy set up for success or stuck at basic implementation? Discover the five critical areas that make a difference. After 25 years in the industry, I've seen many businesses make the same AI strategy mistakes—over-relying on superficial measures like chatbots and simple automation. This video explains the five areas you need to focus on for a robust AI strategy: 1. **AI Powered Experiences** to evolve past what's now considered standard. 2. **AI Built Systems** that address the shift in the developer's role from code writing to oversight and orchestration. 3. **AI Run Operations** that boost productivity through automation across various departments. The real game changers, however, are: 4. **AI Discoverable Products and Services** ensuring AI recommendations pinpoint your offerings. 5. **AI Consumable Services** enabling AI to autonomously use your APIs and services. Embrace AI not just as a tool but as an active consumer. Watch to ensure your business isn't missing the mark. #AIstrategy #ProductivityBoost #AIFuture
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After twenty five years of building and scaling products, I'm seeing the same AI strategy mistakes over and over again. Most companies buy enterprise AI tools, add a chatbot, build a few agents, and then pause, wondering if that's enough. It isn't. Here is how I think about AI strategy across five practical areas, and most companies just think about the first three. First, AI powered experiences. AI helping users write, summarize, search, and make decisions inside products. What felt innovative six months ago already is table stakes. Second, AI built systems, code assistance, low code tools, faster shipping. Let's be clear. The role of developers is shifting from writing code to orchestrating and overseeing it. Third, AI run operations, automation, observability, faster incident response across engineering, customer service, HR, and sales. These first three areas matter. They make teams more productive, but they are largely internal optimizations. The real strategic shift is in the last two, and most leadership teams aren't thinking deeply enough about them yet. Fourth, AI discoverable products and services. When someone asks ChatGPT for a solution, can AI find you, understand you, and recommend you correctly? Fifth, AI consumable services. Can AI agents actually call your APIs and use your services autonomously to get work done? Here is the shift. AI is no longer just helping your teams or customers. Increasingly, AI is your customer. The companies winning aren't just using AI for productivity. They're positioning themselves to be discovered and consumed by AI. I'll break down each of these five areas in more detail in upcoming posts. Follow along if AI is a strategic priority for you.

Are you misusing AI in your product experiences? As industries evolve, AI-powered experiences are no longer a luxury but a standard. Enhancing user experiences means more than introducing new features – it’s about solving genuine problems within existing workflows. Avoid the three pitfalls that many teams fall into: creating AI for non-existent problems, implementing cumbersome processes, and segregating AI from users' workflows. Watch to learn how successful teams start with real pain points and quickly prototype AI solutions that seamlessly integrate. Ensure your AI tools enhance, not hinder, by addressing real needs and fitting into the daily flow of work. Don't make AI for the sake of AI; make it meaningful. Discover strategies to lift your AI implementations from basic to brilliant, where users truly benefit from intuitive, AI-driven assistance. #AIIntegration #UserExperience #TechInnovation
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This is where most companies start their AI journey. It makes sense because the ROI feels immediate. AI powered experiences are features inside your product that help users write better, search faster, summarize information, or make better decisions. Think copilots. Think AI assistance embedded directly into workflows. Think autocomplete that actually understands context. This was genuinely exciting a year or two ago. Today, it's expected, and that's the challenge. The bar keeps raising. What felt innovative six months ago is now table stakes. I've seen teams spend months building AI features that users try once and never touch again. And in almost every case, it comes down to the same three mistakes. First, the AI solves a problem the user doesn't actually have. It was built because it was technically possible, not because users will actually benefit from it. Second, the AI requires too much hand holding. If it takes longer to correct the AI's output than to do the work yourself, users will quietly abandon it. Third, the AI is not integrated into the real workflow. It lives in a separate tool, model, or screen, breaking momentum instead of enhancing it. The teams getting this right do a few things consistently. They start with a real pinpoint. They prototype quickly to see if AI actually improves outcomes, and then they embed AI directly into the flow where users are already working. AI powered experiences are table stakes now, but only when they solve real problems inside real workflows. Next, I'll talk about AI built systems, how teams are using AI to ship products dramatically faster.

