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upGrad Enterprise aims to build the world’s largest GenAI learning initiative to enable high-growth companies to embrace technology’s transformative business impact. Hosted by Srikanth Iyengar, CEO, upGrad Enterprise, the GenAIrous Podcast, will curate an exciting roster of global experts and guests, who are at the cutting-edge of Generative AI, and its varied applications in the world of business.
Welcome to the GenAIrous Podcast where we unravel the fascinating world of generative AI and its transformative impact on business globally. I'm your host, Srikanth Iyengar, CEO of upGrad Enterprise. At upGrad Enterprise, we're building the world's largest Gen AI learning initiative, empowering high growth companies to leverage cutting edge technology. Each week, join me and the roster of global experts as we explore innovations shaping the world of work as we know it. Let's get GenAIrous.
Srikanth Iyengar, CEO upGrad Enterprise:Welcome to another episode of the GenAIrous Podcast. Super excited to have, two senior executives from a very exciting company, Photon. Please welcome Deepak Sharma, Chief Client Strategy Officer, and Amit Padalkar, who is SVP of Customer Success, who between them, you know, bring a very, very clear view of the intersection of business and technology across their clients and the transformation that they're driving. So, Amit and Deepak, welcome to the podcast, and thank you for your time today.
Amit Padalkar, Photon:Thank you for, having us, Srikanth. Great to be here.
Deepak Sharma, Photon:Same here.
Srikanth Iyengar, CEO upGrad Enterprise:Wonderful. Thank you both. I know you're joining from the sunny Bay Area. But, coming to Photon specifically, I know you're a global company. You operate across different continents. So maybe for our listeners, a quick background on Photon before we start talking about generative AI?
Deepak Sharma, Photon:Photon, if you were to think about it simply, is one of the world's largest digital platform engineering organizations. We are global, with over, you know, couple of 1000 people in the US and double the size of that in India and then spread across Europe and India as well. We started with the customer platforms, progressed on to data, cloud, a lot of martech, all the digital wallet, integrations, supply chain. And then finally, today, over the Shankar year and a half, Gen AI is kind of super, eclipsing our business and has kind of taken over. So that's been the history primarily in the financial banking, pharma, health care, consumer retail, and recently a lot more, integrated with multiple PE firms and the QSR, the restaurant space, which is growing like crazy for us.
Srikanth Iyengar, CEO upGrad Enterprise:Oh, fantastic. Deepak, thank you for that. And maybe, Amit I'll start with you because you obviously been at Photon for a short while, but you've also seen this from other viewpoints in the past with some global companies. And so, you know, what's your perspective, Smit? Given, business transformation, you know, obviously, everybody's talking about, Gen AI every company worth its salt. It's mentioned in annual reports. It's mentioned in earnings releases. It's mentioned in, investor, presentations. Some companies are declaring victory. Others are saying that they're being cautious. So clearly, there's a lot of discovery going on. What's what's your sense? What are you reading from the market?
Amit Padalkar, Photon:Yeah. Thanks for that question, Srikanth. Yeah. I feel like the reality of where things are are pretty much in the way in which you frame the question. There are some industries that have taken an early lead for obvious reasons, in the AI space, in the Gen AI space who've been experimenting with AI for a while.
Amit Padalkar, Photon:From a business perspective, some of these industries that we've seen let's say I've seen traction, you know... Just before my time at Photon, were where the data was not as much regulated. You know? We talk about things like manufacturing to a certain extent, a lot of the AI experiments, in the tech business as well, semiconductor, these kind of, industries. But more recently, a lot of accelerated AI experimentation has been going on in the more regulated industries as well, to Deepak's point. Photon itself, in my short time that I've been here, we have seen quite a few experiments, pilots, small sprints starting off in sectors like financial services for that matter.
Amit Padalkar, Photon:So that's been where things have kind of started off. The the mandates are coming from the board level, and it's very interesting that it's one of the Shantha initiatives, that has been very CEO led as opposed to just, in inside out, which is a transformation led by the IT organization with an intent of, for business to catch up. This has really been business being very impatient with the transformation objective that they're kind of trying to set out and putting a forcing function on the rest of the company for the tech to catch up to deliver that, that external outcome. So at a very high order bit, that's that's my perspective coming in on on this particular point.
Srikanth Iyengar, CEO upGrad Enterprise:No, Amit. Thanks for that. And Deepak, maybe requesting a different lens from you. Amit, give us a perspective of some industries that have probably started taking the lead. But given that Photon is a global company, do you see any differences between North America and Europe or any other geographies?
