>> Live from San Francisco. It's the cube covering IBM thing twenty nineteen brought to you by IBM. >> Welcome back to Mosconi North here in San Francisco, California. I'm student of my co host, A Volante. We're in day three of four days live. Walter. Wall coverage here at IBM think happened. Welcome back to the program. Talk about one of our favorite topics. Cube alarm. Steve Hill, who's the global head of innovation. That topic I mentioned from KPMG, Steve, welcome back to the program. >> Seems to have made good to see you. >> All right. So, you know, we know that the the only constant in our industry is change. And, you know, it's one of those things. You know, I look at my career, it's like innovation. Is it a buzz word? You know? Has innovation stalled out of the industry? But you know, you're living it. You you're you're swimming in it. Talkinto a lot of people on it. KPMG has lots of tools, so give us the update from from last year. >> Well, I think you know, we talked about several things last year, but innovation was a key theme. And and when I would share with you, is that I think across all industries, innovation as a capability has become more mature and more accepted, still not widely adopted across all industries and all competitors and all kinds of companies. But the reality is, innovation used to be kind of one person's job off in the closet today. I think a lot of organizations or realizing you have to have corporate muscle that is as engaged as in changing the status quo as the production muscle is in maintaining the status quo has >> become a cultural. >> It's become part of culture, and so I think innovation really is part of the evolution of corporate governance as far as I'm >> concerned. What one thing I worry about a little bit is, you know, I see a company like IBM. They have a long history of research that throws off innovation over the years. You know, I grew up, you know, in the backyard of Bell Labs and think about the innovation a drove today, the culture you know, faster, faster, faster and sometimes innovation. He does sit back. I need to be able to think longer, You know? How does how does an innovation culture fit into the ever changing, fast paced you? No need to deliver ninety day shot clock of reality of today. >> Well, I think innovation has to be smart, meaning you have to be able to feed the engines of growth. So your horizon one, if you will, of investments and your attention and efforts have to pay off the short term. But you also can't be strategically stupid and build yourself into an alleyway or to our corner, because you're just too short term thought through. Right? So you need to have a portfolio of what we call Horizon three blended with Horizon one and Horizon two types investment. So your short term, your middle term and your longer term needs are being met. Of course, if you think about it like a portfolio of investments, you're going tohave. Probably a smaller number of investments that air further out, more experimental and a larger proportion of them going to be helping you grow. You could say, almost tactically or sort of adjacent to where you are today, incrementally. But some of those disruptive things that you work on an H three could actually change your industry. Maybe you think about today where we are. Azan Economy intangibles are starting to creep into this notion of value ways we've never seen before. Today, the top five companies in terms of net worth all fundamentally rely on intangibles for their worth. Five years ago, it was one or two, and I would argue that the notion of intangibles, particularly data we'll drive a lot of very transformative types of investments for organizations going forward. So you've got to be careful not to starve a lot of those longer term investments, >> right? And it's almost become bromide. Large companies can innovate, but those five companies just mentioned well alluded to Amazon. Google, etcetera Facebook of Apple, Microsoft there, innovators, right? So absolutely and large companies innovate. >> Yes, clearly, yeah, but you have to have muscle, but it doesn't happen by accident, and you do put discipline and process and rigor and tools and leadership around innovation. But it's a different kind of discipline than you need in the operation, so I'll make him a ratio that makes sense. Maybe ninety five percent production, five percent innovation in an organization. That innovation engine is always challenging that ninety five percent Are you good enough? Are you relevant enough? Are you fast enough? Are you agile enough? You need that in every corporate organization in terms of governance to stay healthy and relevant overtime. >> So it's interesting. You know, I was in a session that Jack Welch talk wants, and he's like, I hear big companies can innovate is like big companies made up of people. People are the things that can innovate absolute. But, you know, I've worked in large organizations. We understand that the fossilization process and the goto market that you have, you know, will often kill, you know, those new flowers that are blooming, what separates the people that can drive innovation on DH? You know, put those positive place and kind of the also rans that, you know get left behind window disruption. >> Well, there's several. There's a couple things that I would highlight of a longer list, one