>> Narrator: 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 here everybody Jeff Frick here with theCUBE. We're in downtown San Jose at 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 that's going to kick off this whole program shortly is Tom Stuermer. He is the I got to get the new title, the Global Managing Director, Ecosystem and Partnership, from Accenture. Tom, welcome-- >> Thank you, Jeff. >> And congrats on the promotion. >> Thank you. >> So IoT, AI, buzz words, 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 in to 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 with a lot of customers, you get to see this stuff implemented in industry, what'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's been implemented successfully, industrial controls on 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 spaces 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. I think that's one of the inflection points we're going to see in other areas where there's a lot more predictability and 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. So we're really not seeing a lot of that yet, but we're seeing some of the micro pieces being injected in where the danger of it going wrong is lower, because the training for those systems is very difficult. >> 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 in. We talk all the time, we optimizing for one wind turbine? Are you optimizing for one field that contains so many wind turbines? Are you optimizing for the entire plant? Or are you optimizing for a much bigger larger system that may or may not impact what you did on that original single turbine? So a systems approach is a really critical importance. >> It 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 mind. 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 micro pockets. 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. >> Short that mine company, because we had Caterpillar on a couple weeks ago and you know their driving fleets of autonomous vehicles, they'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 had a fight with his wife, or whatever reason is bringing down the productivity of those vehicles. So it's actually amazing the little pockets where people are doing it. I'm curious to get your point of view too on kind of you managed to comment 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 cost money, and you had to provision servers, and storage, but really now and 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 you asset value, you seeing some people start to figure that out? >> Well they are. So if you look, if 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 what kind of customer care 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. And so, excuse me, and so when you zoom it back in to 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 telemeter 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 predictably analyze when something fails and get spares there in time. And so those are some of the pockets where it's 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 for all kinds of different providers, and all kinds of the 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 your 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, and so they're really deploying that way. And some of them are looking for ways to let the battles of the platforms, let's 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 that we have to adoption. >> The other thing you mentioned interesting before we turned on the cameras is really thinking about AI as a way to adjust the way that we interact with the machines. There's two views of the machines taking over the world, is it the beautiful view, or we can freeze this up to do other things? Or certainly 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 the 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, you know 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's so much different from driving a regular car, right? You have to worry about at the minutia of executing the driving process. You don't have to worry about throttle, break. You'd have to worry about taking a right turn on red. You'd 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 injury coming off of medical equipment. 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 it to your intentions. Someone at the call center that we talked about. I mean 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. And more often than not you get it right. >> Well the other great thing about autonomous vehicles, it's just 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 drivers. So how does the interior experience of a car change when you don't have to build basically a crash cage? I mean there's just 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 cloud innovation where it's not just is it cheaper to rent a server from Amazon than to buy one from somebody else? It's 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 Moore's Law my favorite, we overestimate in the short term and completely underestimate in the long term, the impacts of these things. >> It's the doubling function, exactly. >> Jeff: Yeah, absolutely. >> I mean it's hard for people, human kind is geared towards linear thinking, and so when something like Moore's Law continues to double every 18 months price performance continues to increase. Storage, compute, visualization, display. >> Networking, 5G. >> You know the sensors in MEMS, all of these things have gotten so much cheaper. It's hard for 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? 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, 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. Is people rethinking fundamental product. There's a great example of a company that's completely disrupted an industry. 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 rapidly. 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 getting people's conceptual mindset shifted to having owned a car to 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? >> Whole different impact of the autonomous vehicles. And if I do own a car why should it be sitting in the driveway when I'm not driving it? Or I send it out to go work for me make it a performing asset. Well great conversation. You guys Accenture's in a great spot. You're always at the cutting edge. I used to tease a guy I used to work with at Accenture you've got to squeeze out all the fat in the supply chain (laughs) your RP days and again a lot of these things are people changing the lens and seeing fat and inefficiency and then attacking it in a different way whether it's Uber, Airbnb, with empty rooms in people's houses. We had Paul Doherty on at the GE Industrial Internet launch 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. >> I appreciate that. >> I will tell you I mean supply chains is another one of those high level systems opportunities for AI where being able to optimize, think about it 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 nother level of efficiency that we can't even contemplate right now. >> Don't bet against Bezos that's what I always say. All right, Tom Stuermer 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 are at The Intelligence of Things, When IoT met AI. You're watching theCUBE. Thanks for watching. (upbeat music)