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In this episode, Chitra reconnects with Srini, a long-time technologist and futurist, for a wide-ranging conversation about what AI means right now and where it could take us. They cover multi-tool AI workflows, generational shifts in adopting technology, information fatigue, and the diverging futures AI could create. Key points in the episode 00:00 Welcome, and why this revolution is different: it competes with the human brain02:32 Who's really at risk? Srini argues it's people with jobs today, not only the already underprivileged05:10 What AI has changed most for Srini: planning with unstructured data, using three tools to curate, critique and refine09:03 Aha moments: the AI that cited his own findings back as "anecdotal data," and the one that zipped past a checkpoint11:25 What his generation contributed: helping people adapt, with lessons from railway and bank computerization14:42 Learning from his son, nephews using AI on legacy code, and AI-set question papers19:36 Text vs images vs video, and why he'd like everything back in a spreadsheet25:46 Rapid fire: what AI has made more human, and what it has made less human27:24 Five years out: a K-shaped world, two possible paths, and why AI plus robotics is his biggest concern30:21 Why people are asking for face-to-face conversations again

“AI gives you wings,” “Wrong things in, rogue things out,” and “Each one, teach one.” From “What is this?” to an indispensable companionIn this episode of Software People Stories – AI and Me, I speak with Ramesh Rajagopal about how AI has moved from something unfamiliar and experimental to an everyday companion that helps him learn, explore, solve problems and make decisions. Ramesh describes his relationship with AI through some wonderful analogies—from Jeeves and Man Friday to Hanuman—capturing how quickly AI has become his go-to source for help. From information search to personal problem solvingOne of Ramesh’s most interesting observations is how dramatically AI has changed access to information. While exploring the life and legacy of Malcolm X, he was able to ask a series of questions and quickly understand what happened to Malcolm X’s family, his organisation and the events surrounding his assassination—something that would previously have required hours of searching. But it is his very real-world examples that bring the conversation alive. When a huge tree fell on the compound of his property in Bengaluru during heavy rains, Ramesh used AI to figure out whom to contact, which numbers to call and what options were available. After several attempts, the right contact was eventually found and the tree was cleared. And when his Lenovo laptop froze, he simply asked AI what to do, followed the instructions and fixed it himself. These moments led to perhaps the most powerful insight of the conversation: AI gives you wings. AI as a thinking partnerRamesh also uses AI to explore alternatives and support decision-making. Whether it is designing a solution for people travelling from India to the US, evaluating different approaches, or looking at investment decisions, he uses AI to generate options, compare pros and cons and structure his thinking. But he draws an important boundary: The human remains in the loop. AI can provide alternatives and analysis, but decisions—particularly decisions involving people—still need human judgment and accountability. Everyone has a role in shaping AIA particularly important part of our conversation is Ramesh’s view that not everyone needs to build an AI model to contribute to AI. The AI architecture may be developed by a relatively small group of specialists, but the knowledge that AI learns from comes from everyone. Blogs, experiences, expertise, documentation and shared knowledge all contribute to the ecosystem. His warning is equally important: “Wrong things in, rogue things out.” As AI multiplies and redistributes information at enormous speed, the quality and accuracy of what we contribute becomes increasingly important. Turning institutional knowledge into AIRamesh shares a fascinating example from PM Power, where years of accumulated expertise from experienced consultants has been captured through blogs and knowledge contributions. That knowledge has now been brought together into an internal AI system called Power Guru, where people can ask questions about management, careers, leadership and workplace situations and receive advice based on the collective experience of the organisation. It is a beautiful example of how institutional memory can become an AI-powered knowledge companion. The AI future: optimism with cautionWhen asked what he hopes AI can achieve over the next five years, Ramesh imagines a world where AI helps us produce food more efficiently, conserve water, generate electricity efficiently and ultimately create a happier, more comfortable world. But he also has a concern: AI used for warfare and destruction. For him, regulation, awareness and human responsibility will be essential to ensuring that the technology serves humanity rather than the other way around. Rapid FireOne AI tool you can't live without? LLMs. One thing you'll never delegate to AI? “My consciousness.” One word to describe your relationship with AI? Love. Your AI aha moment? Every day. Ramesh compares watching AI evolve to watching a child grow—there is always something new that makes you say, “Wow!” One final messageRamesh leaves us with a simple but powerful thought: You cannot ignore AI. Learn it. Use it. Contribute to it. And help others learn it. He sees a new divide emerging between people who understand AI and those who don't. His answer is beautifully simple: Each one, teach one. Because the future of AI isn't just about the people building the models. It is also about all of us who use them, contribute knowledge to them, question them and decide how they should be used. Ramesh can be reached at https://www.linkedin.com/in/ramesh-br-32a5111/?isSelfProfile=false

