 everyone and welcome back to theCUBE's live coverage of Google Cloud Next here in beautiful Las Vegas, Nevada. I'm your host, Rebecca Knight, along with my co-host, John Furrier, also Rob Stretche and Savannah Peterson on with us. This has just been a great conference. We're nearing the end of day two and one of the things that is just so striking are these partnerships and these strategic alliances that Google Cloud. Yeah, HCL tech's coming on now. We're going to have a great conversation. And what's really exciting is that they're at the forefront of accelerating solutions and they're unfolding right in front of us. Also, one of the guests has been a CUBE alumni going back to 2011. Yes. Okay. Oh gee. Let's introduce her. Okay, let's go. Go ahead. Siky Guinta, she is the executive vice president, CloudSmart, industry cloud consulting, Google ecosystem unit at HCL tech. Thank you so much for coming on the show. Thank you. And Alan Flower is the EVP and head of cloud native and AI labs at HCL tech. Thank you both. Lovely to join you today. And Siky, again, distinguished CUBE alumni who remembers when it all got started. Yeah, I remember it was tiny. It was a laptop and a camera and now look at the spread of the production. It's fun. I remember we were actually talking about cloud at that time and we were actually talking about some of the same things now, but it's gotten so big. It's now the business model. And I think the AI wave is here for sure. We just had a previous guest on from Google who runs all the container stuff. Ten years of Kubernetes, okay? So, all right, things are moving along. Now, the biggest problem everyone's trying to solve is what's my solution look like? How does Gemini and Google's case AI impact? How do I infuse that into it? And how can I get it deployed? As with use cases that are identified, then there's the, what does it mean in the future? So, help us understand where your customers are at right now because as vendors bring stuff to the market, we see a lot of announcements here, customers are in a different spot. But she's in the enterprise. Yes, we have a great solution with the cloud native lab. We just funnel all our requests to one spot and help us really understand the magic use cases. And now we have a year of experience and Alan will give you a lot of really good insight. But we, I think, have decided that the upper layer of AI, the industry specific, is the one that is going to impact the business the most. There are some horizontal use cases we are, you know, taking to market. The last thing is we are partnered with Google on Gemini. And in the floor we got, you know, manufacturing solutions. We look at telecom and life science. These are the areas where we have got clients that went to see Alan and they want to do it and now we are ready to just prototype and industrialize. Alan, you got the keys to the kingdom. You got the AI labs, all the actions coming to you. What are you seeing? Tell us some stories, give us the update. Well, look, it's quite remarkable, isn't it really? I think in the space of barely a year, we've seen our clients go from kind of tepid, kind of exploration, maybe looking at some horizontal use cases, but then rapidly gaining confidence. Maybe they've deployed Gen AI into the software engineering domain, for example. They rapidly get confidence. Then they move on to how is this going to transform my business? So as Sicki says, what we've seen in barely a year is these clients now expecting to do significant business transformation enabled by Gen AI. Is there ever any overconfidence? I mean, as you said, they've seen some immediate benefits which have inspired them to do more. But is there ever a time when it's... It's a good question, isn't it? Because we often joke that ChatGPT, best sales tool the industry has ever developed, right? And it does give you a lot of confidence, most of which is justified, all right? So it's done a very good job. A lot of enthusiasm, for sure. A huge amount of enthusiasm. Double the enthusiasm. A huge amount of enthusiasm. And so the remarkable thing is, right, we've done well over a thousand workshops with clients now around the globe. So we think we've got pretty good insight into what those use cases are. Fundamentally, it's the business leadership that are driving Gen AI. It's not the technology estate. Business leaders very rapidly saw the business potential. Fundamentally, they're thinking about improvements in productivity, quality, velocity, right? That brings a significant impact to their business. Hence, they're coming to us. They know the use cases. They think they know where they want to deploy Gen AI. They need a partner like HCL Tech to accelerate the journey and build the solution. You know, that reminds me of that famous Steve Jobs video where he says, it's not about the technology trying to find a solution. You got to work backwards. And I think the recognition that we're seeing is similar is that people see the bridge to the future, but they got to build it. That's like it's not built yet. I'm building on Rebekah. It's a double-edged sword because sometimes people are very confident and then the build model and these are hallucinating to them straight away. And the problem is that they retrench very fast and they lose credibility with the business. The other big area, especially for instance in financial services, they need to have things like 99% governance