Explore how AI-built systems are transforming the development process, reducing bugs, and increasing delivery speed by up to 40%. This video unpacks AI's role in code assistance, low-code scaffolding, and quality assurance without eliminating human insight. Learn how teams achieving real ROI set standards, invest in training, and focus on results, ensuring consistent adoption of AI. Plus, understand where AI shines—handling boilerplate tasks—while human developers tackle complex design decisions. Ready to redefine your development process with AI? Watch as we break down the mindset shifts necessary for success, focusing on orchestration and validation rather than just writing code. #AIBuiltSystems #FasterDelivery #TechInnovation
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When people hear AI built systems, they assume AI is writing all the code. That's not what's actually happening, and it's not the point. AI built systems are about using AI to help teams ship faster and better. Better code quality, fewer bugs, less time stuck in debugging. This includes using code assistance, low code scaffolding, and AI that helps with testing, refactoring, and documentation. But here is the important part, and this is where I have a strong point of view. Humans are not disappearing from these workflows. Their role is changing. Developers are shifting from writing every line of code to orchestrating, reviewing, and approving the work that AI produces from execution to judgment. The same thing is happening in operations, not creating every playbook from scratch, but reviewing them, refining them, and deciding when they apply. This isn't good or bad. It just is, and it requires a mindset shift. When the shift is handled well, teams see real gains, often 30 to 40% faster delivery. Without the mindset shift, AI gets treated like magic, and the ROI never materializes. The most common mistake I see is assuming productivity will improve automatically. What actually happens is uneven adoption. Some developers trust the tools and move faster. Others don't, and the teams end up with inconsistent quality and velocity. The teams getting real ROI do three things consistently. First, they set clear standards for how AI is used. Then they invest in helping teams learn how to effectively orchestrate, validate, and govern AI generated work. And they measure real impact, not just whether the tool is enabled. One last point. AI is excellent at repetitive boilerplate work. It's not as good at complex architecture decisions or deeply understanding your business. The developers who win use AI to remove the tedious parts and apply their judgment where it matters most. AI built systems aren't replacing humans. They are reshaping the work and redefining roles, and the teams that accept this reality will ship faster and with more confidence. Next, I will cover AI run operations, where AI takes on the behind the scenes work that keeps your business running.

Discover how AI run operations can transform your business and lead to significant returns on investment. AI's journey started with IT monitoring and incident detection, and has now extended to technology, support, customer service, sales, and HR. Learn how AI can detect anomalies, route incidents, triage support tickets, and highlight urgent sales conversations, all while minimizing costs and maximizing efficiency. But beware: AI is not a magic solution. Inconsistent telemetry, messy categories, and lack of clear playbooks can derail AI efforts. Successful teams clean up data, standardize processes, and implement feedback loops to ensure AI learns from real-life outcomes. The role of humans is not diminished; it evolves. As AI handles repetitive tasks, humans can focus on overseeing systems and preventing issues before they arise. Prepare to reduce incident response time by 20% and move from reactive to proactive operations. Watch to learn how to build a strong foundation for effective AI operations. #AI #BusinessOperations #AIEfficiency
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AI run operations is where AI takes on the behind the scenes work that keeps your business running, and most companies are sitting on far more opportunity than they realize. This started years ago with AI ops in IT, monitoring, alerting, incident detection. Today, it's much broader. AI run operations now span technology, support, customer service, sales, and HR departments. Any operational function where patterns repeat and decisions slow teams down. In technology operations, this looks like AI detecting anomalies before they become outages, routing incidents based on past resolution patterns, or generating runbooks from previous incidents. In business operations, it's AI triaging support tickets, surfacing customer risk, or highlighting sales conversations that need attention. The ROI here is significant. Downtime is expensive. Slow support frustrates customers. And reactive operations burn out your best people. But here is where companies get this wrong. They buy AI operations tools and expect intelligence to magically appear. AI can't detect issues if your telemetry is inconsistent. It can't triage tickets if your categories are a mess. It can't automate responses if there are no clear playbooks to begin with. The teams winning with AI run operations do the unglamorous work first. They clean up data, They standardize processes, and they create feedback loops so AI learns from real outcomes, not assumptions. I have seen teams cut incident response time by 20% simply by letting AI route issues based on who actually solved similar problems before. That's not magic. That's AI making obvious connections at a scale humans can't once the foundation is in place. And just like with AI built systems, humans aren't being removed here. Their role is evolving. Ops teams move from reacting to alerts to overseeing systems, tuning decisions, and preventing problems before customers ever notice. Next, I will cover AI discoverable products, how you can make sure AI systems can actually find and recommend you when users are looking for solutions.