From democratizing intelligence to redefining how we lead, work and remain humanIn this episode of Software People Stories, Gayatri Kalyanaraman reconnects with Zafar, who brings nearly four and a half decades of experience across technology and multiple waves of transformation—from mainframes and personal computing to the internet, cloud and digital transformation. His observation about AI is simple but profound: previous technology shifts gave humans more powerful tools; AI is beginning to democratize intelligence. And with AI agents, even execution is being democratized. Beyond AI adoption: where is the business value?Zafar is enthusiastic about AI's potential, but cautious about the hype. Giving thousands of employees access to AI tools is not, by itself, transformation. The real question is: what has changed in the way the organization creates value? For him, meaningful enterprise value will come from redesigning workflows, integrating data, changing decision rights and building domain-specific AI systems—not simply adding AI assistants to existing processes. The leader of the futureAI also changes what leadership means. When information, analysis and execution are increasingly available on demand, leaders cannot rely simply on having the answers. Their role shifts towards asking better questions, creating clarity, setting context, exercising judgment, aligning people and taking accountability. The future may be less about managing people or technology and more about orchestrating human and AI capabilities around outcomes. What happens to our human capabilities?Perhaps the most thought-provoking part of the conversation is Zafar's concern about cognitive over-dependence. We already outsource calculation to calculators and recall to search engines. AI takes this much further by allowing us to outsource drafting, analysis and even parts of reasoning. His question is not whether we should use AI—we clearly should—but: What mental capabilities do we deliberately continue to exercise ourselves? He argues that productive struggle—the process of wrestling with a problem, making mistakes and changing our minds—is part of how we develop critical thinking and intellectual independence. AI and the human relationshipThe conversation then moves from cognition to something even more fundamental: human relationships. AI is infinitely patient, always available and highly personalized. But what happens if frictionless AI relationships begin replacing the messy, challenging and sometimes uncomfortable relationships we have with real people? That raises a bigger question about human agency: are we eventually going to outsource not only memory and reasoning, but also judgment, empathy and emotional support? The responsibility that comes with intelligenceZafar also reflects on the societal implications of increasingly autonomous AI. AI has extraordinary dual-use potential—it can accelerate scientific discovery and healthcare, while also being used for surveillance, manipulation, cybercrime and misinformation. As AI becomes increasingly agentic, his message is clear: governance and human oversight need to advance alongside capability. He argues for a risk-based approach to AI governance: a tool rewriting an email should not face the same level of scrutiny as an AI making decisions about healthcare, employment, credit or public security. Ultimately, this is a conversation about much more than AI technology. It is about what happens to organizations when intelligence becomes abundant, what happens to leaders when answers become cheap, and what happens to humans when machines become increasingly capable. And perhaps the most important question we leave with is: As AI becomes better at thinking for us, what must we continue learning to think for ourselves? Zafar can be reached at https://www.linkedin.com/in/zafar-ahmad-4704b76/

My guest today is Paramu Kurumathur: IT Leader, Author. Here is the snippets of interesting stories around how AI has changed his life and how he’s impacting AI - From building AI in 1980 to using AI to code, write, research and imagine the future What makes Paramu's story unusual is that his AI journey actually began in 1980, when his M.Tech project explored automatic program generation using structured data and if-then rules. Four decades later, he finds himself using generative AI in almost every facet of his life. AI as a companionFrom preparing a draft of his family will to asking Gemini to explain a television show, AI has become a surprisingly useful personal companion for Paramu. At work, he uses AI coding tools such as Cursor to turn ideas into working software—even when he doesn't know the programming language particularly well. One of his recent projects used AI, Python and a vector database to help people discover government schemes they may be eligible for. For Paramu, this ability to take an idea and quickly turn it into something tangible is one of the biggest "aha moments" of AI. AI and the authorAI has also found its way into Paramu's writing. He uses it for Sanskrit transliteration, translations, research, introductions and making his writing more conversational. But he is equally conscious of AI's limitations. Hallucinations become particularly problematic when dealing with subjects where the available data is limited, such as astronomy and ancient Indian texts. This leads to an important observation about the need for good foundational data. What should remain human?Perhaps the most interesting question in the conversation is what Paramu would never delegate to AI. His answer: the core idea of his books, personal relationships and the human connections involved in coaching and consulting. AI can help us create, research and accelerate—but there are parts of our lives where the relationship itself is the work. AI for social goodWhen asked what is underrated about AI, Paramu points to its potential for social and collective benefit. From identifying potholes and holding contractors accountable to helping people discover government benefits, he believes some of the most meaningful applications of AI could emerge outside traditional productivity use cases. The conversation ends with a glimpse into the future—from AI agents that can behave in surprisingly autonomous ways to Paramu's wish that technology could help people communicate when they cannot express themselves easily, or even objectively understand pain. And, of course, there is a rapid-fire round before we finish—with one final thought: perhaps five years from now, Paramu might return to the podcast… in AI form! A conversation about AI, human judgment, social good and what we choose to keep uniquely human. And finally… a rapid fireThe conversation ends with a rapid-fire round. Favorite AI use case? A tool. What is overrated about AI? Nothing. What is underrated? AI for social and collective benefit. What would you never delegate? The core message of his work—and the human relationships that matter. And perhaps the perfect closing thought: when Gayatri imagines that five years from now Paramu might be sitting next to her in AI form, Paramu signs off by saying he enjoyed the "stimulating" discussion—while joking that he always confuses stimulating with simulating. A fitting end to a conversation about humans, machines and everything in between. Paramu can be reached at https://www.linkedin.com/in/paramu-kurumathur-01489a6/ and paramukurumathur.com

