(upbeat music) >> Okay, welcome back everyone to theCube's coverage of AWS re:Mars here in Las Vegas. I'm John Furrier, host of theCube. Got Alexey Surkov, Partner at Deloitte joining me today. We're going to talk about AI biased AI trust, trust in the AI for the, to save the planet to save us from the technology. Alexey thanks for coming on. >> Thank you for having me. >> So you had a line before you came on camera that describe the show, and I want you to say it if you don't mind because it was the best line that for me, at least from my generation. >> Alexey: Sure. >> That describes the show and then your role at Deloitte in it. >> Alexey: Sure. Listen, I mean, I, you know, it may sound a little corny, but to me, like I look at this entire show, at this whole building really, and like everybody here is trying to build a better Skynet, you know, better, faster, stronger, more potent, you know, and it's like, we are the only ones, like we're in this corner of like Deloitte trustworthy AI. We're trying to make sure that it doesn't take over the world. So that's, you know, that's the gist of it. How do you make sure that AI serves the good and not evil? How do you make sure that it doesn't have the risk? It doesn't, you know, it's well controlled that it does what we're, what we're asking it to do. >> And of course for all the young folks out there the Terminator is the movie and it's highly referenced in the nerd circles Skynet's evil and helps humanity goes away and lives underground and fights for justice and I think wins at the end. The Terminate three, I don't, I can't remember what happened there, but anyway. >> Alexey: I thought the good guys win, but, you know, that's. >> I think they do win at the end. >> Maybe. >> So that brings up the whole point because what we're seeing here is a lot of futuristic positive messages. I mean, three areas solve a lot of problems in the daily lives. You know, machine learning day to day hard problems. Then you have this new kind of economy emerging, you know, machine learning, driving new economic models, new industrial capabilities. And then you have this whole space save the world vibe, you know, like we discover the moon, new water sources maybe save climate change. So very positive future vibe here at re:Mars. >> Alexey: Absolutely. Yeah, and it was really exciting just watching, you know, watching the speakers talk about the future, and conquering space, and mining on the moon like it's happening already. It's really exciting and amazing. Yeah. >> Let's talk about what you guys are working at Deloitte because I think it's fascinating. You starting to see the digital transformation get to the edge. And when I say edge, I mean back office is done with cloud and you still have the old, you know, stuff that the old models that peoples will use, but now new innovative things are happening. Pushing software out there that's driving you with the FinTech, these verticals, and the trust is a huge factor. Not only do the consumers have a trust issues, who owns my data, there's also trust in the actual algorithms. >> Exactly. >> You guys are in the middle of this. What's your advice to clients, 'cause they want to push the envelope hard be cutting edge, >> Alexey: Right. >> But they don't want to pull back and get caught with their, you know, data out there that might been a misfire or hack. >> Absolutely. Well, I mean the simple truth is that, you know, with great power comes great responsibility, right? So AI brings a lot of promise, but there are a lot of risks, you know. You want to make sure that it's fair, that it's not biased. You want to make sure that it's explainable, that you can figure out and tell others what it's doing. You might want to make sure that it's well controlled, that it's responsible, that it's robust, that, you know, if somebody feeds it bad data, it doesn't produce results that don't make sense. If somebody's trying to provoke it, to do something wrong, that it's robust to those types of interactions. You want to make sure that it preserves privacy. You know, you want to make sure that it's secure, that nobody can hack into it. And so all of those risks are somewhat new. Not all of them are entirely new. As you said, the concept of model risk management has existed for many years. We want to make sure that each black box does what it's supposed to do. Just AI machine learning just raises it to the next level. And we're just trying to keep up with that and make sure that we develop processes, you know, controls that we look at technology that can orchestrate all this de-risking of transition to AI. >> Deloitte's a big firm. You guys saw you in the US open sponsorship was all over the TV. So that you're here at re:Mars show that's all about building up this next infrastructure in space and machine learning, what's the role you have with AWS and this re:Mars. And what's that in context of your overall relationship to the cloud players? >> Alexey: Well, we are, we're one of the largest strategic alliances for AWS, and AWS is one of the largest ones for Deloitte. We do a ton of work with AWS related to cloud, related to AI machine learning, a lot of these new areas. We did a presentation here just the other day on conversational AI, really cutting edge stuff. So we do all of that. So in some ways we participate in that part of the, the part of the room that I mentioned that is trying to kind of push the envelope and get the new technologies out there, but at the same time, Deloitte is a brand that carries a lot of, you know, history of trust, and responsibility, and controls, and compliance, and all of that comes, >> John: You get a lot of clients. I mean, you have big names. Get a lot of big name enterprises >> Right. >> That