of them we culture. I mean, I think innovation has been part of a culture. People in the institution have value innovation and want to be part of it. And there is, you know, a role that everyone can play. Just because you're in operations, if you will, doesn't mean you ignore change or you ignore the opportunity to improve the status quo. But you still have you get paid to operate what I find that is related to culture that gets a lot of people, you know, slow down or or roadblock is the disconnect between the operating part of the business and the innovative part of the business. If you try, if you build them to separately, what happens is you have a disconnection. And if you innovate the best idea in the world over here. But you can't scale it with production, you lose. So you have to make sure that, as as a leader overall, the entire enterprise you build those connections, rotations, leadership, You know, How do you engage the production, you know, engine into the innovation engine? It's to be very collaborative. It should be seamless. You know, everyone likes to say that, but that word, but relative seamlessness is, is heavy architecture. You've gotto build that, you know, collaboration into your model of of how you innovate >> and >> don't innovate in the vacuum. >> And it comes back to the cultural aspects we're talking about. Do you mentioned the ninety day shot? Clocks were here in the Bay Area. Silicon Valley. The most innovative place in the world. They've lived along the ninety day shot clock forever, and it seems to have not heard that so called short term thinking. Why is that? >> Well, there's so much start up here. I mean, at the end of the day, there is so much churn of new thinking and start up in V C. And there's so much activity that it's almost a microcosm, right? Not every place in the world smells, feels, looks like Silicon Valley, right? And the reason for it is in part because there's just so much innovation in what happens here. And these things change me. If you think about, uh, these unicorns that we have today. Today there's about three hundred ninety one unicorns. Just five years ago, there were one hundred sixty globally on before that. Hardly people didn't know they were hardly recognized. But that's all coming from pockets of innovation like Silicon Valley. So I'd argue that what you have here is an interesting amalgamation of culture being part of a macro environment region that that really rewards innovation and demonstrates that in in market valuations in capital raises, I mean, today one hundred million dollars capital raise is pretty common, especially for unicorns. Five, ten years ago. You never see me. It was very difficult to get a hundred million dollars capital, right? >> You mean you're seeing billion dollar companies do half a billion dollars raises today? I mean, it's >> all day, right? And some of them don't make a profit. Which is I mean, and that's kind of the irony, Which is, Are those companies? What did they get that the rest of us, you know, there was that live on Wall Street right out of in New York. What do we not see? Is that some secret that downstream there will be some massive inflow? Hard to say. I mean, look at Amazon is an example. They've used an intangible to take industries out that they were never in before they started selling books, and they leverage customer behavior data to move into other spaces. And this is kind of the intangible dynamic. And the infection >> data was the fuel for the digital disruption to travel around the world. You see that folks outside of Silicon Valley are really sort of maybe creating new innovation recipes? >> Yes. I think that what you see here is starting to go viral right on DH way that KPMG likes to share a holistic way to look at this for our clients. What is what we call the twenty first century enterprise. So the things that we used to do in the twentieth century to be successful, hire people, build more machines, right? You know, buy more assets, hard, durable assets. Those things don't necessarily give you the recipe for success in the twenty first century. And if you look at that and you think about the intangibles work that's been well written about there's there's all kinds of press on this today. You'll start to realize that the recipe for success in this new century is different, and you can't look at it in a silo to say, Okay, so I've gotta change my department or I've got a I've got to go change, You know, my widgets. What you've got to think is that your entire enterprise and so are construct called the twenty first Century prize. Looks at four things. Actually, it's five, and the fifth one is the technologies to enable change in the other four. And those technologies we talk about here and I have made him think which are, you know, cloud data, smart computers or a blockchain, etcetera. But those four pillars our first customer. How do you think about your customer experience today? How do you rethink your customer experience tomorrow? I think the customer dynamic, whether it's generational or it's technologically driven, change is happening more rapidly today than ever. And looking at that front office and the customer dementia, it is really important. The second is looking at your acid base. The value of your assets are changing, and intangibles are big category of that change. But