What's Up with Tech?

How TCS Turned A Company-Wide Hackathon Into An AI Engine

• Evan Kirstel

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What happens when a global tech leader turns its entire company into an AI lab? Our conversation with TCS’s CIO Janardhan Santhanam opens the door on a culture-first transformation powered by the world’s largest AI hackathon—half a million ideas, 55 million lines of code in days, and a wave of agentic apps built from no‑code to pro‑code. The result isn’t just energy; it’s a blueprint for scaling AI responsibly, measurably, and fast.

We break down the five pillars guiding TCS’s shift to an AI‑led services company: internal reimagination where every function becomes an AI practitioner; new client services from AIOps to AI‑driven development; talent acceleration through code assist, platform boot camps, and AI dojo programs; sector solutions spanning retail, supply chain, finance, and life sciences; and a robust partner ecosystem across hyperscalers and niche platforms. Along the way, we dig into what surprised the team—equal participation from HR and finance, agentic applications shipped by early‑career talent in hours, and the use of generative AI to evaluate submissions at unprecedented speed without losing human judgment.

We also tackle a big product question: are apps dying? Not quite. Routine tasks move to conversational agents, while apps double down on deep work—research, modeling, and analysis—exposing services that agents can orchestrate. To make any of this scale, governance comes first: security, IP, privacy, and legal built into the SDLC, plus a hard pivot from scattered POCs to a true path to production. And the scoreboard is business value, not hype: faster month‑end close, shorter deployment cycles for people, higher learning satisfaction, better margins, and happier customers.

If you’re mapping your own AI journey—how to democratize tools safely, design for outcomes, and keep talent growing—this conversation offers concrete steps you can apply now. Subscribe, share with a colleague who’s stuck in POC purgatory, and leave a review with the first process you’d reimagine with AI.

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SPEAKER_00:

Hey everyone, Evan here. Super excited for this chat today on how TCS is using AI at massive scale from the world's largest AI hackathon. Jenna, how are you?

SPEAKER_01:

All good, Evan. Thank you. And great talking to you and your listeners today.

SPEAKER_00:

Well, thanks for being here. Of course, everyone knows that TCS has a global tech and IT giant. But how do you describe the company these days and what's your role and your team doing within TCS?

SPEAKER_01:

So we are a company that now aspires to be the world's largest AI-led tech services company. And my role in this company is that of the CIO. And I lead our internal digital and AI transformation efforts. I am based out of Chennai in India, but of course we are global and I work across all our teams.

SPEAKER_00:

Amazing. Well, what a company these days, transforming, as we all are in the global enterprise race. Let's talk about the hackathon. What motivated TCS to run the world's largest AI hackathon? What's happening?

SPEAKER_01:

So the first thing is even if we have to take AI and work with our customers and reimagining their enterprise, then we have to be customer zero for that. We have to show TCS as the best example. What better way than to drive AI transformation within the company itself? And that is clearly not a tech transformation alone. It's not also just a business transformation, it's a culture transformation as well. So that being the first pillar of my entire agenda, we said we will drive a big culture change in the company. You start culture change by not just preaching to people, but first by democratizing access to something like AI in their hands. So you give people AI tools, and that's what we've done. Massive rollouts of AI models and availability of the popular open source as well as propriety models to everyone. Purpose-specific AI tools for people to do what they do in their everyday jobs and profession. Someone designs with AI, someone codes with AI, someone trains on AI. So there are various personas with different needs, and making access to AI tools available to them in a safe way has been the first big step. Now you give all them, all of them access, and then you want to make sure they are utilizing it and unleashing their creativity, driving innovation in their work and for the company. And that doesn't come naturally just by making access available. So we wanted to do something at such a large scale that really energizes the entire company to work with AI and everyone becoming an AI practitioner. And what better way to do that than drive a whole hackathon around this theme itself? Normally, we hackathons are not new to us. We've been doing it for a while, several, you know, more than a decade or so. But uh this being something that's was really important, strategic, and we wanted everyone to get on board quickly. We said we'll do something at a really massive scale. We were aiming to start with about 100,000 of our associates. That's about one-sixth of our workforce. We ended up engaging almost half of our workforce in the SAC, which was just the annual global format. Um, and we got about half a million submissions, and these submissions include things like code and architecture and a viewpoint on how the benefits would accrue out of implementing something like that. And they covered about 21 different themes, which are very relevant for AI in industry, AI in solutions, in technology, AI for cybersecurity, AI for data. So we picked a lot of such themes and then put it out, um, curated challenges for people to solve, allowed them to ideate and bring their own challenges, put up a whole series of platforms to enable it so people don't have to go search for where do I go build my idea out. And uh got very good results. The company is energized, and now many of them are needing access to it on an ongoing basis. And we so we're continuing the hackathon culture on a weekly basis now in what we call as AI Friday, uh, as labs across India. We've set up 16 such labs across India and expanding across uh multiple geographies as well now. So it's an ongoing thing, not a one-time activity as well.

