Software People Stories

About Software People Stories

Stories of what worked and sometimes what did not, in the course of discovering, designing, developing and delivering software based solutions – as shared by practitioners who went through these situations.

Publisher

PM Power Consulting

Category

business · technology

Language

en

Explicit

No

First episode

5 Oct 2018

Latest episode

23 Oct 2026

Latest episodes

3 of 415 episodes have a transcript.

1. 

   23 Oct 2026

   From Creator to Reviewer, Human & good Humans with S. Srinivasan

   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 brain 02:32 Who's really at risk? Srini argues it's people with jobs today, not only the already underprivileged 05:10 What AI has changed most for Srini: planning with unstructured data, using three tools to curate, critique and refine 09:03 Aha moments: the AI that cited his own findings back as "anecdotal data," and the one that zipped past a checkpoint 11:25 What his generation contributed: helping people adapt, with lessons from railway and bank computerization 14:42 Learning from his son, nephews using AI on legacy code, and AI-set question papers 19:36 Text vs images vs video, and why he'd like everything back in a spreadsheet 25:46 Rapid fire: what AI has made more human, and what it has made less human 27:24 Five years out: a K-shaped world, two possible paths, and why AI plus robotics is his biggest concern 30:21 Why people are asking for face-to-face conversations again

2. 

   16 Oct 2026

   You cannot ignore AI. Contribute to it. And help others learn AI with Ramesh

   “AI gives you wings,” “Wrong things in, rogue things out,” and “Each one, teach one.” From “What is this?” to an indispensable companion In 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 solving One 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 partner Ramesh 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 AI A 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 AI Ramesh 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 caution When 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 Fire One 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 message Ramesh 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 (https://www.linkedin.com/in/ramesh-br-32a5111/?isSelfProfile=false)

3. 

   9 Oct 2026

   AI, Human Capability and the Future of Leadership with Zafar

   From democratizing intelligence to redefining how we lead, work and remain human In 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 future AI 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 relationship The 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 intelligence Zafar 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/ (https://www.linkedin.com/in/zafar-ahmad-4704b76/)

Inside a recent episode

From COBOL to AI with Allen Cooper

Published 9 May 2025 · Transcript excerpt

[…] I think it's just a matter of how how does it end up evolving? And I don't think developers are going to not be needed. I think developers are going to be used differently. I think that it's kind of like the going from like a slide ruler to a calculator to a computer. You know, you still need to understand a lot of things to use it and maximize it. And I think developers will probably leverage AI and to help do things faster. And it might even be better, you know, in terms of like what they start with. And they could probably learn in some ways they're going to help coach AI to produce a better product. […]

Who has been a guest on Software People Stories

Names identified in recent episode analyses. Showing up to five guests.

• Chris Miller
• Spart Parthasarathy
• Galvin Widjaja
• Tom Elliott
• Thanos Diacakis

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

YouTube views

358,439

Score snapshot 15 Sept 2025

Apple Podcasts (US)

5.0 / 5

2 ratings

Spotify

5.0 / 5

3 ratings

Podcast Authority Score: 70 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

72

Social presence

0

YouTube

86

Engagement

81

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