Are you ready for your business to thrive in the AI age? Learn why AI discoverability is crucial as AI systems revolutionize how customers find products and services today. Gone are the days of relying solely on SEO and brand reputation. AI systems use structured information, trusted sources, and solid evidence to make recommendations. Can AI find and recommend your business correctly? This video unpacks what being AI discoverable means in practice and why ignoring this trend is a dangerous assumption. You'll learn about: - Presenting your business with structured, accessible information. - Integrating into the ecosystems where AI seeks data: developer platforms, APIs, and more. - Providing compelling evidence like customer outcomes and case studies. Even companies outside the tech space must prepare for this shift, as customers will expect AI to offer knowledgeable insights about all products. Don't let traditional assumptions blindside your market presence. Equip your business to be visible in the AI-driven landscape today. #AIDiscoverability #BusinessStrategy #AIRevolution
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The first three AI strategy areas I covered are about AI helping your teams and products perform better. The next two are different. They are about AI interacting with your business from the outside. Here is the shift most companies aren't thinking about deeply yet. When someone asks ChatGPT or any AI system for a recommendation, can that AI find you, understand you, and recommend you correctly? This is what I mean by AI discoverable products and services. Today, AI assistants routinely recommend products and services as part of the conversation, often without users ever clicking a link, and that changes the game. If a buyer asks what's the best solution for supply chain visibility in construction or which CRM works for health care compliance, does your company show up? Most companies assume they will because they have good search engine optimization or a strong brand. That assumption is risky. AI systems don't discover companies the same way search engines do. They rely on structured information, trusted data sources, platform integrations, and evidence they can reason over. I have seen companies with significant market share get completely bypassed in AI recommendations. Not because their product was worse, but because their information wasn't accessible or understandable to AI systems. So what does AI discoverable actually mean in practice? Three things matter. First, clear, structured information about what you do, your use cases, capabilities, pricing, who you serve, not marketing copy buried across dozens of pages. Second, presence in the ecosystems where AI looks for answers, developer platforms, public APIs, trusted directories, and partner knowledge bases. And third, evidence, real customer outcomes, case studies, and proof points that AI systems can reference when making recommendations. And if your product isn't digital today, this still applies. AI systems reason over digital interfaces and structured information. So companies in traditionally nondigital categories need to think seriously about where and how digital capabilities become part of their offering. Because customers will increasingly expect AI to reason about your product, whether you plan for that or not. The companies preparing for this now are deliberately documenting their value in ways AI can understand and placing that information where AI systems are already looking. If you're not thinking about AI discoverability yet, you are assuming customers will keep finding you the way they always have. That's a dangerous assumption. Next, I will cover AI consumable services where AI agents don't just recommend you, but actually use your services to get work done.

Discover how AI agents using your services will transform industries. In this video, we delve into the most advanced aspect of the AI strategy framework—AI consumable services. It's not just about recommendation anymore; AI systems are now executing tasks autonomously, creating new possibilities. Imagine AI agents that plan construction projects, analyze spending, and more without any human intervention. This shift requires businesses to rethink both technically—with safe APIs and effective documentation—and strategically, with innovative business models. Leading companies are already adapting, experimenting with AI-friendly policies and tiered access models. They're not just building for today's needs; they are constructing a future where AI drives transactions and operations. Are you prepared to innovate or risk falling behind? Watch to understand how you can incorporate these strategies into your business and become part of the AI revolution. #AIStrategy #FutureOfWork #AIInnovation
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This is the most advanced area of the AI strategy framework and the one that'll separate the leaders from followers over the next few years. AI consumable means AI systems don't just recommend your product or service, they actually use it to get work done. An AI agent doesn't just suggest a tool and step aside. It connects your service, calls your APIs, and completes the task on the user's behalf. Imagine an AI helping a construction manager plan a project. It doesn't just recommend a scheduling software. It pulls in project data and builds the schedule automatically. Or an AI helping a CFO analyze spend. It connects to the financial system, runs the analysis, and delivers insights without the CFO doing a lot of manual work. This is already happening in pockets. AI agents booking travel, ordering supplies, scheduling appointments. But most companies aren't set up for this and the challenge isn't just technical, it's strategic. Yes. You need APIs that AI can safely call, authentication that works for nonhuman actors, and documentation that AI systems can actually understand. But more importantly, you need to rethink your business model. If AI agents are consuming your services on behalf of users, how does pricing work? How do you prevent abuse? How do you maintain quality and control? The companies getting ahead of this are thinking and acting now. They are designing AI friendly APIs. They are defining clear policies for agent access. And And they are experimenting with pricing models where the customer is AI, not a human. One pattern I'm seeing work well is tiered access, low cost or free access for discovery and evaluation, and paid access when AI agents actually execute transactions or use premium capabilities. Companies that become AI consumable aren't just building for today's customers. They are building for a future where AI agents are doing the searching, the choosing, the buying, and even using of your service. That's the full AI strategy framework covering five key areas, AI powered experiences, AI built systems, AI run operations, AI discoverable products and services, and AI consumable services. If you want to pressure test how this applies to your business, feel free to reach out. I would be happy to talk.