My guest today is Yeshwant Satam, an ICT professional and consultant with over four decades of experience. In this conversation, he freely shares: His four-decade journey from soldering boards and PROM patching to firmware, software, IT services leadership, and manufacturing automation.Continual learning multiple programming languages including Pascal, Fortran, C, and Occam, then rapidly building capability through curiosity and commitment.Memorable moments including reverse engineering an IBM PC BIOS before realizing the boot floppy was still in a drawer in the US.The early days of Indian software exports: faxed requirements, shared processors, STPI floppy submissions, and the birth of professional delivery processes.That managers in technology must stay connected to the craft, even if they no longer code every day.Tips for great crisis leadership which includes staying calm, finding the real problem solvers, shielding the team from noise, and tracking technical progress closely.On AI in manufacturing, he distinguishes stable predictive AI in core operations from generative AI, which is currently safer at the fringes and in human-assisted decision making.Career advice for new entrants: study where hiring is moving, reskill seriously, and do not wait for a company to teach you everything.Career advice for mid-career professionals: get back in touch with technology, learn continuously, and show that you can move with new directions.Actionable takeaway: stay curious, stay close to the work, respect people who solve problems, and never let a title separate you from learning. Yeshwant Satam is an ICT professional and consultant with over four decades of experience. Over his career he has experience in multiple domains and technologies. A few highlights: Solutioning, managing and guiding full life cycle projects, including network managed services.Managing global delivery units with multi-million USD P&L responsibility.Building long-term sustainable customer and partner relationships.Work experience spans from conceptualizing IT solutions and products on heterogeneous platforms, to successful implementation and support in diversified cultural environments.International exposure and is conversant in dealing with international customers.Consistent track record of driving teams in achieving higher customer satisfaction. Guide teams in establishing processes to optimize effectiveness of IT deliveries.Defining and designing IT architectures and approaches for solutions in OT to IT integration in manufacturing enterprisesIdentifying technologies, frameworks and standards for practical and fit-for-purpose solutions www.linkedin.com/in/yeshwant-satam-a3b1809

My guest today is Arun Subash Manickam. An Engineering leader at Google. In this conversation, Arun shares: his journey from Chennai schooling to computer science and a long career in networking, startups, acquisitions, and Google.transitions from individual contributor to manager, startup life, acquisitions, autonomy, AI, and career growth.learning continuously, adapting to change, and working hard with purpose as core career principles. Highlights A teacher’s suggestion changed Arun’s path from commerce to computer science.Early Cisco work built strong engineering foundations, while startups deepened product and business understanding.Startup experience offers intense learning, faster execution, and visible impact.Moving from IC to manager requires mindset change, feedback, and self-awareness.Hybrid work balances flexibility with in-person collaboration and team bonding. Acquisitions vary: autonomy depends on overlap, integration model, and business uniqueness.AI is a major productivity enabler, but domain knowledge remains essential.AI-generated code and documents still need clear human direction and review.Career advice: learn, adapt, and work hard—smartly and consistently.Late-career focus shifts toward impact, enjoyment, experimentation, and grounded personal practices. https://www.linkedin.com/in/arun-subash-manickam-768b1416/ [email protected]

My guest today is Dr. Kishore Reddy, co-founder of & Chief Strategy Officer at ProHance. Conversation highlights: Background & Early Life: Kishore grew up across India—moving from Gujarat to Delhi and eventually settling in Bangalore, which he considers his home.Medical Career & Transition to IT: Completed his MBBS from Bangalore Medical College with plans to pursue neurology residency in the US, inspired by family members.After facing US visa denials (under section 214B), his brother Kiran suggested exploring the booming IT field around 1998–1999.Approached Sudhir Sharma at Network Solutions to learn IT on the job without demanding a salary.Leveraged his medical background's intensive reading habits to quickly master IT manuals.Early IT Experience & Growth: Had early exposure to computers during childhood thanks to his father and brother, including learning GW-BASIC.Worked on a World Bank-funded Health Information System (HIS) / Hospital Management Information System (HMIS) project for the Government of Karnataka.Mentored by DVN Sarma, gaining early exposure to data analytics and statistics.Network Solutions was acquired by IBM in 2005, leading to Kishore working at IBM for four and a half years.Founding Jamocha Tech: Decided to start his own venture in October 2008 and teamed up with Rajesh (his former colleague from Network Solutions).Received early mentorship and backing from Sudhir Sharma.Named the company Jammocha Tech over coffee at Barista, driven by their passion for Java technology.Evolution to ProHance: Initially built multiple products (e.g., network management, energy management) before streamlining focus.Shifted roles based on advice: Kishore transitioned to sales/technology head, while Rajesh handled delivery and support.Coined the name ProHance from "Productivity Enhancement" ("Pro" + "Hance").Rebranded completely to Prohands to avoid market confusion between the company and product names.Formally established ProHance India Private Limited in 2020, with ChrysCapital taking a majority stake in 2023.Key Entrepreneurial Takeaways: Adaptability: Expect the journey not to be a straight line and remain flexible.Team Building: Recognize that while founders start the journey, the team drives the company's ultimate success.Brand Focus: Avoid confusing customers with too many entity/product names; stick to one strong identity. https://www.linkedin.com/in/kishore-r-2a37aa5/ Experienced Chief Technology Officer with a demonstrated history of working in the information technology and services industry. Strong product engineering skills. Built and rolled out several large enterprise class products across various domains.