Deepak Sharma, Photon:A little bit, Srikanth. I'll just build on that. I would say that this is a wave that has been in the making for multiple decades. Right? If you think about it, you had the handcrafted models in the sixties seventies. Then you got Google search, computing large scale enabled. Then you got Alexa, which is Google search but on voice. Now we have Chat GPT which is I can interact with a human. So I think the fad part is a little bit less. AI is real. We have clients who are actually in production, who've gone from design to proof point to full production live in their systems in, like, 3 to 5 months, over big use cases. And this is one of those things which, yes, you will still see a little bit of, like, you know, the bubble will burst, but it's not gonna go back down. It's just gonna stabilize at the right point.
Deepak Sharma, Photon:Now to your question, Srikanth, in terms of geographical differences, I think I may be a little bit biased given our client base is remarkably US and UK EMEA driven, but we are seeing demand in Asia as well, right, come up. And what I would say is that if you think about the use cases, 50% of the use cases are kind of industry agnostic, and 50% are specific to the domain.
Deepak Sharma, Photon:So if I think of a support chatbot, AI chatbot, that's kind of industry agnostic. If I think of contact center transformation and disruption, to a large extent at the technology level and what has to be done to integrate the systems is kind of agnostic. The upper layer may be different based on the industry in the retail. The support works differently than in a b2b for example. So I would say there's a little bit more flavor by the vertical domain versus the regional domain, so to say.
Deepak Sharma, Photon:The one regional thing we are seeing is that you are consolidating things because you can run them remote, which was happening with cloud. Now with AI, I can even do the translation that way, so I can find my most optimized location strategy to make it work. So there's a little bit of shift regionally because of that.
Srikanth Iyengar, CEO upGrad Enterprise:No. I think that that makes a lot of sense. And that demand in Asia is very interesting because, obviously, you know, we we have learners from across the globe, and we see demand across the globe, especially in India and Southeast Asia because we are very strong there as a brand. And I guess it's representative of the fact that, you know, two of the world's largest, economies and high growth economies are based in that part of the world, and that would drive adoption at scale. And, you know, very, very clear. That helps.
Srikanth Iyengar, CEO upGrad Enterprise:I wanna touch on something else you brought up, Deepak, as a follow-up. You talked about different parts of the value chain. You talked about customer success or customer contact. Obviously, conversational AI has been around for a while, and there are new generations coming up. You know, obviously, Amit earlier talked about manufacturing and supply chain. So I wanna drill a bit down on enterprise productivity. The front end, the customer experience is clearly an area for disruption. But what do you see happening with enterprise productivity?
Deepak Sharma, Photon:Yeah. I would break it down. The way we are seeing, Srikanth, is there are 3 big patterns independent of the industry in any enterprise. It's with AI, Gen AI, I can run a better tech shop. Think of that as copilots making productivity in the SDLC process, code generation, testing, automation. The next is I can run a better business. This is really attacking all of those traditional processes that were very document heavy, which were very manual, like legal, HR, contract management that were not really looked at before. They are getting disrupted with AI, Gen AI very rapidly. And then the Shankar one is which has always been there, which is customer engagement, experience, revenue. So those 3 are there.
Deepak Sharma, Photon:If you do those 3, there's a 4th benefit you get with it, which is your architecture modernizes. In order to implement those, whether you like it or not, you basically end up modernizing your architecture. So even in contact center, they're creating copilots for the agent. That'll enable the agent to talk to the customer better, but they're still not fully ready to automatically float everything out to the customer. Because there's this thing of hallucinations, ethical explainability, traceability.
Deepak Sharma, Photon:So those challenges still have to be solved. However, on the run of better tech shop, the use cases are actually getting there into production. We have clients where their CSO has approved using GitHub for some common code development, therefore, increasing the productivity of the developers. We are doing software testing automation for some key clients We're we're gonna bring that down by, like or increase the cycle time by 40%, right, 40% faster by doing it. Then the question becomes with the capacity I free up, do I go do more value add things, or do I do some traditional cost optimization things? And the choice is both ways.
Srikanth Iyengar, CEO upGrad Enterprise:Absolutely. And I think, Amit that's a great segue to just, go to a different question that I think you might have a perspective on. Clearly, most clients have had pressure, like you said, from boards. CIOs have had, you know, show results. And the common metric is how many ports are you running? How many POCs are you running? And that's been the metric probably for the Shankar 12 months. But, obviously, that metric at some point is gonna run out of runway. So what do you think the next few metrics for progress will be across enterprises? Any perspectives there?
