 From the Fairmont Hotel in the heart of Silicon Valley, it's theCUBE, covering when IoT met AI, the intelligence of things. Brought to you by Western Digital. Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Jose, the Fairmont Hotel. At a little event, it's when IoT met AI, the intelligence of things. As we hear about the internet of things all the time, this is really about the data elements behind AI and machine learning and IoT. And we're going to get into it with some of the special guests here. We're excited to get, the guy's going to kick off this whole program shortly. This is Tom Sturmer. He is the, I got to get the new title, the Global Managing Director, Ecosystem and Partnerships from Accenture. Tom, welcome. Thank you Jeff. And congrats on the promotion. Thank you. So, IoT, AI, buzzwords, a lot of stuff going on, but we're really starting to see stuff begin to happen. I mean, there's lots of little subtle ways that we're seeing AI work its way into our lives and machine learning work our way into its life, but obviously there's a much bigger wave that's about to crest here shortly. So, as you kind of look at the landscape from your point of view, you get to work a lot of customers, you get to see this stuff implemented in industry. It's kind of your take on where we are. Well, I would say that we're actually very early. There are certain spaces with very well-defined parameters where AI has been implemented successfully. Industrial controls at a micro level where there's a lot of well-known parameters that the systems need to operate in. And it's been very easy to be able to set those parameters up. There's been a lot of historical heuristic systems to kind of define how those work and they're really replacing them with AI. So, in the industrial space, there's a lot of take up and we'll even talk a little bit later about Siemens who's really created a sort of a self-managed factory who's been able to take that out from a tool level to a system level to a factory level to enable that to happen at those broader capabilities. And I think that's one of the inflection points we're going to see in other areas where there's a lot more predictability in a lot of other IoT systems, to be able to take that kind of system level and larger scale factors of AI and enable prediction around that, like supply chains, for example. We're really not seeing a lot of that yet, but we're seeing some of the micro bit pieces being injected in where the danger of it going wrong is lower because the training for those systems is very difficult. Yeah, it's interesting. There's so much talk about the sensors and the edge and edge computing and that's interesting, but as you said, it's really much more of a system approach is what you need and it's really kind of the economic boundaries of the logical system by which you're trying to make a decision. And we talk all the time, are you optimizing for one wind turbine? Are you optimizing for one field that contains so many wind turbines? Are you optimizing for the entire plant? Are you optimizing for a much bigger, larger system that may or may not impact what you did on that original single turbine? So systems approach is a really critical importance. It really is. And what we've seen is that IoT investments have trailed a lot of expectations as to when they were going to really jump in the enterprise. And what we're finding is that when we talk to our customers, a lot of them are saying, look, I've already got data. I've got some data. Let's say I'm a mining company and I've got equipment down in mines. I've got sensors around oxygen levels. I just don't get that much value from it. And part of the challenge is that they're looking at it from a historical data perspective and they're saying, well, I can see the trajectory over time of what's happening inside of my mines. But I haven't really been able to put in prediction. I haven't been able to sort of assess when equipment might fail. And so we're seeing that when we're able to show them the ability to affect an eventual failure that might shut down revenue for a day or two when some significant equipment fails, we're able to get them to start making those investments and they're starting to see the value in those micropockets. And so I think we're going to see it start to propagate itself through in a smaller scale and prove itself because there's a lot of uncertainty. There's a lot of work that's got to be done to stitch them together. And IoT infrastructure itself is already a pretty big investment as it is. I'm going to short that mine company because we had Caterpillar on a couple of weeks ago. And they're driving fleets of autonomous vehicles. We're talking about some of those giant mining trucks who any unscheduled downtime, the economic impact is immense, well beyond. Worrying about a driver being sick or how to fight with his wife or whatever reason is bringing down the productivity of those vehicles. So it's actually amazing. There's little pockets where people are doing it. I'm curious to get your point of view too on kind of managing combat. The guy's like, I'm not sure what the value is because the other kind of big topic that we see is when will the data and the intelligence around the data actually start to impact the balance sheet? Because data used to be kind of a pain, right? You had to store it and keep it and it costs money and you had to provision servers and storage. But really now in the future, the data that