In this episode of Software People Stories, Gayathri Kalyanaraman speaks with Vasu Vadlamani, a technology leader, architect, entrepreneur and Professor of Practice, about a career that has evolved alongside the software industry itself. About his accidental start to an AI Era tech leader who’s defining mantra has been Every role has an expiration date!  Vasu takes us back to the early 1990s, when software meant client-server systems, handwritten notes and face-to-face communication. From his early work with Citibank to working with Sun Microsystems, building a professional services organization, experiencing the disruption that followed Oracle’s acquisition of Sun, and later taking on technology and architecture roles at HCL and Cognizant, his journey reflects several waves of technology transformation. But perhaps the most interesting transition came later. While working at Cognizant, Vasu found himself asking a fundamental question: Why should the gap between college education and industry readiness be so large? This led him to work with educational institutions with a very practical goal — to make graduates industry-ready much sooner. Eventually, that became the driving force behind his move from corporate technology leadership to becoming a Professor of Practice. Today, Vasu sees himself less as a software professional and more as someone whose purpose is to help people do their best — in technology, in their careers and in their interactions with others. The conversation explores: The early days of software and how dramatically the industry has evolvedWhy business understanding helped Vasu become a better software professionalBuilding and scaling a technology services organization from zero to 150 peopleNavigating the difficult period when the Sun Microsystems ecosystem disappearedMoving from customer-facing roles to architecting internal enterprise systemsThe philosophy of making software development “zero maintenance”Why the gap between education and industry continues to matterMaking graduates “production ready” rather than simply degree readyWhy professors also need exposure to real-world engineeringCreating safe, sandboxed opportunities for faculty and students to work on real enterprise use casesWhat AI means for technology careersWhy roles will change, but technology itself isn't going awayThe importance of resilience, curiosity, teamwork and being “an engineer at heart” At the heart of the conversation is a simple message from Vasu: Don't worry about the future of technology. Keep learning, keep adapting, and keep building. There will always be a place for people who are willing to do the work and find their way. Quotable Quotes “Don’t worry about AI. Roles will change. The industry will still be there.” “If you fail, use your knowledge to get up and rise again faster.” “IT is a team’s play. One person alone cannot run the ship.” “The younger generation will always be smarter than me. If I can succeed, Gen Z will definitely succeed.” “If Vasu can find a way, you are much smarter than me. You will find a better way.” Vasu Vadlamani is an accomplished IT Professional offering 30 years of experience in integrating Technical and Domain expertise (Financial Services), focusing on Large Complex Digital Transformation Initiatives. An efficient Communicator possessing strong Coordination, Interpersonal, Mentoring and Team Management Skills. Cross Functional Services Leader managing multi geo, multi service line deliveries for over $440+M.  Vasu was  CTO of ADM Insurance Domain, responsible to create and execute an Information Technology Strategy to modernize enterprise technological applications, moving away from their legacy apps to modern On Cloud tech stack. Co-authored the (C)ontain, (M)aintain (I)nvest philosophy so significantly enhance the business value of applications. His  research interests span Artificial Intelligence & Machine Learning, Cloud & Distributed Systems, Data Science, Software Engineering practices, and CS Education & Pedagogy. He is  particularly interested in how emerging technologies reshape both industry workflows and the way Computer Science itself should be taught at the graduate and postgraduate level. Vasu can be reached at https://www.linkedin.com/in/vasuvadlamani/

In conversation with Ashish Jha, Chitra and Ashish explored - His childhood fascination with science and literature to working with Tata Steel as an industrial engineerForay into data science and analytics with GECIS and Genpact getting deep into math and statsConverting customer problems into math equationsBenefitting from great mentorship, anchoring customer conversations very early on in his careerLeadership lessons in inspiring confidence in difficult momentsEntrepreneurship path and founding Analytical Wizards and being a very hands on founderCreating and holding a strong value-based organizational work cultureBuilding compassionate work placesThe human in the centre of all AI and everything we do, how patient journeys and decision engines impact livesIntegrating wisdoms to start Sunny Tribe EthosWhy he strongly believes that humans should build strong, irreplaceable foundations and grow as “a whole person”.Being self aware as a message for all young and aspiring tech folk Guest Bio: Ashish Jha is an entrepreneur and co-founder of Sunnytribe Ethos, where he is building and scaling Thrivin’Up, a holistic personal development platform that maps 21 dimensions of growth ranging from sleep and fitness to emotional balance, resilience, deep work, and purpose. He began his career as an Industrial Engineer at Tata Steel in 1999, and later moved into data science with GE Capital. Most recently, he co-founded Analytical Wizards, a technology-driven data science company, where he led India Operations, HR, and the Data Science vertical. After a deliberate hiatus exploring literature, music, and meditation, Ashish returned with renewed clarity to focus on empowering individuals and guiding technology founders in building collaborative, resilient cultures. Beyond work, he enjoys spending time with his family, learning music, and writing. Ashish is reacheable via https://www.linkedin.com/in/ashishrjha/ and https://www.thrivinup.com/