and security in the right country. So there is a little bit of a citation on things that impact private information, money and everything to go, but anything that is process acceleration like I have to do a loan applications, that things will go fast. I give my advice is just understand what the business need if I talk to IT and I've talked to business, say just temper your enthusiasm. This is not a magic wonder. Things happen and the data is not there. Not all the data is there and that's a big challenge. So Alan, you've got all the use case, thousands of workshops. Okay, business leaders see it. We got to get to the future. The waves here, it's a no-brainer. Kind of like the web, it's coming. We got to get there, start tooling up. What happens next? What's the progression? And what are some of the prerequisites that need to be in place to go to the next level? Take us through a little bit of a journey back there. It's a really good question, right? So we've seen this rapid acceleration of client journeys. They've gone from, we've got a good idea. Let's build a POC. Let's build a minimal viable product to don't switch it off, all right? We see value. Because they like it and it's working. Yeah, we've got plenty of examples where we've built, let's call them POCs for clients. They asked to experiment maybe for a few weeks. Turns out the whole workforce starts using. And then two weeks later the client is saying, don't switch it off, we've got value already. But the challenge for the industry, quite frankly, is clients are now moving so rapidly, is the industry ready to take these solutions into scaled production? All sorts of challenges. A very good example, of course, is in traditional IT, the way we operate solutions tends to be quite static. We sign these things called SLAs, that defines our behavior for the next five years, for example. With Gen AI flavored solutions, it's an organic infrastructure. Someone has to be looking at the accuracy of the solution, the underlying models. Who's going to decide to replace this model with a better one tomorrow? Who's going to make sure that we keep control of the cost of executing some of these models as well? So it's become a very complex landscape as we now contemplate what production looks like. It's like the nexus of FinOps meets system engineering, DevOps, SecOps, and then IT. Kind of all rolled into one. This magic person does not exist, it does not. So you need to have a very coordinated type of skills, skills that can talk to the business, and we are all chasing the same skills. The cloud provided, our partners, us, and I think the things that all my clients says, scale means around the globe, is who's going to support it? And how can the business learn how to use it and be confident in there? And I know that everybody thinks that JNAI is going to take the job of everybody. I say, no, we are going to move up the ladder and change our skills to be there, because as automation and JNAI is going to make the business better. It's just we have to change the workforce and we have to have a extremely good governance, I think. So going back to that, it really will take entirely different kinds of skills and entirely different kinds of analytical thinking. We've talked a lot on this show at Google Cloud Next about how we're going to see this explosion in creativity because we've reduced the toil and automating so many tasks. But you also brought up an excellent point, Alan, which is, is the industry ready for how fast clients want to move? So how are you walking through your customers through these very difficult questions? I take the view that all of us, it doesn't matter which business we work within, there's a growing expectation from clients, stakeholders, peers, managers, leaders, that we now all start to leverage and take advantage of this productivity gain. It applies to us just like any other business, of course. Now a very good example of where we're taking advantage of that is we've recently launched something called AI Force. AI Force is really HCL tech taking the lead, taking the industry to the next stage of application of JNAI. We're all very comfortable, for example, with the use of JNAI in the software development realm. For many people, the journey starts there, doesn't it? You download a co-pilot. Well, our clients expect us to go well beyond that. We're expected now to use JNAI to really automate and enhance the entire software development life cycle. So you could look at AI Force, for example, as a change of posture to an AI-led delivery model. Is AI Force a platform or a methodology? Can you explain what it is? It is an IP that we use to deliver our transformation, code transformation for clients. And it's a little bit of an end-to-end de-generate code, depending on which model. So it's technology too. Yes, but AI is the technology. Yeah, to do that. Then you test it. There is a good element of security because when you auto-generate the code, you could insert a lot of security liability in it. So we have that. We have a cost model in there. It's used in conjunction with our clients. Which is how you guys execute your delivery. Yes, absolutely. And the clients too. And how do they get involved? Is it part of every engagement or does it have a certain pipeline for criteria? We've already started to deploy it. Okay, so clients are already seeing very significant gains. We ran a session here today, for example, where