relied on you. >> Right, and so >> They rely on you now. >> Exactly, yeah. And so, it is natural for us to be in the marketplace, not only with the message of, you know, let's get to the better mouse trap in AI and machine learning, but also let's make sure that it's safe, and secure, and robust, and reliable, and trustworthy at the end of the day. And so, so this trustworthy message is intertwined with everything that we do in AI. We encourage companies to consider trustworthiness from the start. >> Yeah. >> It shouldn't be an afterthought, you know. Like I always say, you know, if you have deployed a bot and it's been deciding whether to issue loans to people, you don't want to find out that it was like, you know, biased against a certain type of (indistinct) >> I can just see in the boardroom, the bot went rogue. >> Right, yeah. >> Through all those loans you know. >> And you don't want to find out about it like six months later, right? That's too late, right? So you want to build in these controls from the beginning, right? You want to make sure that, you know, you are encouraging innovation, you're not stifling any development, and allowing your- >> There's a lot of security challenges too. I mean, it's like, this is the digital transformation sweet spot you're in right now. So I have to ask you, what's the use case, obviously call center's obvious, and bots, and having, you know, self-service capabilities. Where is the customers at right now on psychology and their appetite to push the envelope? And what do you guys see as areas that are most important for your customers to pay attention to? And then where do you guys ultimately deliver the value? >> Sure. Well, our clients are, I think, are aware of the risks of AI. They are not, that's not the first thing that they're thinking about for the most part. So when we come to them with this message they listen, they're very interested. And a lot of them have begun this journey of putting in kind of governance, compliance, controls, to make sure that as they are proceeding down this path of building out AI, that they're doing it responsibly. So it is in a nascent stage. >> John: What defines responsibility? >> Well, you want to, okay, so responsibility is really having governance. Like you have a, you build a robot dog, right? So, but you want to make sure that it has a leash, right? That it doesn't hurt anybody, right? That you have processes in place that at the end of the day, humans are in control, right? I don't want to go back to the Skynet analogy, right? >> John: Yeah. >> But humans should always be in control. There should always be somebody responsible for the functioning of the algorithm that can throw the switch at the right time, that can tweak it at the right time, that can make sure that you nudge it in the right direction that at no point should somebody be able to say, oh, well, it's not my fault. The algorithm did it, and that's why we're in the papers today, right? So that's the piece that's really complex, and what we try to do for our clients as Deloitte always does is kind of demystify that, right? >> John: Yeah. >> So what does it actually mean from a procedures, policies, >> John: Yeah, I mean, I think, >> Tools, technology, people. >> John: Yeah, I mean, this is like the classic operationalizing a new technology, managing it, making sure it doesn't get out of control if you will. >> Alexey: Exactly. >> Stay on the leash if you will. >> Alexey: Exactly. Yeah. And I guess one piece that I always like to mention is that, it's not to put breaks on these new technologies, right? It's not to try to kind of slow people down in developing new things. I actually think that making AI trustworthy is enabling the development of these technologies, right? The way to think about it is that, we have, you know, seat belts, and abs brakes, and, you know, airbags today. And those are all things that didn't exist like 100 years ago, but our cars go a lot faster, and we're a lot safer driving them. So, you know, when people say, oh, I hate seatbelts, you know, you're like, okay, yes, but first of all, there are some safety technologies that you don't even notice, which is how a lot of AI controls work. They blend into the background. And more importantly, the idea is for you to go faster, not slower. And that's what we're trying to enable our clients to do. >> Well, Alexey, great to have you on theCube. We love Deloitte come on to share their expertise. Final question for you is, where do you see this show going? Where do you guys, obviously you here, you're participating, you got a big booth here, where's this going? And what's next, where's the next dots that connect? Share your vision for this show, and kind of how it, or the ecosystem, and this ecosystem, and where you're going to intersect that? >> Wow. I mean, this show is already kind of pushing the boundaries. You know, we're talking about machine learning, artificial intelligence, you know, robotics, space. You know, I guess next thing I think, you know, we'll be probably spending a lot of time in the metaverse, right? So I can see like next time we come here, you know, half of us are wearing VR headsets and walking around and in meta worlds, but, you know, it's been an exciting adventure and, you know I'm really excited to partner and spend, you know spend time with AWS folks, and everybody here because they're really pushing the envelope on the future, and I look forward to next year >> The show is small, so it feels very intimate, which is actually a good feeling. And I think the other thing in metaverse I heard that too. I heard quantum. I said next, I heard, I've heard both those next year quantum and metaverse. >> Okay. >> Well, why not? >> Why not? Exactly, yeah. >> Thanks for coming on theCube. Appreciate it. >> Thank you. >> All right. It's theCube coverage here on the ground. Very casual Cube. Two days of live coverage. It's not as hot and and heavy as re:Invent, but it's a great show bringing all the best smart people together, really figure out the future, you know, solving problems day to day problems, and setting the new economy, the new industrial economy. And of course, a lot of the world problems are going to be helped and solved, very positive message space among other things here at re:Mars. I'm John furrier. Stay with us for more coverage after this short break. (upbeat music)