My guest today is Vijays Kadiala, ,Executive Director  for Architecture, Engineering, AI, Data, Cloud in DBS Tech India talks about his accidental start to an AI Era tech leader who’s defining mantra has been Every role has an expiration date!  Conversation highlights 00:32 — 25 years of a career that wasn't planned Vijay introduces his journey across startups, TCS, Infosys, Capgemini, JPMorgan Chase and DBS, with data and data-intensive applications as a recurring theme. 02:35 — From a two-bedroom startup to the corporate world Vijay recalls his unexpected entry into entrepreneurship after graduating with no job offers and describes the freedom, ownership and lack of bureaucracy that made those early startup years memorable. 05:39 — Why he left the startup A fascinating look at how social and family expectations influenced career decisions in the early 2000s—and why working for a recognised IT company mattered so much at that time. 07:57 — Has entrepreneurship become a label? Vijay reflects on today's startup culture and the difference between genuinely solving a customer problem and simply wanting to be called an entrepreneur. 10:02 — Are GCCs really global capability centres? Vijay offers his perspective on GCCs, suggesting that organisations should think beyond the label and focus on what makes an India-based technology centre genuinely different and valuable. 11:09 — The “expiration date” of a career role One of the central ideas of the conversation: every role has a lifespan. Vijay's approach has been to find a problem, solve it in roughly 18 months, create customer value and move on to the next challenge. 14:19 — Make yourself redundant Gayatri shares her own three-year theory, while Vijay explains why he believes professionals should constantly create enough value for their current role to become unnecessary. 15:19 — Choosing the uncomfortable path From production support to PL/SQL to data architecture, Vijay describes several career decisions where he deliberately entered unfamiliar territory. 18:10 — Moving to the US as a data architect A new role, a new country, a new culture and a new way of communicating. Vijay talks about the challenges of his first international assignment. 19:29 — Taking the biggest career bet In 2018, Vijay chose to move from people management into an executive individual-contributor role at JPMorgan Chase—putting his promotion trajectory at risk in the process. 22:26 — You have to make the decision A powerful discussion about career choices, risk and the importance of betting on yourself. Even a wrong decision, Vijay argues, can cost only a year or two in a career that may span 35–50 years. 23:11 — Building a second identity through writing Vijay explains how his leadership talks evolved into his first book and why his leadership philosophy begins outside the workplace. 24:58 — What driving can teach us about leadership His unusual leadership framework connects everyday driving behaviour with leadership behaviours—how we respond to rules, pressure, uncertainty, other people and unexpected situations. 26:57 — Writing with the help of LLMs Vijay and his son began putting their ideas on paper and then using LLMs to help develop them. His philosophy is simple: the ideas and experiences remain human; AI becomes a tool to help express them. 28:01 — Giving back to the next generation Vijay has spent nearly 14 years visiting universities and colleges, sharing practical experiences with students and helping them accelerate their journeys. 40:01 — Learn across cultures Vijay encourages professionals to work with organisations from different parts of the world—not only to gain technical experience, but to understand how culture influences leadership, communication and behaviour. 45:01 — What will technology careers look like in 2031? Vijay predicts that AI agents will transform traditional roles such as business analysis, testing and software development. Instead of specialised roles operating in isolation, he sees a future of professionals combining technical depth, domain knowledge and AI capabilities. 50:00 — From I-shaped to M-shaped professionals Vijay describes the evolution from deep expertise in one area to professionals who have multiple areas of technical depth combined with domain understanding. 52:00— Four skills for the AI era Curiosity — the ability to keep asking “Why?”Domain experience — understanding the business and context behind the technology.Orchestration — knowing what humans should do, what agents can do and where humans and agents should work together.Judgment — making decisions when AI produces ambiguous, conflicting or potentially incorrect answers.55:00— Why curiosity matters Vijay reflects on how traditional cultures can sometimes discourage questioning and argues that curiosity will become one of the most important capabilities in an AI-driven world. 56:00 — Five movies that imagined the AI future Vijay recommends Terminator 2, I, Robot, Surrogates, Passengers and WALL-E as interesting ways to think about different dimensions of an AI-driven future. Key Takeaways Your first career decision doesn't have to define your career. Sometimes the best opportunities begin with circumstances you didn't choose. Get comfortable being uncomfortable. Vijay's biggest career moves often came when he knew very little about the new role. Don't wait for your role to become obsolete. Create value, make the role redundant and move forward. Take calculated bets on yourself—even when the safer option is available. Build your career across people, technology and cultures. Leadership is not something that exists only inside a workplace; it is reflected in our everyday behaviour. AI will not simply replace technology roles—it will reshape them. The future professional will combine technical depth with domain knowledge and the ability to orchestrate AI. Curiosity and judgment remain deeply human capabilities. Vijaya Kadiyala is a technology leader with over two decades of experience spanning data, AI, cloud and enterprise transformation. Having worked with organisations such as TCS, Infosys, JPMorgan Chase and DBS Bank, he has evolved from a deep technical specialist into a leader shaping technology at GCC scale. At DBS Tech India, Vijaya works at the intersection of Data, AI, Cloud and enterprise transformation, helping build technology capabilities for a highly regulated financial-services environment. His journey also includes patents, innovation awards, mentoring and building high-performing technology teams. What makes Vijaya's story particularly interesting is the evolution of his career—from being a hands-on technologist and database expert to becoming a technology and transformation leader navigating the rapidly changing world of AI. Vijay can be reached at https://www.linkedin.com/in/vijaya-kadiyala/