Amit Padalkar, Photon:Yeah. That's a great point, Srikanth. You're absolutely correct that, the small experimentation models that you were talking about, pods or pilots or or boot camps, any such, initial experiments with were what boards and CEOs started with. But now I feel like that a lot of these, folks, because of shareholder pressure, because of, pressure of the cost of running many of these AI models, they're beginning to come to a point where they want to start moving an operational metric.
Amit Padalkar, Photon:A case in point is, for example, from my personal experience, just just before Photon of engaging with the InsurTech or the insurance industry, smaller insurers, especially, their ability to service, you know, catastrophic claims like the Baltimore bridge collapse or or some of these multiparty claims, complex claims that run across states, the CEOs are under more pressure than ever to Shantha a, improve the productivity adjusters to get these claims out the door as quickly as possible for the lowest cost as much as possible, for the least amount of liability as much as possible. Now in the past, they would have looked for armies of people at a certain cost model to run run this profitably and and do it in a way in which their exposure is restricted. Now with the, introduction of AI, all of a sudden, the pressure is to for these CEOs to deliver that same outcome and, you know, make the make sure the business is going growing profitably, but do do it in a way that's fast, that's accurate, that's cheap with human in the loop, and does not get the company in trouble.
Amit Padalkar, Photon:So there is a lot of that pressure, that we are seeing, you know, at least I'm seeing in the market, that is bound to make its way into the kind of, kind of programs that Deepak is talking about and how the services companies or the solution providers or the product companies will have to actually respond to that to that reality. And this is not just across any industry. You know, I brought up an insurtech example. I'm seeing this in in manufacturing. I'm seeing this in supply chain. You know, I feel like, there is a lot of chatter in the market as well about the cost of running these models. You you there's a very interesting anecdote that, 50,000,000,000 was spent on AI silicon with just 3,000,000,000 in revenue generated from it. So there's a massive disparity, of of, ROI there.
Amit Padalkar, Photon:That pressure is beginning to tell.
Srikanth Iyengar, CEO upGrad Enterprise:Well, a lot of the AI, you know, inflection is happening starting in the Bay Area. So I guess that VC mindset still stays that you pump 50,000,000,000 in for 3,000,000,000 of revenue. But, I say that tongue in cheek. Absolutely, we need the progress. But, Amit, that's a great example, and that brings me to my next point. You know, clearly, if you take your InsurTech example or insurance industry, like you said, catastrophe models or cat models are super complex, new variables all the time. You get public feeds. You've got experience. You've got past claims. You've gotta be all of that.
Srikanth Iyengar, CEO upGrad Enterprise:And then I can understand how, Gen AI can ease that process to get a more accurate quote faster to the client and assess risk if an incident, unfortunately, does happen like the Baltimore Bridge collapse. But, clearly, you would have needed armies of underwriters to look at that, adjudicators to look at that in the past. Now models can do it. That means that the skills that were built over years, which is it's experience based, change. So how do you see that impacting it? And that's a trend that probably goes across industries. Job roles change. People need to rescale themselves. How are you seeing that panning out? Because this could have large scale impact on the workforce across industries.
Amit Padalkar, Photon:Oh, yeah. Absolutely. See, my perspective, again, business side, coming in is really there are 2 big functions that are getting impacted as a result of this, skill change imperative. A lot of these decisions are being made by these councils as as, Deepak can also echo, because, what the CEOs have realized and the boards have realized that this the the way to kind of, peel the onion here is to bring in these different perspectives from tech leaders in the company, from the business folks, let's say, the the InsurTech example I talked about, as well as the CIO organization itself that has been traditionally the custodian of the data, traditionally the custodian of the applications going out. One of the things, I personally did here, you know, before joining Photon is, the ability for the CIO or, for instance, to reskill its people on the generative AI products and tools that the big hyperscalers are are, releasing out to them.
Amit Padalkar, Photon:They are being, of course, in deep conversations with many of these hyperscalers. So that was one angle that kind of did approach it. And the other angle, of course, was, the constant pressure for them to not just be a cost center because somehow they are now being included in the conversations to be a to be a solution and not just a part of the problem. So one of the things I did hear from some a CIO in this particular example was how with any initiative he launches with Gen AI, how can that actually be monetized for the company? That was that very direct ask from the CEO that came into him. So this is, again, a business perspective outside, and I'm sure Deepak would have some very specific points on the roles and skills and what he's seeing.