Unlock massive savings by transitioning from a project to a product mindset. Many businesses are unwittingly wasting resources on short-term projects that lack continuous improvement and customer focus. Discover how embracing a product mindset can solve customer problems, ensure better adoption, and ultimately save your company significant amounts of money. Dive into the key differences in staffing, measures of success, and resource allocation between the two mindsets. Projects often focus on deadline-driven delivery, whereas products rely on continuous feedback and iteration. Learn how to better allocate resources for ongoing success. Stay ahead in the competitive world of software and AI by building resilient assets that delight customers, supporting sustained investment and engagement. #ProductMindset #BusinessStrategy #CustomerSatisfaction
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Most companies say they are building products, but they are actually running projects, and the difference is costing them millions. Our project mindset says we are building something with a fixed scope, a deadline, and a definition of done. Once we ship it, we move on to the next thing. A product mindset says we are building something that solves a customer problem, and we are going to keep improving it based on what we learn after its life. I've seen this play out dozens of times. A company builds a customer portal, launches it, checks the box, and then wonders why adoption is terrible. That's a project mindset. You build the thing, you're done, but the customer problem isn't fully solved, so they don't use it. A product mindset would say, let's ship a basic version, watch how customers use it, and iterate based on real behavior. The difference shows up in how you staff teams. Project teams disband after launch. Product teams stay together and own outcomes over time. It shows up in how you measure success. Projects emphasize on time delivery. Products measure adoption and customer satisfaction. And it shows up in how you allocate resources. Projects get funded once. Products get continuous investment as long as they are delivering value. Both mindsets are needed in large companies. But if you need to build a durable asset and delight customers, especially in the areas of software and AI, you need a product mindset.

Ready to revolutionize your business strategy? Discover the power of a product operating model! Unlike traditional IT setups with temporary project teams, a product operating model provides structured teams dedicated to specific products, focusing on continuous improvement and clear business outcomes. Learn how businesses have shifted from project-based approaches, starting with a singular product and scaling across the company. Understand the importance of measuring success through customer behavior and business impact rather than focusing solely on budget and deadlines. Join us to uncover how this model drives innovation and keeps companies competitive in the digital age. #DigitalTransformation #ProductInnovation #BusinessStrategy
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If you are a leader of a business that didn't start as a tech company, you've probably heard the term product operating model and wondered what it practically means. Here is the simple version. It's how your company organizes people, makes decisions, and allocates resources to build and improve digital products over time. Most non digital companies don't have this. What they typically have is an IT function that maintains systems and project teams that build one off digital solution when someone asks. That works fine if digital isn't core to your strategy. But if you're trying to compete on customer experience in AI, it breaks down fast. A product operating model means you have dedicated teams that own specific products, not projects that disband after launch. It means those teams have a clear mission tied to business outcomes, and they have autonomy to make those decisions about how to achieve those outcomes. It means you're measuring success based on customer behavior and business impact, not just whether you delivered on time and on budget. And it means you're investing continuously in digital capabilities, not treating every initiative as a one time project. The companies that made this transition didn't do it overnight. They started with one product and built a dedicated team around it. They proved that the product operating model works, then scaled it. You can do the same.