Intro: Jithu Gopal is a fintech product leader based in Bangalore, with 18+ years across engineering, consulting, and product. He is AVP Product at Scripbox and currently owns the charter of building the advisory experience that'll help a million families make confident choices about their portfolios. Before Scripbox he co-founded Nilenso, a boutique technology consulting firm, where he built a social network, rewrote the checkout experience for a ticketing platform, and took on a few other projects. He is curious about what creates habits and what motivates people. Shownotes: In this conversation, Jithu Gopal and Chitra Gurjar explore - Working as a software engineer and an early observer of the cost of quality in workActive community-based learning from Java and Ruby user groups in early careerBuilding a social network , learning the “ethics” of software engineering, recognizing and applying design patterns Work at Nilenso, an employee-owned cooperative-like company, applying functional programming principlesLiving the principles of “being agile”, using Test-Driven-Development to shape a problem and approach it from the outside, rather than just build, staying in the problem spaceThe versions of software engineering, 1.0 and 2.0Indispensable systems thinkingShifts in Product Management, building strong foundations of working with engineering using high agency and trust AI Mavens, followers, the power and tools to leverage knowledge and do way moreExamining AI in layers for any organization or system How we organize ourselves differently in the age of machinesKeeping the “Why” in focus with organization memory and problem memoryWhat “post AI” looks like - awakening consciousness and invoking agency, AI cycles bringing more value addSocial bonding as a key to young people’s future of work and lifeBooks that have had a powerful influence on him.For aspiring software engineers to keep building and asking about what “good” looks like Few conversations flow back and forth in time, not unlike when you meet friends from childhood. In this episode of the Software People Stories podcast, I found myself having tremendous deja-vu moments as Jithu Gopal shared his flow of thoughts across various aspects of software engineering through his 18 plus years of work, how his interests and ways of connecting what he reads, experiences and works with have significance and relevance in the AI whorl that seems to be sweeping us with it and what habits and disciplines can hold us through as we begin to figure out new ways of working, learning and what the future might hold for our children and aspiring engineers. Listen on.

In this episode of Software People Stories, I speak with Mario Lewis, a manufacturing specialist turned IT expert with over 35 years of experience. He talks about his career that began far away from software—in mechanical engineering, manufacturing, operations, and quality—and eventually found its way into the software services world. We talk about what manufacturing can teach software teams, why domain knowledge and business understanding matter, how to stay adaptable through career transitions, and Mario’s experiment with AI-assisted software development through Project Klyve. It’s a thoughtful conversation about learning, reinvention, and doing meaningful work—even when the path is unexpected. Highlights from this conversation: Early passion for aviation led to mechanical engineering, manufacturing, and operations research.Manufacturing taught lessons in process rigor, quality, planning, and business constraints.Software became a chosen path, not a passion, but Mario approached it with discipline and curiosity.Adaptability emerges as a key career skill across industries and eras of change.Mario argues that technology is an enabler; real value comes from solving business problems.Domain depth and tacit knowledge are becoming more important, especially in GCC-style models.Project Klyve tested how far AI and agentic tools could go in building a complete application.Advice for early-career professionals: master fundamentals, understand hardware, networks, data, and security.Advice for mid-career professionals: keep learning, build domain depth, and understand market forces.Retirement feels comfortable because IT was meaningful work, but not Mario’s core passion.Mario began his career on the shop floor with planning and manufacturing optimization roles in machine tool and switchgear manufacturing. That early focus on physical manufacturing and basic operations grounded his transition into IT services and enterprise software, where he spent over 35 years leading delivery, consulting, operations and business teams. Along the way, in 2006, he authored a book on the realities of IT service offshoring based on his practical experience in the field. At the end of 2024, Mario stepped away from full-time corporate roles into retirement. He spent that time building Klyve, an automated desktop software factory that implemented an SDLC to build full software applications using agentic AI. It was built around strict engineering controls, deterministic design, and local data privacy. For Mario, managing career shifts comes down to fundamentals. Staying useful across changing industries requires an openness to lifelong learning, disciplined routine, steady execution, and personal standards while tracking industry and technology trends." Mario may be reached at: mariolewis @ gmail.com. The Klyve repo is at https://github.com/mariolewis/klyve_factory His book (from 2006) is available at https://www.amazon.in/Application-Service-Offshoring-Insiders-Guide/dp/0761935258