you have, the algorithms you apply to it will probably be an increasing percentage of your asset value, if not the primary part of your asset value. You see people start to figure that out. Well, they are. So if you step back away from IoT for a minute and you look at how AI is being applied more broadly, we're finding some transformational value propositions that are delivering a lot of impacts to the bottom line. And it's anywhere from where people inside of a company interact with their customers, being able to anticipate their next move, being able to predict, given these parameters of this customer, even what kind of customer carry agent should I put on the phone with them before you even pick up the phone to anticipate some of those expectations. And we're seeing a lot of value in things like that. Excuse me, so when you zoom it back into IoT, some of the challenges are that the infrastructure to implement IoT is very fragmented. There's 360 some IoT platform providers out in the world and the places where we're seeing a lot of traction in using predictive analytics and AI for IoT is really coming in the verticals like industrial equipment manufacturers where they've kind of owned the stack and they can define everything from the bottom up. And what they're actually being able to do is to start to sell product heavy equipment by the hour, by the use, because they're able to get telemetry off of that product, see what's happening, be able to see when a failure is about to come and actually sell it as a service back to a customer and be able to predictively analyze when something fails and get spares there in time. And so those are some of the pocket's words really far ahead because they've got a lot of vertical integration of what's happening. And I think the challenge on adoption of broader scale for companies that don't sell very expensive assets into the market is how do I as a company start to stitch my own assets that are from all kinds of different providers and all kinds of different companies into a single platform. And what the focus has really been in IoT lately for the past couple of years is what infrastructure should I place to get the data? How do I provision equipment? How do I track it? How do I manage it? How do I get the data back? And I think that's necessary, but completely insufficient to really get a lot of value IoT because really all you're able to do then is get data. What do you do with it? All the value is really in the data itself. And so the alternative approach a lot of companies are taking is starting to attack some of these smaller problems. And each one of them tends to have a lot of value on its own. They're really deploying that way. And some of them are looking for ways to let the battles of the platforms at least get from 360 down to 200 so that I can make some bets. And it's actually proving to be a value, but I think that is one of the obstacles we have to adoption. The other thing you mentioned interesting before we turn on the cameras is really thinking about AI as a way to adjust the way that we interact with the machines. And there's two views of the machines taking over the world. Is it the beautiful view where it frees us up to do other things or suddenly nobody has a job, right? The answer is probably somewhere in the middle. But clearly AI is going to change the way and we're starting to see just barely the beginnings with Alexa and Siri and Google Home with voice interfacing and the way that we interact with these machines which is going to change dramatically with the power of, as you said, prescriptive analytics, presumptive activity and just change that interaction from what's been a very rote, fixed, hard to change to putting, as you said, some of these lighter weight, faster to move, more agile layers on the top stack which can still integrate with some of those core SAP systems and systems of record in a completely different way. Exactly. And you know, I actually use, I often use the metaphor of autonomous driving and people seem to think that that's kind of way far out there. But if you look at how driving an autonomous vehicle is so much different from driving a regular car, right? You don't have to worry about it, I'm the new chef of executing the driving process. You don't have to worry about throttle break. You don't have to worry about taking a right turn on red. You don't have to worry about speeding. What you have to worry about is the more abstract concepts of source, destination, route that I might want to take. You can offload that as well. And so it changes what the person interacting with the AI system is actually able to do and the level of cognitive capability that they're able to exercise. We're seeing similar things in medical treatment. We're using AI to do predictive analytics around imagery coming off of medically. It's not only starting to improve diagnoses in certain scenarios, but it's also enabling the techs and the doctors involved in the scans to think on a more abstract level about what the broader medical issues are. And so it's really changing sort of the dialogue that's happening around what's going on. And I think this is a good metaphor for us to look at when we talk about societal impacts of AI as well. Because there are some people who embrace moving forward to those higher cognitive activities and some who resist it. But I think if you look at it from a customer standpoint as well, no matter what business you're in, if you're a