Are your digital projects failing to hit the mark? The secret to success may lie in how you approach building them. Creating a product that addresses customer problems rather than just adding features can lead to better adoption and business outcomes. It's easy to focus on capabilities like dashboards or notification systems, but these don't always align with what customers truly need. A product should solve a specific issue and every feature should be designed to assist in that process. Learn how to shift your focus from what technology allows you to do and what the business wants, to what the customer actually needs. This strategy requires a deep understanding of customer problems and purposeful iteration on your product's capabilities. If your projects aren't catching on, it may be time to evaluate if you are truly solving the problems your customers face. #ProductDevelopment #CustomerNeeds #BusinessStrategy
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Here is a pattern I see often a company builds something new, launches it, but then adoption doesn't follow. Business outcomes don't materialize. The problem isn't the quality of what got built. It's that they built features instead of a product that solves a real customer problem. Features are capabilities. A dashboard, a reporting tool, a notification system, they may be valuable, but not connect to a job the customer is trying to do. A product solves a specific customer problem and every feature exists because it helps the customer make progress on that job. When you build features, you may start with what's technically possible or what the business wants. When you build products, you always start with the customer's problem and build whatever is needed. And you iterate on product capabilities with a razor sharp focus on customer needs. This is easy to say, hard to implement in practice. You've done customer research. Is that enough? Your largest customer is asking for a feature. Do you build it? The answer is not that simple. I have worked on products where we sunset features which had adoption from a small set of vocal customers because we had to focus on the needs of our core customer base. If your digital initiatives aren't getting adopted, ask yourself whether you're building features or a real product that solves customer problems.

Discover the top three investments essential to building a successful product operating model. Failing to invest correctly could stall your progress even after years of effort. First, prioritize hiring dedicated product talent. Bring in seasoned professionals who understand how to discover customer problems and deliver iteratively, such as experienced product managers or engineering leads. Next, ensure your product teams have direct and continual access to customers. They need to understand customer experiences without the sales filter that many non-digital companies impose. Finally, upgrade to modern tools and infrastructure. Legacy systems slow down innovation. Equip your teams with AI, modern APIs, and real-time analytics to stay competitive and innovative. Companies making all three investments witness real improvements within six to twelve months. Don't let your strategy linger due to oversight in these critical areas. #ProductManagement #DigitalInnovation #BusinessStrategy
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If you're building a product operating model, there are three investments you can't skip, and most companies try to cut corners on at least one. First investment, dedicated product talent. And I don't mean existing talent just relabeled as product experts. I mean, people who know how to discover customer problems and ship iteratively. I recommend that you hire one seasoned product leader, could be a product manager or an engineering lead, and let them assemble the team. Second investment, direct access to customers. Product teams need to talk to customers constantly, not through a sales filter. They need to observe customers. Most non digital companies make this incredibly hard with account management concerns and approval layers. Third investment, modern tools and infrastructure. You can't run a product operating model on legacy systems that takes six months to make a simple change. AI advancements make this simpler. Product teams need to ship at least weekly. They need real time data on usage, and they need the ability to experiment. This means investing in APIs, analytics, and deployment processes that lets teams move fast. The companies that make all three investments see real results within six to twelve months. The companies that skip one of these spend years wondering why it's not working. If you are building a product operating model, budget for all three upfront.

Many companies measure digital products like projects—on time, on budget, feature delivery. But do these really indicate success? In this video, discover why traditional metrics fall short and learn how to measure true product success by focusing on business outcomes. We'll break down the importance of leading indicators like customer usage frequency for retention and time savings for efficiency improvements. Understand why feature delivery is an unreliable metric and how digging deeper into defining the right leading indicators can unlock the real reason you're building the product. Avoid relying on revenue or customer satisfaction as leading indicators—make business and customer outcomes your focus, not just outputs. Shift your product measurement strategy today and drive meaningful results. #ProductSuccess #BusinessOutcomes #MetricShift
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Many non tech companies measure digital products in the same way as projects. Did we finish on time? Stay on budget? Deliver what we said? Those metrics tell you if you executed well. They don't tell you if you built something that matters to customers and the business. Here is how to practically measure product success. Start with business outcomes. What specific results are you trying to improve? Is it customer retention, operational efficiency, revenue per customer? Then measure leading indicators of those outcomes. If you're improving retention, measure how often customers use the product and whether usage is increasing. If you're improving efficiency, measure time saved, manual steps eliminated, and whether error rates are going down. These leading indicators tell you if you are on the right track before the business outcome shows up in quarterly results. What you don't do is measure success based on feature delivery. Shipping 20 features means nothing if customers aren't using them or they are not driving results. It may be hard to define the right leading indicators for your product. If so, you need to dig deeper because that gets to the heart of why you're building the product. By the way, revenue or customer satisfaction are not good leading indicators. If you want product teams to drive customer outcomes, measure them on those outcomes, not outputs.