In this episode of Software People Stories, I speak with Saraswathi, a marketing leader with over 15 years of cross-industry experience,  about the role of marketing in technology—especially how product marketers translate technical ideas into stories customers can understand and care about. We talk about digital communication, customer empathy, product adoption, AI in marketing, useful metrics, and practical career advice for anyone curious about moving into marketing. It is a thoughtful conversation on bridging products, people, and business impact. Among the highlights from this conversation are: Marketing fundamentals stay consistent, but customer pain points differ across industries.Digital content must be concise, compelling, and quick to capture attention.Audience niches and personalization help messages resonate better.Product marketers act as the bridge between product, UX, sales, and customers.Training works best when broken into short, accessible, in-product learning moments.Email remains effective when segmented and relevant to the audience.AI helps with competitive research, brainstorming, mockups, and faster first drafts.Brand voice can be protected by giving AI clear style guides, examples, and do’s and don’ts.Marketing impact should connect to opportunities, conversions, and revenue—not only vanity metrics.Support tickets offer unfiltered customer insight and can shape better messaging and product decisions.Career switchers can use transferable skills and start by getting closer to customers.Planning, yoga, short walks, and pausing before reacting help Saraswathi stay calm and grounded. Saraswathi Shanth Kumar (Sara) is a marketing leader with over 15 years of cross-industry experience translating complex products into compelling market stories. Her career spans advertising, journalism, SaaS, and Big Tech, leading to her current role at an edtech company in the Bay Area. Sara sees product marketing as the critical translator between product and market — and approaches every challenge with a problem-solver's mindset and a startup's appetite for experimentation. Outside of work, you can find her gardening, practicing yoga, and hiking the many trails in the Bay. My LinkedIn: http://www.linkedin.com/in/saraskumar

My guest today is Chander Sankaran, an ERP and change management Specialist. With a professional background in finance and accounting, he has played many cross-functional roles that bridge the gap between finance and information technology.  In this conversation, Chander talks about Beginning his career in finance and accounting, then entering software through early ERP implementation work in a manufacturing plant in India.His move to the U.S. in the late 1990sHow his accounting and operational background helped him stand out in consulting by giving him stronger context for business requirements.Critical factors for smooth go-livesThe importance of change management for any enterprise technology work How factors such as global teams and hybrid work models require flexibilityThe benefits of AI in improving individual productivitySome concerns about AI’s environmental footprint and its potential effect on entry-level roles.He also shares practical career advice including keeping a resume updated, understanding that everyone is replaceableHis personal practices for staying grounded Chander S is an ERP Specialist based in Texas, and originally from Chennai, India. With a professional background in finance and accounting, he has played in cross-functional roles that bridge the gap between finance and information technology. Outside of work, Chander enjoys yoga, trekking, and volunteering in his local community. He may be contacted at https://www.linkedin.com/in/chander-sankaran-9027503/

My guest today is Mathangi Sri Ramachandran, Co-Founder of YuVerse. The conversation included Mathangi ’s early fascination with data-driven decision-making → AI as a force for democratisation → empathy at scale → career choices driven by impact → sustaining a long career as a woman → the ecosystem behind innovation and patents. 00:00 – Introduction: A life and career deeply rooted in AI Mathangi introduces herself as someone whose “heart and soul” are in AI. She talks about her role as CEO and co-founder of U-verse, a last-mile AI company focused on taking frontier AI models beyond experimentation and applying them to real business workflows and outcomes. 01:20 – What does “last-mile AI” really mean? Mathangi explains the gap between powerful frontier models and actual enterprise outcomes. She discusses how AI transformation requires bringing together technology, workflows and the human elements of work—across banking, insurance, real estate, retail and other industries. 02:30 – A 20-year journey in data science Long before the current generative AI wave, Mathangi was working in data science and analytics. She reflects on the long history of AI and reminds us that data-driven decision-making, machine learning and conversational technologies have been evolving for decades. 03:50 – The campus interview that changed her career direction A simple example during a GE campus interview at NIT Trichy—using data to decide where windmills should be located—sparked Mathangi’s interest in scientific, data-driven decision-making. That idea became a defining theme throughout her career. 05:00 – Using data to improve decisions at scale From marketing analytics to risk scorecards in financial services, Mathangi saw first-hand how large-scale data could improve enterprise decision-making. She shares her belief that moving from human judgement alone towards data-informed systems can help reduce bias and democratise access. 06:30 – Can machines deliver empathy at scale? In one of the most thought-provoking parts of the conversation, Mathangi challenges the assumption that machines cannot be empathetic. Drawing from her experience with conversational AI, she argues that machines can deliver consistent empathy across hundreds or thousands of difficult interactions in ways that are extremely challenging for human agents. 07:40 – Why difficult customer conversations can escalate Using debt collection conversations as an example, Mathangi explains the emotional burden placed on human agents who may handle a hundred difficult calls every day. She demonstrates how quickly a human-to-human interaction can escalate—and how well-designed conversational AI can maintain consistency and bring the emotional temperature down. 09:35 – From an idea today to an outcome tomorrow The arrival of large language models has dramatically shortened the distance between an idea and its implementation. For Mathangi, this makes the current era one of the most exciting times to work in AI. 10:55 – What continues to motivate her after two decades? The answer is simple: possibilities. Mathangi talks about her desire to use technology to build a better world by reducing bias, improving decision-making and democratising access to services. 