Tech to Transform

Leveraging Generative AI to Solve Recurring Data Ecosystem Problems Leveraging Generative AI to Solve Recurring Data Ecosystem Problems: A Data Practitioner's Perspective Artificial Intelligence (AI) has already started solving some process problems across industries, but it has barely scratched the surface of challenges within data ecosystems. As data practitioners, we see recurring issues throughout data lifecycle that persist regardless of the technology used. While some issues are systemic in nature, others are related to usage and interpretation of data. In this blog, we explore these common problems in data ecosystems, how AI can be leveraged to address them and potential risks involved.

Why the Future of Code Must Be Green   By Kedar Deo – Green IT Practice Head, Tech Mahindra Introduction: From Code to Carbon When I wrote my first lines of code almost 30 years ago, my biggest concern was whether my application would run without crashing. Performance tuning meant saving a few milliseconds off execution time. “Sustainability” wasn’t in the vocabulary of most developers. Fast forward to today, and the stakes are far higher. The code we write, the platforms we use, and the architectures we design have a direct impact on the planet . 💡 Fact: By 2040, the Information and Communications Technology (ICT) sector could account for 14% of the world’s carbon emissions — nearly 10x higher than in 2007. The good news? We can change that. The better news? It doesn’t mean sacrificing innovation — in fact, it’s an opportunity to innovate smarter.

Milestone Insights: What 15 Weeks in GenAI Have Taught Me Fifteen weeks. Fifteen posts. Countless conversations, fresh ideas, late-night rabbit holes, and more than a few “aha!” moments. When I launched this blog back in April with my very first post — Why Start a New Tech Blog When the Internet Is Already Full of Them? — I wasn’t trying to reinvent the wheel. I simply wanted to create a space where my 22+ years in technology could intersect with the new and constantly evolving universe of AI, ML, and Generative AI. Now, 15 weeks later, it feels like a natural milestone — a point to pause, look back, and capture the big patterns emerging before the journey accelerates again. Think of this as both a recap for long-time readers and a friendly on-ramp for anyone joining us now. And trust me, the bus is still very much at the station — there’s plenty of room to hop aboard. Why GenAI Feels Different Every decade or so, we see a breakthrough that shifts not just what we can do wit...

Alignment in AI: Why It Matters More Than Ever   In 2024, Saarthi Telecom, a fast-growing Indian telecom provider, proudly launched its new AI-powered customer support chatbot. Designed to handle thousands of queries daily, the chatbot promised 24/7 service, instant responses, and reduced call center load. At first, things looked promising. The chatbot handled basic queries in English with ease—billing issues, data plans, SIM activation. But trouble began when Meena, a customer from Nashik, tried to resolve a network issue in Marathi. “ माझं नेटवर्क चालत नाही ,” she typed. The chatbot responded with: “I’m sorry, I didn’t understand that. Can you please rephrase in English?” Frustrated, Meena tried again in Hindi. The chatbot still failed to interpret her message correctly. After multiple failed attempts, she gave up and called the helpline—waiting 40 minutes to speak to a human agent. Meena wasn’t alone. Thousands of users across Tamil Nadu, Maharashtra...