SPEAKER_00:

Amazing. So many results and findings, I'm sure, but at a high level, what stood out the most from the results?

SPEAKER_01:

So, first, uh when you put a hackathon out like this, typically you would see uh the engineers, the technical people uh coming in first, and they becoming the huge majority. We saw equal representation from um from HR, from finance, procurement, sales leaders, consulting heads, because AI is now democratic and you don't need to know Python TensorFlow and all of these tools uh to program your way to build your resolution. So the first big uh exciting thing that we saw, we got as much proportion of women, of non-technical people, of people from overseas geographies, uh people from HR and so on, as we have in general as a population demographic within our company. That was the first big um uh it was an expectation, and very glad to see that it translated to reality. Uh the second thing is the kind of ideation that has happened has really uh triggered uh you know a lot of great ideas that have come through from uh how to make uh everything accessible in one's life, how to serve community, how to do automated credit risk scoring, uh, how to make uh visually impaired people see better with AI. And so there are tons of ideas for industry, for IT itself, uh for communities that people have really gone all out. Um and the third thing is talent discovery. In this kind of thing, you know, you get you get to see talent from places that you've never dreamt uh there would be people so enthusiastic about AI. Um there are people who have solved 49 challenges in in a matter of eight weeks uh and and cracked cracked all of them. Uh, you see people who have built agent dich applications in a matter of five hours, complete with all security guardrails and everything. Uh, and they are barely less than two years experience overall in IT. So great talent that gets discovered. And the best part about it is now we are starting to mine the solutions and and give use generative AI in order to do that too. We use generative AI in execution of this hackathon as well, um, which would otherwise have taken about 90 experts a whole year to uh to evaluate, was done in a matter of uh about 15 days with AI and humans working together. So these are some interesting things that came out uh through the course of the hackathon.

SPEAKER_00:

Amazing. And I'm reading here employees wrote over 54 million lines of code. Is that right? And what does that tell you about how fast Gen AI is being uh adopted?

SPEAKER_01:

So that was just one platform, one wipe coding platform. Um, and that was about 55 million lines of code in a matter of days. Um like I said, people come from different personas, right? There are people who want to write code, so we gave them wipe code and 54 million lines of code just on that. There are people who want to have nothing to do with coding, who want to build AI solutions just by no-code platforms. So we have a no-code platform called TCS AI Wisdom Next, and we gave democratic access to that, and they built both RAG kind of uh applications and agentic applications on that. Just that platform we consumed close to 5 billion tokens. Uh around 50,000 people built 80,000 solutions on that platform. There are pro coders who want to build the entire solution ground up and code their way to build agentic apps. We had enormous uptake of that. There are people who want to solve small timed challenges where the challenge is known, the solutions known, they just have to crack it like a game and move to the next level. So we had about 70,000 people engaged on it. So the enthusiasm was really widespread, and we were glad we were able to cater to these people who want to do different things with AI and with using different tools.

SPEAKER_00:

Fantastic. And of course, it's no secret that TCS's goal is to become the world's largest AI-led IT services company. How's that journey shaping up? And how is the hackathon kind of helping in this process towards that goal?