(bright upbeat music) >> Welcome back to theCube's coverage of HPE's Green Lake announcement. We've been following the caves of Green Lake's announcement for several quarters now, and even years. And we're going to look at cloud adoption and frameworks to help facilitate cloud adoptions. You know, in 2020, the world was on a forced march to digital and there was a lot that they didn't know. Big part of that was how to automate, how to reduce your reliance on physically, manually and plugging things in. And so, customers need an adoption framework to better understand and how to de-risk that journey to the cloud. And with me to talk about that are Alexia Clements, who's the Vice President at Worldwide go to market for GreenLake cloud services at HPE and Alexei Gerasimov who's the vice president of Hybrid Cloud Delivery advisory and professional services at Hewlett Packard Enterprise. Folks, welcome to theCube. >> Alexia: Thanks so much for having us. >> You're very welcome. So, Alexei, what is a cloud adoption framework? How does that all work? >> Gerasimov: Yeah, thanks Dave. So the framework is a structured approach to elevate the conversation, to help our customers get outcomes. So we've been helping customers adopt the benefits in the most of IT for a decade. And we've noticed that they basically focus on eight key areas as they transform to cloud-like capabilities. It's a strategy and governance, it's innovation, people, a dev ops applications, operations security, and data. So we've structured our framework around those core components to help our customers get value. Because end of the day, it's all about changing the way they operate. To get the advantage of all of it. >> Yes. So you can't just pave the cow path and kind of plug your existing process. There's a lot that's unknown, as I said up front. So, so Alexia, maybe you could talk a little bit more about some of the real problems that you're solving with customers that you see in the field. >> Alexey: Yeah, absolutely. So most customers are going through some form of digital transformation and these transformations are difficult and they need a structured approach to help them through that journey. I kind of like to think of it as a recipe to make a meal. So you need to know what ingredients to buy and what are the steps to perform to make that meal. >> Okay. So when you talk to customers, what do you, what do you tell them? That's in it for them after the, after you've actually successfully helped them deploy? What are they telling you? >> Yeah, well, they're telling they now have reached their business outcomes and they're, you know, they're a more agile organization. >> What's the experience look like when you, when you go through one of these journeys and you, you apply the adoption framework, can you sort of paint a picture for us? >> Yeah, absolutely. So every customer is in some sort of transformation, like Alexia said, that transformation implies you've got to know where you start and again, know where you're going. So the experience traditionally is customers need to understand what are my current hybrid cloud capabilities? What do I have, what am I missing? What's lacking and then determine where do you want to go? And in order to get from point A to point B, they have to get a prescriptive approach. So the framework sort of breaks down their path from where they are to their desired maturity. And it takes them in the very prescriptive path to get there. >> So you start with an assessment, you do a gap analysis based on their skill sets. I presume you identify what's possible, help them understand, you know, best practice, which they may not achieve, but this is kind of their north star. Right? And then do you help? How do you help them fill those gaps? Because are skills gaps. Everybody talks about that today. You guys presumably can provide additional services to do that, but so can you add a little bit color to that scope? >> Yeah, yeah, absolutely. And so to your point, the first is a maturity level. So once you figure out the maturity level, you understand what needs to be done. So if you look at our domain, the eight domains that I mentioned and the framework, people is a big one, right? Most of the folks are struggling with people's skills and organizational capabilities. And it's so because it's an operating model change, right? And people are the key component to this operating model change. So we help our customers figure out how do we achieve that optimal operating level and operating a model maturity. And that could be on-prem that could be on public cloud. That could be hybrid. That could be at the edge. And yeah, we, if we can HP, the framework, by the way is pretty, pretty open and pretty objective. If we can help our customers address and achieve their sales gaps great. If we can not directly, then we can have a partner that can help them, you know, plug in something that we don't have. >> Are you finding that, that in terms of the maturity that most people have some kind of experience with, with cloud, but they're struggling to bring that cloud experience to their