Unlock the potential of customer feedback! In this video, you'll learn how to transform the vast amount of data you collect into strategic decisions. Many companies misunderstand feedback, leading to feature bloat. The truth is, feedback reveals what customers say they want, not the underlying problem they're trying to solve. Discover how successful companies change this dynamic: - Start by asking 'why?' to uncover real customer needs beneath the surface requests. - Look for common threads across feedback. A single request could be unique, but similar issues shared by multiple users deserve your attention. - Prototyping is key; test ideas with users before committing to development. By focusing on problems, identifying patterns, and validating ideas, you elevate your decision-making process and product offerings. #CustomerFeedback #ProductDevelopment #BusinessStrategy
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Most companies collect tons of customer feedback, survey results, support tickets, feature requests, but they don't turn into clear decisions. Here is why that happens and how to fix it. The key problem is that most feedback tells you what customers want, not what problem they are trying to solve. And if you just build what they ask for, you get a bloated product. Customers will tell you they want better reporting. What they actually need is faster access to the three metrics they check every morning. The companies that turn feedback into great decisions do treat things differently. First, they ask why. When a customer requests a feature, they dig into the underlying job and the pinpoints. Second, they look for patterns across customers. One request might be an edge case. 10 customers with a similar problem means you found something worth solving. Third, they prototype and test with customers before building anything big. As always, these are easier said than done. If your largest customer gives you strong feedback, do you ignore them? If that feedback is aligned with product strategy, great. If it is a specialized ask, better to build a customer specific solution by extending your platform. Longer post for another day. So how do you turn customer feedback into decisions? Focus on the problem, look for patterns, and validate before you build.

Why I post short videos has nothing to do with going viral or building a following. In this short message, I explain the real goal: to be useful, gain clarity in my thinking, and connect with people. I share four reasons I started posting. First, usefulness: after years of lived experiences and learning from others, I feel motivated to share what I know, even if it only helps one person see a topic differently. Second, writing and speaking are tools for thinking. To talk about something publicly, I need clarity on what I’m saying and what I believe. Third, connection: these posts have helped me reconnect with people I worked with years ago and start conversations with people I’ve never met, which has been energizing. Finally, discomfort: I wanted to face the cringe and self-doubt of recording and posting regularly. It was hard to start, and I’m seeing how long I can keep it up. Thanks to the Zinc team and Arjun for nudging me along. #creators #communication #career
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If you follow me, you've likely seen that I've started posting some short videos. I wanna explain why I started doing that. The goal is not to go viral or build a following. Attention may be a side effect, but it's not a goal. The main goal of posting these videos is simple. I want to be useful. At this point in my career, I've accumulated a lot of lived experiences. I've learned a lot from others. Now I feel ready, even motivated, to share what I know. If I can help one person think about a topic a little differently, I would have achieved this goal. The second reason is that writing and speaking are tools for thinking. In order to talk about something publicly, I need to get clarity on what I'm saying. I need to understand what I believe in. The third reason is to connect with people. These posts have helped me reconnect with people I've worked with years ago and also sparked conversations with people I've never met. That is energizing. The fourth and final reason is to feel uncomfortable. I wanted to face the cringe and self doubt of recording and posting publicly on a regular basis. It was hard to get started. Let's see how long I can keep it up. Thanks to the Zinc team and Arjun for nudging me along.

You may know me through tech, product, or leadership roles, but this video shares my other passion: theater. I talk about cofounding Indus Creations, an Indian language theater group in Seattle, and how creativity became a way to bring people together. Indus Creations started with a simple goal: doing something meaningful for the Indian community in Seattle. Over time, it grew into large productions involving 100 to 200 people, including murder mysteries and dramas with songs and dances. A key lesson from the journey is staying connected to the community. As tastes and needs changed, we evolved too, because the purpose was always to create connection and shared experiences. I’ve enjoyed acting, directing, producing, and script writing, but the contribution I value most was setting fundamental values and bringing together people who took the organization to the next level. We even made a full-length Tamil movie, released it in India, and it can be watched on Amazon Prime Video today. Have you built something creative that became bigger than any one person? Share your story in the comments. #Creativity #Community #Theater
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Most people I work with know me through tech, product, or leadership roles. They may not know that I love doing theater, and I cofounded, Indian language theater group in Seattle. Indus creations started very simply with a goal of bringing people together and doing something meaningful for the Indian community in Seattle. But over time, it grew a lot. We put on large productions involving 100 to 200 people that wear murder mysteries, dramas with songs and dances. We evolved with the tastes and needs of the community because our goal was always to bring people together. I enjoy acting, directing, producing, script writing, but my most important contribution was to set up some fundamental values and bring together people who have taken the organization to the next level. At one point, we even made a full length Tambor movie. We released it in India, and you can watch it on Amazon Prime Video today. Reflecting on this, creativity has always been my passion, and I love to bring together a group of people to build something that has durable impact. Typically, it turns out bigger than the individuals involved. Have you done something like this? Please share in the comments.