do your do your hard assets make the difference today and forward. Or all these intangibles. Companies that don't have a date a strategy today are at peril of falling victim to competitors who will use data to come through a flank. And Amazons done that with groceries, right? The third category is as a service capabilities. So if you're growing contracting going into new markets are opening new channels. How do you build that capability to serve that? Well, there's a phenomenon today that we know is, you know, I think, very practised, but usually in functions called as a service by capability on the drink instead of going out and doing big BPO deals. Think about a pea eye's. Think about other kinds of ways of get access to build and scale very fucks Pierre your capabilities and in the last category, which actually is extremely important for any change you make elsewhere is your workforce. Um, culture is part of that, right? And a lot of organizations air bringing on chief culture officers. We and KPMG did the same thing, but that workforce is changing. It's not just people you hire into your four walls today. You've got contingent workforce. You have gig economy, workforce a lot of organizations. They're leveraging platform business models to bring on employees to either help customers with help. Dex needs or build code for problems that they like to solve for free. So when you talk about productivity, which we talked about last year and you start thinking about what's separating the leaders from a practical standpoint from the laggers from practically standpoint, a lot of those attributes of changing customer value of assets as a service growth and workforce are driving growth and productivity for that subset of our community and many injured. >> So when you look at the firm level you're seeing some real productivity gains versus just paying attention to the macro >> Correct, any macro way think proactive is relatively flat, and that's not untrue. It's because the bottom portion the laggards aren't growing. In fact, productivity is in many ways falling off, but the ones that are the frontier of those top ten percent fifteen hundred global clients we've looked at, uh, you know, you see that CD study show that they're actually driving growth and productivity substantially, and the chasm is getting larger. >> So, Steve, Steve, it's curious what this means for competition. I think about if I'm using external workforces in open source communities, you know, Cloud and I, you know, changes in the environment. A supposed toe I used to kind of have my internal innovation. Now I'm out in these communities s O You know, we're here than IBM show. You know, I think back the word Coop petition. I first heard in context of talking about how IBM works with their ecosystem. So how did those dynamics change of competition and innovation in this? You know, the gig. Economy with open source and cloud. May I? Everywhere. >> Big implications. I mean, I I think you know, and this is the funny point you made is nontraditional competitors, because I think most of our clients and ourselves recognized that we haven't incredible amount of nontraditional competitors entering our space in professional services. We have companies that are not overtly going after our space, but are creating capabilities for our clients to do for themselves what we used to do for them. Data collection, for example, is one of those areas where clients used to spend money for consultants coming in to gather data into aggregate data with tools today that's ah, a very short process, and they do it themselves. So that's a disintermediation or on bundling of our business. But every business has these types of competitive non Trish competitive threats, and what we're seeing is that those same principles that we talked about earlier of the twenty first century surprise applies, right? How are they leveraging there the base and how they leveraging their workforce? Are they? Do they have a data strategy to think through? Okay, what happens if somebody else knows more about my customers than I do? Right? What does that do to make those kinds of questions need to be asked an innovation as a capability I think is a good partner and driving that nothing I would say, is that eco systems and you made you mention that word, and I want to pick up on that. I mean, I think eco systems air becoming a force in competitive protection and competitive potential going forward. If you think about a lot of you know, household names relative Teo data, you know Amazon's one of them. They are involved in the back office in the middle ofthis have so many organizations they're in integrated in those supply chains. Value change, I think services firms, and particularly to be thinking about how do they integrate into the supply chains of their customers so that they transcend the boars of, you know, their four walls, those eco systems and IBM was We consider KPMG considers IBM to be part of our ecosystem, right? Um, as well as other technology. >> So they're one of one of the things we're hearing from IBM. Jenny talked about it yesterday, and her keynote was doubling down on trust. Essentially one. Could you be implying that trust is a barrier to ay? Ay adoption is that. Is that true? Is that what your data show? >> We we we see that very much in spades. In fact, um, you know, I