we actually shared some of the productivity gains. And on some of our projects, we've recorded in excess of 60, 70% productivity acceleration through the use of AI force. So we've already convinced ourselves, right? We already have clients using this. But don't forget, of course, that HCL tech has been building AI-enabled technologies and solutions for over a decade, right? We are the industry's, of course, preferred engineering partners. So this is a landscape that we know particularly well. So we've just taken the opportunity not just to consume co-pilots off the shelf. Anyone can do that. But we've built this entire platform and framework to ensure that we can deliver engineering projects now with greater productivity, better quality. And of course, we can take features to market for our clients far quicker than ever before. Sikker, you do the ecosystem side of HCL tech as well. With Google, they're very open. We have great stuff, but also we work with other environments. The cloud's edge is huge. We mentioned that briefly. As multi-vendors come into the play here, that becomes a big part of maybe AI force and the tooling. What's the ecosystem involvement? What is there, do you just join? How do you, is it part of the process? I think, you know, Alan and I, we know there is a multi-cloud, private and... Ultimodal, multi-cloud. It's a world that is going to be like that. At the moment, the more disciplined clients pick a platform of choice and they develop models on that. But I can tell you there are models where chat is better than Gemini and that Gemini is strong. It's like, you know, the beginning of cloud when your head is going to be that way. And I find that the ecosystem partner, they're all picking their own lanes. And for instance, in the case of Google, everything that we get in Gemini 5 is something, 1.5. They've been used internally at Google for a long time and now they put it on their strength is search. And obviously Microsoft has a different value proposition. It's more, you know, users friendly and things like that. Our job is to navigate the clients to get the best... And you got the industries, you got all the interactions and engagements. We've been here and love to get your thoughts when you hear the same thing or react to it. We're hearing here on theCUBE and our other events, optionality is job one in this new design because they saw the cloud game. If they went, if they over-rotated it into a cloud, even outside of the core, compute stuff, the multi-high-level services, if they didn't actually need them in AI, stay with the more optional approach. Do you hear the same thing? What's your reaction? Fundamentally, AI is a hybrid multi-cloud journey. You've heard that before, right? It's exactly the same. Clients expect to be able to run their AI workloads wherever they need to run. And don't forget, AI moves to the data, not the other way around. So we see interest in on-prem deployments. We see interest in multi-cloud deployments. And some of the announcements, of course, that we heard yesterday play really strongly to this. For example, Google distributed cloud, that ability to run Gemini in my data center aligns perfectly to what clients expect. Yeah, and then they got the new project with 130 miles in vertex is interesting too. And you got Gemini 1.5 is looking really strong. I have to tell you the story. Last year, and sometimes I'm baffled about my Googler and I tell them why you make this so complicated. And I tell TK, I was in line to do my hackathon on vertex and bar at the time to add. It took me long to line up, then actually build the entire system. And it's crazy. It is actually very easy. The problem that we in the tech world, we're actually making it more complicated. And everybody thinks that AI is just Google and Microsoft. Oh, Adobe does a great job. For anything that is taxed and then SAP has a lot of AI in their system. So service now for after. So again, it has to be a multi-environment because the business has a multi-purpose. And it's going to be simple too. I mean, it's like the old enterprise show. How do enterprise solutions solve problems by making it more complex? Yes, I think it's a remarkable thing, right? Because we've moved on so much in the last year or so that we've got this point where gen AI is fundamentally consumable but it's remarkably easy to build solutions on it. As a general guide, I mean, we often struggle to spend more than four or five weeks building a complete solution. A really good example of one we did here in the States with a healthcare provider is we now have a gen AI enabled clinical advisor. If you're a medical professional, you're struggling to spend enough quality time with your patients, we've now enabled gen AI to give guidance to clinicians, treatment plans, other advantages. And this is a really good example where we leverage a technology like Gemini. We enhance it with more accurate data from the client's estate. We blend those together into a specialized solution. And if you think about some of the inherent advantages that gen AI offers from a conversational perspective, our clinical advisor, you can ask it to perform the role of a cardiologist. You can ask it to perform the role of a diabetes specialist. It's the same application. Right, whereas medicine is so siloed in real life that it's taking away those things. I'm curious about how your relationship with clients has changed now