Conviction beats comfort, and this short story proves it. In this video, you’ll hear how one risky interview move turned a standard conversation into a real dialogue by testing how the candidate thinks. Early in his career, the speaker worked at IBM straight out of engineering school, with great training and smart teammates. But he wanted more challenging problems, so he interviewed with a smaller firm even though he didn’t have the years of experience they might have wanted. About 15 minutes in, he felt in his zone and tried something unusual: he asked the interviewer if he could ask questions, questions designed to test how he thinks. It could have ended the interview, but instead the interviewer leaned in. They went deep into real world problems, challenged assumptions, and talked about how things really worked inside the firm. The interview became a conversation. The lesson: when your actions are anchored in genuine conviction, you stop doubting yourself and become effortlessly authentic. He got the job, and it eventually brought him to the US and opened more doors. #interviewtips #careerstory #authenticity
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Early in my career, I was working at IBM straight out of engineering school. Great brand, awesome training, smart teammates. But I was hungry for some challenging problems to solve. So I interviewed with a smaller firm. On paper, I didn't have the years of experience they might have wanted. But I had something else. I knew that my skills, my drive, and my attitude would make me valuable. The interview started normally. Polite conversation, a few standard questions. About fifteen minutes in, I knew I was in my zone. And then I did something unusual. I asked the interviewer if I could ask him some questions, questions that could test how I think. Risky move, maybe even interview ending, but he leaned in. What followed was one of the best professional conversations I've had. We started going deep into real world problems. I challenged assumptions. He shared how things really worked inside the firm. It stopped being an interview and started becoming a dialogue. I wasn't trying to impress him. I didn't even plan for it. I was simply being myself. That moment taught me something. When your actions are anchored in genuine conviction, you show up differently. You stop doubting yourself and are effortlessly authentic. I got the job. It eventually brought me to The US and opened more doors. But the real win? Learning that convection beats comfort every time.

Talent calibration can either strengthen culture or quietly weaken it. In this video, learn how to make talent calibration transparent, professional, and useful across a product and engineering organization. The context: a product and engineering team of about 170 people spread across seven countries, with multiple subcultures from acquisitions. The goal was to build a high growth SaaS product, and that required a new level of clarity and accountability. You will hear a hands-on approach that avoids closed-door decisions. First, define roles clearly with real job descriptions, then use them to hire a few people so expectations become concrete for everyone. Next, ask every team member to self-calibrate using a simple one pager covering experience, role fit, strengths, and development gaps. Then, managers place people on a standard performance versus potential grid and meet as a group to compare notes, helping everyone understand talent across the org. Finally, close the loop with team members in two tracks, product and engineering, fully visible across the team. It took two quarters, and the outcome was a culture where performance conversations are not a mystery. #leadership #engineeringmanagement #talent
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Think of a product and engineering team of about 170 people across seven countries. Talented people, smart people, many had come through acquisitions, which meant multiple subcultures. Our goal, build a high growth SaaS product that required a different level of clarity and accountability. Talent calibration sounds obvious, but in many companies, it's either deprioritized or done quietly at the top. Both approaches weaken culture. We took a hands on route. First, we define roles clearly, real job descriptions. Then we use them to hire a few people. That alone helped existing team members to internalize expectations. Second, we asked everyone to self calibrate. A simple one pager with experience, role fit, strengths, development gaps. Many were surprised. They assumed decisions would be made behind closed doors. We held open forums, explained why we were doing it, answered tough questions. Without that transparency, this would have failed. Third, managers place people on a standard performance versus potential grid, then we met as a group and compared notes. Many just didn't know about others in the org. Now the internal talent pool got clearer and new career paths were possible. Finally, we close the loop with team members, two tracks, product and engineering, fully visible across the team. It took two quarters, but we weren't building a grid. We built a culture where performance conversations aren't a mystery. They're open and professional.