on-premise state. They don't want to just shove everything into the cloud. Right. So, what does that kind of typical journey look like for folks? I know there's--it's a wide spectrum, or you've got people that are maybe more mature. Maybe some of the folks in financial services got more resources, but can you sort of give us a sense as to what the typical, the average. >> Oh yeah yeah yeah, absolutely. By the way. So that give you a customer example, perfect example of a large North American integrated energy company. They decided to go cloud fresh, like a lot of companies. that wants to do cloud first. And why? The reason was agility. So they started going to the cloud and they realized in order to get agility, you can't just go to you, pick your public CSP, you got to change the way to operate. So they brought us in and they asked, could you help me figure out how we can change the organization? So we actually operate on the proper level of maturity. So we brought our team in. We help them figure out what do we need to look at? We need to look at operations. We need to look at people. We need to look at applications, and we need to figure out what gives you the best value. So when all said and done, they realized that their initial desire of, you know, public first or cloud first, wasn't really public cloud first. It's a way to operate. So now the customer is in three different public CSPs. They're on-prem, there are at edge and everywhere. So that's the focus. Yeah. >> Is the scope predominantly the technical organization. How deep does it go into the, to the business? Is it obviously the application development team is involved, but how deep into the business does this go? The framework. >> Right, and it's absolutely not a technology focused, the whole concept areas, it's outcomes based, and it's a results based. So if you look at the framework, there's really not a single element of the framework that says tech, like storage or compute. No, it's its people, its data, it's business value, strategy and governance, because the goal for us is being objective is we're just trying to help them address the outcomes. Not necessarily to give them more tech. >> So Alexia, I like that answer because it's a wider scope as, I mean, if we just focused on the tech and that's the swim lane, it'd be a lot easier. But as we all know, it's the people in the process that are really the hard part. So that, that makes the challenge for customers greater. You're hurting more cats. So what are the, some of the obstacles that potentially you help customers before they dive in understand. >> Yeah. So we're giving them a roadmap on where they need to go. So we're like I mentioned that recipe, so we're really trying to identify what is their strategy and where do they, what are the outcomes that they're trying to drive and help them on a street, you know, with that path to meet those outcomes. So some of those, I mean, every customer's a little bit different. I mean, we had one customer, which was a, one of the largest hospitals in north America and they, they would needed to, they wanted to go to the cloud, but they realized they couldn't put all of their patient data on the cloud. So what we did was we helped them in changing their operating model and really look to see how does that, how do they need to what's that end game for them, and actually help redo their operating model to have some in the cloud and some on-prem and, and really identify, you know, where they needed to go for their roadmap. So that was an obstacle that they had, hey, we can't put all this stuff out there. How does that now need to work in this new world? >> I would think the data model is a big deal here. I mean, you just gave an example where there's a, there's a, there's a governance and compliance aspect to it. So thinking about that example, did they have to change the way in which they provided federated governance was that presumably identify whose whose responsibility that was to adjudicate, but also had to get the, the implementers to follow that's the, how does that all work? Is it just the deep conversations? And then you figure out how to codify it or. >> No. So what so we have, so through those eight domains that Alexia mentioned, we go through, step-by-step how they need to think about it. And within mind, what are their business outcomes and goals that they're trying to achieve? So really identifying how they need to change that operating model to meet those business outcomes. >> So what's the output, it's a plan, right. That's tailored to the customer. Is that, is that correct? And, and then sort of assistance in implementing downstream or what do they get? >> Yeah, yeah, absolutely. Just to piggyback to what Alexia said, the alignment, the early alignment, the strategy and governance, as you mentioned, this is probably the most important thing, because everybody says we want to be cloud first, but what does that mean? Cloud first means different things to everyone. So we said, give him a plan. The first we'll help with figure out is what does that mean for you? Because at the end of the day, you're not going to the cloud for the sake of cloud, or anywhere you go into the cloud to get some sort of value. So what's that alignment. So the plan is supposed to help you on your road to that value, right? So we'll help them figure out what I want to do, why, for what purpose, what's going to actually address my business value. So yes, they will get a plan as part of it. But more importantly, they get, they get a set of activities, communication plans, which by the way, another block that you got to address. >> Dave: Huge. >> Yeah. >> Yeah. I mean, a lot of executives tell me, look, if you don't change your operating model and