SPEAKER_01:

So the hackathon and broadly the AI-first culture and internal transformation of TCS is the first pillar towards going towards that goal. And the hackathon has really energized the entire company to uh rethink the way we work, uh, flip our own internal um processes around. So a large part of my work is also about working with RHR, our finance, our contracts, our sales in reimagining their work and rolling out solutions for that. So there is very high enthusiasm amongst people to transform their own functions with AI. So that's a first pillar of AI, first transformation in the company. Naturally, this is also spurring a lot of customer interest now. As they hear about what we are doing internally, they want to drive similar initiatives in their companies as well. So we've had lots of inquiries from customers on how we can replicate this for them. Uh, so taking a new definition of our services to our customers, reinventing those services, um, application development now has a new meaning. Support is now AI ops and cognitive support. Enterprise apps get rolled out and implemented in different ways. And driving this kind of AI-led innovation boot camps is the first way to drive uh a large transformation engagement. So services reimagination is the other piece that uh is getting inspired by this, and that's the second pillar. Of course, transforming our talent is the third pillar, and hackathon obviously plays a key role in uh in giving the kickstart for people to go deeper into AI and skill themselves. In the last one year, we've doubled the number of people who have uh got deeper skilled in AI and have at intermediate skills level and above. Then, of course, client-specific solutions for for an FS, for um uh for retail, for consumer goods, all of those are the fourth pillar. And the last, and very relevant for this hackathon as well, is the partner ecosystem that we work with. Creating a rich partner ecosystem and then having solutions that work across all hyperscalers, uh niche partners. Uh, and uh you know, the same thing with uh happened in our hackathon as well. Worked with multiple partners and then that set a good base on how we have to engage with them and go jointly to the market going forward.

SPEAKER_00:

Fantastic. And beyond the hackathon, how do you see AI upskilling, you know, and changing the way employees learn and grow just on a daily basis?

SPEAKER_01:

So the the the fact that now I have an AI companion, a couple that or an agent available to me 24 by 7 uh itself triggers the thought on how I could do my work differently. And we are prompting a lot of that by giving people many, many examples and use cases and highlighting good things that people have done. A classic example is code assist tools and uh AI in my own team. Um so we have given them tools to uh to do their coding with AI and their design with AI. And we we are prompting them to start thinking about different ways in which they could use it. And we have we have seen a huge explosion of uh um, you know, uh from one to two to then and now at least uh 27 to 30 different ways in which people have thought about leveraging these in different ways uh as part of their code and application development. Um then when they start exploring, they figure out that yes, they need more formal training on agents. They need understanding of um Microsoft's agent platform. So we then trigger boot camps for them to go through the formal training certification process, give them the environments there. And there is a mid and senior segment which typically has worked either on traditional technologies or on non-technical roles. For them, we have created learning programs that coax them into the world of technology and then reimagine their work, and we call those programs as AI dojo programs. Anywhere between 11 to 20 hours and uh virtual or in person, they come in, they immerse themselves on AI, sit with experts, get reverse mentored by the Gen Z, and that way they build their uh knowledge and skill thereafter. So these are various ways uh it's going well.

SPEAKER_00:

Amazing. Reverse mentored by the Gen Z. That's a great uh concept we can all think about. Uh there's also a lot of talk of a gentic AI, reducing the need for traditional apps, you know, SaaS. Um do you see the decline of apps or SaaS coming in the future?

SPEAKER_01:

I see uh apps getting reshaped. Um so a lot of uh typical work that people go to apps to do. I might want to apply for a loan, I might want to create a contract, draft document, or I might want to apply for leave, go travel. These are activities no longer require people to go to an application and then click through and log in and then do these things. These are conversationally possible with agents now, and we see a lot of that happening. So the work of apps now is to enable this in the back end by providing these services to agents instead of to people now. Uh so that is a recast. On the other hand, people go to apps in a more meaningful way to do deep work. When I am researching multiple facets of a problem, looking at different data points, understanding charts and comparing them across the screen, um, or doing what-if scenario modeling, uh, then apps still make sense and they make more sense now to do than apps. So apps are getting recast as deep work and uh to provide services to agents now.

SPEAKER_00:

Yeah, it's an amazing time. And uh we talked about scale at the beginning of the chat. Obviously, you know a thing or two about scaling uh at TCS, but what what are some of the biggest challenges that companies face when trying to scale AI across the organization, not just within a silo or particular use case?