Deepak Sharma, Photon:Yeah. I mean, let me, Srikanth and Amit just take a small step back. I'll zoom in, and then I'll zoom out, on the roles. So where, as I was saying before, where the AI is starting is you're deep in the process. So when you're, like, thinking about running a better business or running a better tech shop, you're able to get deep in the process, and you're able to connect the process into the P&L statement of the company. So the tie of what outcome I will get, how often will I get it, how long will it take me for first time to value is very clear.
Deepak Sharma, Photon:For example, we we worked with an organization that's in the aviation space. They're using it in their contact center. At the end of 5 months, their agents were taking 60 to 90 minutes to respond to an email. Now they're taking 15 minutes because all they're doing is reading the email.
Deepak Sharma, Photon:So I think if you think of it that way, the scorecard that you follow and kind of that's why we've got customer success at Photon, a unique model to kind of drive that with clients is working very well. You're able to tie the business value into the P&L. The second is, as Amit was saying, the mandate is coming from the board. So every time you're getting engaged in a POC or a pilot, you're actually creating a larger business case, which the board first approves in order to get the journey started. And the third thing I would say is that since you have a dynamic scorecard or a progressive scorecard, basically, people are running it in terms of Gen AI sprints.
Deepak Sharma, Photon:So instead of, like, you know, software sprint is 2 weeks, a Gen AI sprint could be 8 weeks or 12 weeks, every 12 weeks, I look at what I've achieved. I compare it against to the targets I had, and then I decide what to do next in the next 12 weeks. And I have to sometimes go left, sometimes go right based on how fast the industry is moving and how fast my own organization is moving. The multidisciplinary team comes in this execution, which is this is not just about IT running it or business running it. You need IT, business, product, legal, HR. That's why you have those AI councils or AI governance bodies or AI nerve center, whatever you can call them in the organization. The trick there is to make it robust enough, but not make it like a bureaucracy that kind of slows you down. So what we are seeing now is, like, hey. Your AI council has become, like, 25 people. That may be more like a quarterly update, but the AI council to progress, that should be the key 5 or 7 disciplines you need to get this thing going, if that helps.
Srikanth Iyengar, CEO upGrad Enterprise:No. I think, very, very interesting perspectives both from a business functional tech standpoint. Just to follow-up, Deepak, for you is you talked about the AI accounts, and you're right. We see that with our clients as well. And especially connecting it to what Amit said about some of these initiatives becoming revenue driving as opposed to being just productivity. That brings up a whole host of questions because often as companies try to monetize data, there are concerns around privacy, security, you know, whose data is it, ownership. So, obviously, the council helps, let's say, understand or regulate some of that. But at the same time, it slows companies down. So there's probably a balance between speed of execution. There is a bit of form out there in the market.
Srikanth Iyengar, CEO upGrad Enterprise:At the same time, there needs to be a corporate governance structure to ensure that risks are perceived, and you take a calculated risk. So how do you see that balance playing out in these conversations?
Deepak Sharma, Photon:I think it's still playing out, Shrikant, frankly. I don't think it's solved. A big question in the c suite still is around the liability, around the explainability, the traceability. That's why we are seeing use cases that are more internal and enabling external getting prioritized over the ones that are going straight out to external. For example, you have generative engine optimization coming in as a way to replace SEO.
Deepak Sharma, Photon:It's there, but it's not fully there. Then you look at a chatbot or you look at a contract management analysis. That is moving to production much faster. So contract management is all internal, but it's a high cost, enhancement that enables my p&l immediately. And it enables revenue because, you know, if you look at a large organization, a large bank, for example, they have thousands of contracts.
Deepak Sharma, Photon:They don't know if they're overusing, underusing, What's the risk profile? Other ones coming in, is it similar risk as before? In order to do that today, it takes them, like, weeks. And sometimes they have to call people out of retirement who had signed the contract. Right? The person is no longer there. If you put all that into an LLM, if you have a model that I can query, if you have the right prompts defined, then it's like a matter of minutes for the executive to know how the contract management landscape is for him.