I if you think about it quite frankly, our oppa has driven a lot of people to class to class three. Amalgamation czar opportunities. But what's happening is we're seeing a slowdown because the price of some of these initials were big. But trust, culture and trust are big issues. In fact, we just released recently. Aye, Aye. And control framework, which includes methods and tools assessments to help our clients that were working with the city of Amsterdam today on a system for their citizens that helped them have accountability. Make sure there's no bias in their systems. As a I systems learn and importantly, explain ability. Imagine, you know. Ah, newlywed couple going into a bank to get a house note and having the banker sit back and have his Aye, aye, driven. You know, assessment for mortgage applicability. Come up moored. Recommend air saying no. You Ugh. I can't offer you a mortgage because my data shows you guys going to be divorced, right? We don't want to tell it to a newlywed couple, right? So explain ability about why it's doing what it's doing and put it in terms that relate to customer service. I mean, that's a pretty it's a silly example, but it's a true example of the day. There's a lot of there's a lack of explain ability in terms of how a eyes coming up with some of its conclusions. Lockbox, right? So a trusted A I is a big issue. >> All right, Steve, Framework that you just talked about the twenty first century enterprise. Is there a book or their papers? So I just go to the website, Or do I need to be a client? Read more about, >> you know, absolutely. You can go to our website, kpmg dot com and you can get all the della you want on the twenty first century enterprise. It talks to how we connect our customers front to middle toe back offices. How they think about those those pillars, the technologies we can help them with. Make change happen there, etcetera. So I appreciate it that >> we'll check it out that way. Don't be left in the twentieth century. Come on. >> No, you can't use twentieth century answers to solve twenty first century challenges, right? >> Well, Steve, he'll really appreciate giving us the twenty first century update for day. Volante on student will be back with our next guest here. IBM think twenty nineteen. Thanks for watching you.

>> Announcer: Live from Las Vegas, it's theCUBE, covering IBM Think 2018, brought to you by IBM. >> Welcome back to theCUBE. We are live on Day One of our three days of coverage of IBM Think, the inaugural single event from IBM. I'm Lisa Martin with Dave Vellante. We're at the Mandalay Bay in beautiful sunny Las Vegas, and we're excited to welcome to theCUBE for the first time, Steve Hill, the Global Head of Innovation at KPMG. Welcome. >> Thanks for having me here. >> So you are giving a talk Wednesday, you said, at the event. >> Yes. >> I want to get a little bit into your role at KPMG, as well as your session. So talk to us a little bit about what your role as the Global Head of Innovation. >> So Innovation is an overused word. I don't particular like the word innovation, but in the context of my role, it really is taking a look at our business and our clients, and saying what it is that our clients need for their futures. What's going to create relevance for our clients as we go forward, and how does our portfolio of services relate to that relevance? And if we have gaps where we see our services not serving them best, or not going to serve them best in the future, my job responsibility is to help for strategy purposes and for investment purposes, bring those points to bear, and to get either investment into those areas, right, or changes in the business as appropriate to make KPMG more relevant to our clients, and to their relevance to their clients, right, that's the whole idea. >> So, Lisa and I talk to a lot of people in theCUBE, and we talk lots about invention, startups inventing something or new technology that gets invented, but innovation to us, and I think KPMG is at the heart of this is taking an invention and actually applying it to effect change, getting it adopted, >> That's right. >> and changing a business, a societal change potentially, is that-- >> That's right, I mean, our short phrase for it is idea to cash for our clients, right. I mean at the end of the day, and I think this is profound in certainly corporate governance evolution, right. We've seen the advent of lots of escrow changes of how companies have been managed, enterprise has been managed, right. The Dutch started with the East Indian Trading Company, one of the first large global enterprises, and since that time we've seen the maturation, the new roles. The CIO role didn't exist much prior to 1950, right. Today we're starting to see innovation to be a very important skill and capability for all corporations, all enterprises, including government, right. And I think we're starting to see a maturation of corporate capability, I would say, in the innovation space, because the pace of change is so fast today, the political, economic, technological, social trends are so complex that you've got to get something in your muscle memory that helps you change your business as much as operate it effectively. >> I'd love to know who you're talking to within organizations. You mentioned CIO role, the CISO role, chief data officer. >> Steve: Right. >> Who are