go to the cloud, yeah. You're talking, you know, nickels and dimes. If you want to get telephone numbers, you know, big companies, you want to get into bees with billions, you have to change the operating model. And the problem that they tell me is a lot of times the corner offices, okay, we're doing this, but everybody in the fat middle says, what are we doing? >> Right. And now more than ever, I mean, customers need to look at that model like a more modern operating model to realize the benefits of cloud capabilities, whether that be at the edge, their data centers, their colos cloud. So they really need to look at that. And what we've seen is with our framework, we're really helping customers accelerate their business outcomes. De-risk their transformation, and really optimize that cloud operating model. >> It's that alignment you reducing friction within the organization, confusing confusion. When people don't know which direction they're going, they'll just going to go wherever they're pointed. Right. Right. >> And you back to the alignment. So you've got alignment and you mentioned communication. You have to communicate up and down and left and right across the organization because that's one of the most probably ignored elements of any transformation lots of people don't know. So you got to communicate. And then you have to actually measure and report on how they, you know, how the transformation is happening. So we can help in all three of those. >> Especially when everybody's remote. Yeah. Right. And then I said, hey, these digital transformations, there's so much, that's unknown. >> Alexia: Right. It's difficult. >> It's a lot of new. And so you also have to, I presume part of the plan is, Hey, you're not, it's not going to be a hundred percent perfect. So you have to have. >> Alexia: Right. And you're constantly iterating on that plan. >> What does this have to do with GreenLake? >> Alexia: Yeah. So, I mean, GreenLake is HPE's you know, cloud everywhere. And what we're really doing is this framework is helping customers with that path to get that cloud-like experience and as a service model. And so the framework is really helping clients understand where do they need to go and what GreenLake solutions can help them get there. >> So the fundamental assumption of not every cloud player necessarily bad, I would say most hyperscalers is, hey, ultimately, all the data and the workloads are going to go to the cloud, that's their operating premise. So they all have an operating framework to facilitate that. >> Alexia: Right. >> It's, it's tongue in cheek, but it's true. So, but everybody has one of these. How was yours different? >> Yeah. So like, like you said, there's lots of different, you know, frameworks out there, but what we're really focused on is meeting those business goals and outcomes for clients. So we didn't focus on the technology. Like we mentioned what we were really focusing around. I mean, we kind of learned early on that every customer has technical capabilities, applications, data in multiple clouds, on-prem in colos and at the edge. So we didn't focus on like just the technology. So it's really driving business outcomes and their goals and, and the tech, all those frameworks that we just mentioned, they're really specifically driving a particular technology tool or vendor implementing a particular technology or vendor. >> So we've talked about outcomes a lot, but I wonder if we could peel the onion on that. So, you know, the highest level outcome is I want to increase revenue, cut costs, drop to the bottom line, increase shareholder value, improve employee experiences and retention, make customers happier, grow my business. I mean, those are, I mean, I, I don't know a lot of businesses that don't... >> Alexia: Right. >> want to do that, So. Okay. That's cool. But then I'm imagining you really start to peel the layers and say, okay, this is how we're going to get there. And you get down to specific objectives as to the, how is that sort of how this works? >> Right, and that's due to echo at Alexia. So that's exactly why ours is different. We're not focusing on how to adopt Microsoft or AWS or Alibaba with focusing on how we can deliver the customer experience or a better revenue, you know, or, you know, increase the value for the consumer for whatever the company will help him. So the framework we'll look at that and figure out how do we actually address it, whether it's on public cloud, whether it's on prem, whether it's at the edge. >> You mentioned Alexia, that something, hey, if we don't have the skills, we can get a partner who does, a big company. You got a huge partner network. So for example, if you might not have necessarily a deep industry expertise, that's where you might lean on a partner or is that, is that a good example or is there a better one? >> Yes and we know. We're not going to just like you mentioned AWS or Microsoft, Alibaba thing that everything will go to public cloud. I don't believe so, but at the same time we know not everything will stay on-prem. So the combination of on-prem, the edge, you know, private cloud and public cloud is what the customers are after. So our partners could be either third party, system integrator that can help us implement something or even the public CSPs, because we know our customers have capabilities everywhere. So the question becomes, how can we holistically address their needs, whether it's on-prem, whether it's in public cloud. >> Great. Guys, thanks so much. >> Alexia: Thank you. Thanks for having us. Appreciate it. >> My pleasure and thank you for watching everybody's as theCube's continuous coverage of HPE's GreenLake announcement, keep it right there for more great content. (bright upbeat music)