Srikanth Iyengar, CEO upGrad Enterprise:This complexity that boards and CXOs are dealing with is completely new to them in many ways. Right? They've dealt with other challenges before. But here, one needs an understanding of the business. One also needs an understanding of the technology implications. So how do you see boards and CXOs upskilling themselves? We talked about skilling the larger corporation, which obviously is a an imperative.
Deepak Sharma, Photon:So maybe I'll go first, and, Amit, you can add. I'll I'll give you more of a Photon lens of what we are seeing and what's working. So I would say there are 4 steps that have to happen that we are seeing happen. There is an education and awareness at that level of what this really means, and there are tricks to show that whether it's workshops, whether it's demos, but the the crux of the matter is helping people understand that the the the ugly truth about AI is that it needs data to work properly. And that is complex.
Deepak Sharma, Photon:The cleanup of the data, the preparing of the data, the data modeling, tweaking the models, choosing the right, you know, the LLM is kind of decided there are 3 or 4 that are there. You're gonna choose one of them. Then understanding how do I build an architecture that'll connect with the tech stack I already have, and I know that that tech stack will get AI infused along the way. So I need something that eventually I can, you know, move things around as needed. So there is that education and the understanding that data complexity, you cannot ignore.
Deepak Sharma, Photon:You need don't need MDM, master data, but you need some cleanup of data. The second is the discussions have completely shifted, Srikanth, to being business outcome oriented. That makes it easier for that audience. So I don't have to talk about data architectures and things like that, but I talk about the business outcome. I talk about the new process.
Deepak Sharma, Photon:I talk about how work will change between Friday evening and Monday morning, right, with the new process and what that means. So that makes it a little bit easier because you are not talking about a business outcome and a business process that anybody in the organization can wrap their arms around. And then the last thing which gives, a little bit of confidence is the defined scorecard, which basically boil it down to 3 things. There's metrics of what you are doing. Like, I'm gonna improve contact center resolution time, or I'm going to, do deflection into online from human call to this percentage.
Deepak Sharma, Photon:Then there is the movement part of it or momentum part of it, I would say. Momentum is, are people actually adopting and using the tools the right way? So for a lot of the clients, there's a Gen AI University effort that's going on, which is how do I train people on how to use this? Like, one of our global CIOs said, hey. For plant engineering, I don't need people who can write code. I need people who have human psychology understanding and have good English in the US. Right? Eventually, it'll go to other languages. So that's the second thing is, like, how are they doing that? And then the third is the movement in the market to my business outcome, my p&l
Deepak Sharma, Photon:Right? So is my stock aligned to this? Is it moving up? Is my cost aligned to it? Do we have a higher customer acquisition? Has my digital sales gone up? So those are all clear metrics that I can track on a monthly, quarterly basis and see movement. So that's how we are kind of seeing those things come together.
Srikanth Iyengar, CEO upGrad Enterprise:But let me let me, sort of flip it around a bit, Deepak, because you obviously talk to your clients all the time. Photon, global companies, fast growing company. But how is all this changing the criteria you have for the people you hire? Have things changed or have they remained the same?
Deepak Sharma, Photon:No. Actually, they are changing in a massive way. So I'll just give one fact just to put it in perspective. We've been on this journey for about a year and a half and 2 years. While last year, everyone knows the market was tough and the revenue growth was not there, Our margin growth has been pretty fantastic. And the reason for that is not just the huge, you know, cost management of the organization by the leadership, but the fact that we had started this AI journey. So we've automated the recruiting. We've automated, testing. We've automated code development internally ourselves. We've automated using all the copilots that are out there even on the business side, and we are seeing improvement in that.
Deepak Sharma, Photon:In terms of the skill set and up skilling, we actually have our own Gen AI Academy and master class we are running for the 7,000 plus people across the globe. And the beauty of it is, Srikanth, what we are running and building and building out, call it the AI workbench or AI hub, we can then turn it into a commercialization model and take that as an accelerator to our clients. So that's kind of how that model is constantly evolving and working for us. And it's changing the skill sets people need. So prompt engineering, for example.
Deepak Sharma, Photon:Even in the business, I'll give a very small example. All of us are using, you know, Microsoft Copilot, for example. And it produces good summary of the meeting. But if during the meeting, you give it a verbal cue, you say, hey. So these are the decisions.
Deepak Sharma, Photon:Hey. That's a great action item. It is spot on when it comes back to you. It increases productivity so much, and that's a training. Right? Changing how you do the process in a slightly tweaked manner, and suddenly the output is far better.