that as you were talking about earlier, you're finding all these new ways for them to be more productive. Are you helping them and advising them on, oh, you could make this a new source of revenue. This could be a new business model for you. Are you advising them in those ways? And absolutely. And at HCL Tech, we'll be vertical align and domain because our engineering, if you build large scale system for a client, you need to know that client. But the other interesting thing is the clients themselves are now realizing that they have this amazing value that is their data. And then they're starting up to understand, oh, if I use it there, I can win on a competitive venture. Oh, if I use it there, I can enlarge my marketing. And it's phenomenal to us to see how fast they get on understanding what's the potential. Our, as well in our strategy is though we have a very conscious charter to create ethical AI and to be very mindful of the creation of IPs and to be very mindful that we are dealing with data of clients and this is, we all have to govern well. And the European community is put out in very strong directions and everything. So we do global clients, you have to understand how to deal with the data. It's complex. Well also you mentioned the value, the time to value, that's the cliche in the industry to your POC model, you can get really quick proof points and those little wins that you do internally with your data also show the boss, hey, we got momentum, get more budget. So you're starting to see budgets. Dave Vellante reported on the ETR spending data that budgets now are being allocated on AI. A year ago they're being allocated stolen from other budgets. Now it's like, okay, we see a little bit of wins. Yeah. I mean, one of the things that we've seen, right, we use our cloud native and AI labs uniquely as a place where our clients can come in for a trusted advisor type relationship to your point, we help them understand what's going to work best for them. But it's also a place of innovation and acceleration. But the remarkable thing that we've seen is we'll do an engagement with a client, we'll build, call it a POC, and the client then sticks to the lab thereafter, right? It's really interesting, once the client sees value, the ideas just keep flowing. I think that your product model is genius because that gets them into the discovery side of the solution, okay? And it's not your old school IT transformation playbook, it's we're building it with you. And I think that speeds the process up. And that is actually, you're building the bridge to the future with them. And that's part of the engagement I think people need the most. And in JANAI is 100% value creation, the business level that they want to have. We still, there is some cost reductions, but in the long run, the one that they really go for is exactly what you said, how is it going to change my business? How am I going to accelerate? We have, as an industry, I don't think we have been able to capture very well the diminished cost and business value by here, in this technology we really need to learn how to do it for our clients. Excellent. Siky, now great to have you on. We could go for another hour. Just on your, we should do a whole segment on the customer stories because that's real data that you can share with the field and it would get more motivation, get the confidence up. And it listed the best practices. That's what we know. Confidence is critical, enthusiasm check, everyone's enthused. Confidence is a great point and we'll have to have you back on. To end the segment, we'll give you both the final word, final statement. What's the big relationship with Google? How is that helping HCL and what's the benefits to customers? What should they know about? So HCL Tech was probably the first partner that bet all in on Google Cloud when Google was still very small and a strong challenger. So we have a long-lasting relationship. There is, and HCL as a, HCL Tech as engineers, we like to code on Java. It's a very natural environment for us and there is a little bit of a geek affiliation a lot of that. And we have a very open relationship. We invest on the new releases and we do a lot of engagement in the market. It's pleasant to work with. They're very, they are a challenger so it's always fine to win as your challenger. They have a good AI too. Oh yeah, I told TK, now the pack is getting your way, you just got to push it through because it's enterprise. They got the right formula. And it's a good point. I mean, I think Google have done a particularly good job at making Gen AI consumable. That's really important to our clients. They like that ease of consumption. And clearly, from a Gemini perspective, all of the capability that we expect is there. One thing that I wouldn't want to overlook though is a lot of clients have got a very strong interest in open source approaches. So the ancillary availability of Gemma for example as an open source model approach does actually resonate really strong with clients who favor that opportunity too. Yeah, love the AI force, love the AI labs. It's a great formula, congratulations and it's just getting started. Yes, moving quickly. Thanks for coming on the show. Thank you guys. Thank you for having us. I'm Rebecca Knight for John Furrier. Stay tuned for more of theCUBE's live coverage of Google Cloud Next. You are watching theCUBE, the leading source for enterprise tech news.