services business, if you're a product business, the way you interact with your employees and the way you interact with your customers can fundamentally be changed with AI because AI can enable the technology to bend to your intentions, similar to the call center that we talked about. Those are subtle activities. It's not just AI for voice recognition, but it's also using AI to alter what options are given to you and what scenarios are going to be most beneficial. More often than not, you get it right. Well, the other great thing about autonomous vehicles, I mean, it's such a fun topic because it's something that people can understand and they can see and they can touch in terms of a concept to talk about some of these higher level concepts. But the second order impacts, which most people don't even begin to think, they're like, I want to drive my car, is you don't need parking lots anymore because the cars can all park off site, just like they do at airports today at the rental car agency. You don't need to build a crash cage anymore because the things are not going to crash that often compared to human drive. So how's the interior experience of a car change when you don't have to build basically a crash cage? I mean, there's so many second order impacts that people don't even really begin to think about. And we see this time and time again, we saw it with kind of cloud innovation where it's not just, is it cheaper to rent a server from Amazon than to buy one from somebody else, it does the opportunity for innovation, enable more of your people to make more contributions than they could before because they were too impatient to wait to order the server from the IT guy. So that's where I think too, people so underestimate kind of the big, Mars law, my favorite, we overestimate the short term and completely underestimate the long term, the impact of these things. The doubling function, exactly. Yeah, absolutely. I mean, it's hard for a human kind is geared towards linear thinking, right? And so when something like Mars law continues to double every 18 months, price performance continues to increase, storage, compute, visualization, display, networking, the sensors and mems, all of these things have gotten so much cheaper. It's hard for a human of any intelligence to really comprehend what happens when that doubling occurs for the next 20 years, which we're now getting on the tail end of that fact. And so those manifest themselves in ways that are a little bit unpredictable. And I think that's going to be one of our most exciting challenges over the next five years is what does an enterprise look like? Right. What does a product look like? One of the lessons that I spent a lot of time in race car engineering in my younger days, and actually did quants and analytics, and what we learned from that point is as you learned about the data, you started to fundamentally change the architecture of the product. And I think that's going to be a whole new series of activities that are going to have to happen in the marketplace. It's about rethinking fundamentally products. Uber's a great example of a company that's completely disrupted an industry. And on the surface of it, it's been disrupted because of the fact that they essentially disassociated the consumption from the provision of the product and didn't have to own those assets so they could grow after they. Right, right. But what they fundamentally did was to use AI to be able to broker when should I get more cars? Where should the cars go? And because they're also, we're on the forefront of being able to drive this whole notion of consumption of cars. And in getting people's conceptual mindset shifted to having to own a car to, well, I know an Uber's going to be there. It becomes like a power outlet. I can just rely on it. And now people are actually starting to double think about should I even own a car? Right, right. Whole different, whole different impact to the autonomous vehicles. And if I do own a car, why should it be sitting in the driveway when I'm not driving it, right? Send it out to go. Exactly. To go work for me, make it a performing asset. Well, great conversation. You guys, Accenture's in a great spot. We're always at the cutting edge. I used to tease a guy who used to work with Accenture. You know, we had, you know, you guys squeezed out all the fat in the supply chain in the ERP days. And again, a lot of these things are people changing the lens and seeing fat and efficiency. And then attacking it in a different way, whether it's Uber or Airbnb with empty rooms in people's houses. We had Paul Dordion at the GE Industrial Internet launched a few years back. So you guys are in a great position because you get to sit right at the forefront and help these people make those digital transformations. And I'll tell you, I mean, supply chains is another one of those high level systems opportunities for AI. We're being able to optimize, you know, think about a completely automated distribution chain from factory all the way to the drone landing at your front doorstep as a consumer. That's a whole other level of efficiency that we can't even contemplate. Don't bet against Bezos, that's what I always say. All right, Tom Sturmer, thanks for spending a few minutes and good luck with the keynote. I appreciate it, Jeff. All right, I'm Jeff Frick. You're watching theCUBE. We're at the intelligence of things where I was team that AI. You're watching theCUBE. Thanks for watching.