SPEAKER_01:

So the industry is talking a lot about uh POCs and how POCs are not helping enterprise scale and the failure rate of POCs being quite high. Um and we've seen that too. Like the first big challenge is in how you approach AI. It is great to do POCs if if it's purely for experimental learning. But you can't think that I want to transform a finance function and I'll start with POC. Uh you start with two, three top reimagination programs and capabilities, and then you go all the way to put them to production. So from POC, you go to P2P, just path to production. So that that is a big recast that organizations have to do, and some of them are doing it very successfully. Our approach is also uh to change to do only areas where you want to experiment, explore something new, a new capability, you you go experiment. But otherwise, you put your mind on a few things rather than 100 POCs, and then you go scale. That's the first thing. The second is uh scaling AI in an enterprise requires responsible AI adoption right from the beginning of the entire program. There are many new elements on security, on legal, on intellectual property, on privacy when it comes to AI. You have to adhere to global regulations as well. That cannot be an afterthought after you've put things to production and then you uh start remediating the systems, or you hope that people will uh abide by the guidelines, those have to be built digitally into the way software gets built with AI now. So that takes time, that takes deep understanding of these uh these nuances of how security works with AI. Uh so more time spent upfront in creating the foundational responsible AI framework and uh embedding it into the development lifecycle, better the outcomes with respect to safe and secure applications and uh speed of innovation. These I would say are the two uh first two important things. And the third is obviously, like I said earlier, ensuring that there is a robust change management program to drive adoption, capture the results, amplify it, use it again to drive change. That's the third piece that um gets overlooked sometimes, and we continue to advise our customers and work with them on building enterprise change management as a very key component from the beginning of the programs as well.

SPEAKER_00:

Fantastic. And as you try to increase employee experience, customer experience across all these business functions, how do you think about measurability, measuring results uh over time? What are the benchmarks?

SPEAKER_01:

Yeah. So uh a very foundational for things which are very foundational in nature, like giving everybody access and exciting everyone, it is enough to just see whether it is getting used, all these tools, what is the usage, what is the active use, uh, what are people doing with it? Some basic metrics around usage are good enough there. But that's not what we are after, right? What we are after is um have we started improving growth? Have we achieved margin outcomes? Have we achieved customer satisfaction outcomes? So every one of these reimagination programs that uh we pick or we work with our customers for them to pick, we carve out those specific KPIs that are relevant for it. If there is a finance reimagination, we carve out how it is going to perhaps reduce the whole month end financial processing time from five days to five hours. Are we on that path? If we are going with an HR reimagination program or a learning reimagination program, we carve out KPIs to say how is AI going to help improve the deployability of a person or the mean time to deploy a person in between projects from let's say 10 days to two days. Um how is the learning satisfaction of an individual going to improve from 80% to 95%? So every one of these has specific outcomes tied to it. AI happens to be an enabler to achieve those outcomes, right? So the outcomes are the same as what you would do for a large uh program in general.

SPEAKER_00:

Fantastic. So what's next? Uh the world's biggest hackathon, hackathon Fridays. What what's on your radar as we head into next year?

SPEAKER_01:

So uh the biggest part of uh the hackathon work starts now, which is these half a million ideas, and then those are getting constantly generated, the solutions getting generated to plow them into the way we build our products, the way we uh drive our own internal transformation, take it into our projects that we are doing for our customers. We've got tons of ideas and solutions on retail, on supply chain, on logistics, on drug discovery, on clinical trials, and so on. So there is a big piece of work to take them and leverage them for growth for us and our customers. That's a big one. The second thing is um there are there are about five or six key uh um uh strategic programs that we have picked up, like EI4HR, uh reimagining finance, uh, reimagining our own internal IT, reimagining our whole user support within TCS. Running those uh is a key mandate, and we are working on that. Third is uh enrich and enhance our entire partner ecosystem significantly. We work we have great relationships with the hyperscalers and many other niche partners, but there's so much opportunity in working with them and their whole new products. Um, for instance, a couple of days back, one of the key hyperscalers has rolled out significant product updates, and I'm here in San Francisco going through those, right? Taking TCS as the first customer to experience them, learn about their implementations, and then be able to go advise our customers on what are the best ways along with our partner is the the third important thing. So programs around all of these three areas is what's going to keep me busy for the next year.

SPEAKER_00:

Busy indeed. Well, congratulations on all the success and then moving this exciting initiative forward.

SPEAKER_01:

Absolutely looking forward, even. And um, hopefully we'll connect soon and then exchange notes on how it's going in a few months.

SPEAKER_00:

I can't wait to see it. Thanks so much for joining. Thanks everyone for listening, watching, sharing the episode. And be sure to check out our TV show, Techimpact.tv, known Bloomberg TV and Fox Business. Thanks, Jana. Thanks everyone.

SPEAKER_01:

Have a good day.