11:20 – AI, financial inclusion and a more equitable world Better decision-making can enable deeper financial inclusion and expand access to capital. Mathangi connects AI and data science to a larger societal purpose: ensuring that more deserving people can access opportunities without being excluded by individual biases or subjective judgements. 12:50 – AI and access to healthcare and emotional support Mathangi explores the possibilities of AI in healthcare and therapy, particularly for people in underserved communities. She imagines a woman in a remote village being able to safely access a culturally aware, local-language AI companion when human support may be unavailable or difficult to approach. 14:45 – Why this is the best time to be working in AI Ideas that once took years to reach the market can now move from concept to implementation at extraordinary speed. Mathangi reflects on why she has never enjoyed her work more than she does today. 15:55 – Where the rubber meets the road: making AI deliver impact Looking across her career, Mathangi describes her current entrepreneurial journey as particularly impactful because she can not only build AI solutions but also take them directly into enterprises—improving processes such as customer conversations, underwriting and claims processing. 17:40 – The AI tailwind and pressure from the boardroom AI adoption is increasingly being driven from the top. Boards are asking organisations what they have done with AI and, importantly, what measurable impact it has created. Mathangi discusses both the opportunities and the risks of this pressure. 18:35 – Leadership is hard 95% of the time Mathangi offers a candid perspective on leadership: most days can be difficult and can test your sense of purpose. But the small percentage of moments when you see meaningful impact can make all that difficulty worthwhile. 19:15 – Choosing a bigger canvas over compensation Throughout her career, Mathangi has made decisions based on one guiding principle: where can she create the maximum impact? At times, that has meant walking away from significant compensation and lifestyle benefits in exchange for a larger canvas on which to build and contribute. 20:55 – A lesson from her mother: “Keep your job” Mathangi shares one of the most important pieces of advice she received from her mother. Through motherhood, travel, guilt and the many pressures women encounter, she remained determined not to compromise on her career. 21:40 – Designing life around a career, not a career around everything else Rather than fitting her career around domestic responsibilities, Mathangi says she consciously worked to organise her personal life so that her career could continue. She reflects on the grit required to sustain a career over more than two decades. 23:20 – The invisible infrastructure behind a long career Mathangi speaks with gratitude about mentors, organisations and, especially, her family. She explains why support at home is foundational to sustaining a demanding career—and why a headwind at home can be far harder to overcome than challenges at work. 25:00 – Staying rooted in Bangalore while building a global career Despite opportunities to relocate internationally, Mathangi made a conscious decision to keep Bangalore as her family’s home base. Instead of treating that as a limitation, she built her career around the constraint—travelling extensively while remaining rooted in one place. 27:00 – The story behind nearly 100 patents Mathangi credits the innovation ecosystem at [24]7.ai for creating the conditions in which patentable ideas could flourish. She discusses why innovation requires more than individual creativity—it also needs organisational encouragement, funding, legal support and processes. 28:20 – What makes an idea patent-worthy? Not every mathematically sophisticated model becomes a patent. Mathangi explains that patentable ideas need novelty, meaningful technological application and executability. She reflects on what she learned about innovation, prior art and protecting ideas during her time in an organisation that actively encouraged invention. 29:58 – The ecosystem that enables innovation Reflecting on Mathangi’s experience of filing nearly 100 patents, the conversation highlights how innovation is rarely an individual effort alone. A supportive organisational ecosystem—including encouragement from the team, financial resources, processes and institutional support—can make a significant difference in turning ideas into protected innovations. 31:15 – Career advice for young women: build grit and resilience Mathangi’s first piece of advice is particularly for young women entering the workforce: develop the grit not only to grow, but first to survive and persist. She speaks candidly about the headwinds women can encounter over the course of a career, including societal biases, workplace biases and even self-imposed pressures. 31:50 – Why biases can become more visible as women progress Mathangi reflects on something she wishes she had understood earlier: biases may become more pronounced as women move into more senior positions. Being aware of this possibility, she says, can help women build the resilience needed to navigate difficult moments without being completely shaken by them. 32:55 – “Resilience is the crux of a long career” Her advice is simple and powerful: don’t give up. For women in particular, where the headwinds may be stronger, resilience and the ability to keep going are fundamental to sustaining a long career. 33:10 – Advice for AI professionals: start with the problem, not the technology Whether the technology is AI, LLMs, data science, machine learning or whatever comes next, one thing remains constant: the problem. Mathangi urges technology professionals to develop genuine empathy for the person experiencing the problem they are trying to solve. 33:35 – Let the problem guide the solution Rather than starting with a technology and searching for a problem to fit it into, Mathangi advocates the reverse: understand and empathise with a meaningful problem, and then use your skills and technology to solve it. Finding the right problem to work on, she suggests, can shape as much as 90% of a career. 34:00 – Exposure, curiosity and finding problems worth solving Finding meaningful problems requires stepping outside one’s immediate world—speaking with people, seeking different perspectives and actively looking for opportunities to contribute. This mindset is valuable whether someone is just beginning their career or is already a senior executive. 34:25 – Ending where