Srikanth Iyengar, CEO upGrad Enterprise:Those are great insights, Deepak. But but, just that that brings me to my next point on collaboration. Right? Clearly, as Gen AI disrupts the business landscape, we are seeing different kinds of partners collaborate, whether it be the LLM providers, whether it be the, you know, the hyperscalers, whether it be the consulting and tech services firms. But, you know, and then, obviously, you guys are also in the middle of that. So how do you see the ecosystem reconfiguring itself in terms of partnerships and collaboration?
Amit Padalkar, Photon:You know, it is really devolving into, 2 to 3 horse race, Srikanth, to the hyperscalers playing a massive role, increasingly dominating the role in this market. They are able to bring the cost of deploying AI enterprises, hopefully down rapidly. And what they're trying to do is to seed a lot of these experiments, pilots, co pilots that Deepak talked about, through the various market mechanisms, be it the, $100,000,000,000 Cloud AI deal you may have heard of that Azure gave OpenAI. So there are baby versions of that, that are, in the market today in a very aggressive manner. For example, GCP, which had been a bit of a laggard in the market, is taking a a much more forward thinking role a forward looking role, trying to seed the Gemini into every program that is there.
Amit Padalkar, Photon:And they're releasing a lot of, funding into the market to actually kick start a lot of these, programs, similarly with Azure and to a certain smaller degree with AWS. But I think from a partnership perspective, certainly, the needle is moving very, very rapidly in the direction of of the of the big hyperscalers. At least that's at a at a very high level, that's what's happening.
Deepak Sharma, Photon:Yeah. I'll just build on that, Srikanth. I would say that the LLM war is over. Right? They're 3, 4, like I said.
Deepak Sharma, Photon:So everybody will choose building your own LLM from scratch. There has to be, like, a super, super reason. Like, you're deep in defense or you're doing something so secretive or so this thing that you just can't afford it, but it's like a big expenditure to create your own. So I think and it requires time. Right?
Deepak Sharma, Photon:It requires 5, 7 years to kind of create that kind of thing. I think there'll be 3 things that will drive. It's like companies will have to use differentiated methods in order to maintain their brand value. They'll have to have pre contextualized models. This is where the ecosystem to your point is changing. Do I build myself? Do I work with digital partners? Do I work with my existing SI partners? Do I do something different? Can I just use it from the AI infused in the software packages that I'm buying today?
Deepak Sharma, Photon:So the pre contextualized models, the training of it, the upgrading of it, that will be one. And then the last thing is how do I create my whole tech stack again and reimagine in a way with the modern architecture that it is future proofed? Future proof mean, like, the models are gonna become better. The software is gonna become better. You look at image creation. A year ago, it was frankly crap. Today, it's really good. At the consumer level, it's working, and the use cases are changing. So I have to evolve for that. So those are the 3 that will come.
Deepak Sharma, Photon:You'll choose the LLM layer for sure. Below that, all the data work, that can be with a partner or yourself depending upon the organization. Then above that, you have your integration architecture, which will be a mix. And then you'll have 2 kinds of software folks. 1 is your traditional CRM, ERP, and those, and they're going to infuse AI. And you'll figure out how much of that to use. And on top of it, do I need, you know, like a Photon or something to help me or not? The other is there'll be specialized software. Specialized for, like, a custom use case for, let's say, contract management for a industry or something around chatbots or something else.
Srikanth Iyengar, CEO upGrad Enterprise:No I mean, Deepak, that really helps because clearly, as we said at the start, the space evolves. Still lots of questions for, you know, entities or companies to resolve by the LMM war maybe largely over many different other decisions to make. Clearly, lots of value to be created here even for consulting companies, for transformation companies like yourselves and for us from a skilling perspective. So very, very exciting space to be in.
Srikanth Iyengar, CEO upGrad Enterprise:I just wanna say thank you both. This has been a lot of fun. Good questions to answer, but lots of blue water ahead of us. So much appreciated.
Deepak Sharma, Photon:No. Thank you very much. Really appreciate the time and the discussion. It's a great way to start the day.
Srikanth Iyengar, CEO upGrad Enterprise:And that concludes another episode of the GenAIrous Podcast. We are very grateful to our guests for their time and expertise. A big thank you to our producer, Shantha Shankar in Delhi, and our audio engineer, Nitin Shams in Berlin, for making magic happen behind the scenes. Join us next time, and don't forget to subscribe to GenAIrous wherever you listen to your podcasts.