the minds that you're helping to bring together so that an enterprise that needs to digitalize to be competitive will survive, right, really survive these days? How do you help them really embrace a culture of innovation as really there's no other choice? How do you get these minds collectively agreeing, yes, this is the direction we need to go in? >> Yeah, I think, I mean first of all, this is a C-suite conversation and a board conversation in many cases, but the reality is when you start to look at the lack of innovation in an organization, right, and when the environment changes, competitors start to change, and the more complex it is, it's harder and harder for companies to pivot and to reinvent themselves. And we're seeing a lot of unbundling of businesses in today's environment, whether it's a company that moves packages, right, or a professional services firm, or a company that used to distribute videos, right. I mean things change and some of the irony is that sometimes the innovation in companies like Kodak, Steve Sasson invented digital camera, it took eight minutes to go from a snap to a picture, but they invented digital technology from cameras, and that the distribution of digital videos is that it actually would help to, further the demise of that organization. So that notion of how do you take change going on in the environment that you're working at, and more importantly your customers and clients, how does that convert into your business, that's a C-suite conversation, and I think innovation can be embodied in a person to help build process, meaning how do you take an idea, how do you look at the marketplace and get sensory input, convert that to ideas for strategy and for investment, and the investments have to be deployed to the field to the business, and that relationship, that whole lifecycle of innovation requires a lot of people from the enterprise to be involved in it. And I would argue the culture has to evolve because until recently most people, in fact, I would say, including current times, most people in organizations are rewarded for doing what they do well, not breaking what they do, not rethinking what they do. And the more you get into that operational mindset, that I want to wring all the efficiencies out of this process that I can. Right, the more you're wed to the status quo, the more somebody comes in from the side and takes you out. >> So I love this conversation 'cause Steve you're able to take the long view and then I want to sort of shorten it up, and then maybe put it into a longer term context. So over our, your guys 20-plus-year careers, mine a little longer, most of this industry has marched to the cadence of Moore's Law, that's where innovation came from. >> Yes. >> How do you take advantage of Moore's Law? How do you go to client server software, whatever it was, the innovation equation is changing now. It seems to be a function of, these guys have been hearing me say this all day but data that's not siloed, but data that you have access to, applying machine intelligence-- >> Yep. >> And then getting cloud, scale, economics and network effects, and then applying it to your business. >> Bingo. >> So talk about how you see the new wave of innovation in this world of digital or however you phrase it. >> Well, it's interesting, I mean, I don't hear a lot of people phrase it the way you do which I think is spot on which is, and my words are, ubiquitous access to technology which is cloud, data, and that's a huge question mark and a big C-suite conversation. Having a lot of data isn't the key, having the right lot of data is the key. Right so Dyson is moving into auto-making today, right. They have a lot of data and it's very different from what the incumbents have. Is it better or worse? We're going to see, right. And then of course smart computers which is the machine intelligence, right. Those three elements, I think they're fundamentally changing labor productivity. And what I would say is to your question is that innovation is really important here because if all you do is take those three elements and you just digitize a status quo process, you might get marginal benefits, you might get some labor productivity enhancement, you may get some marginal improvement, you may change an outsourcing agreement to an onshore RPA deal, but if that's all you do, you're setting yourself up for a disappointment because what's really going to happen with thinkers, i.e., those that have innovations, they're going to rethink the process. Most of our analog systems are created around people checking people, so you may have nine steps, I'm making it up, in a process, that in a digital world only requires one or two or zero when launching in some cases. And so if you can rethink that process to go from a nine-step to a zero-step process or a one-step that's a nano second long, that changes the dynamic of the process. In fact that's not even nirvana, right, the real nirvana is can you change your business model, right? And I would use IBM, since we're here, as an example of going from a big box with a lot of people running around it, called IBM of the past, Watson, to an API engine that David Kenny has helped to build that says, we're going to have a platform business model leveraging network effects, and I want to have a supply and a demand curve that are much faster growing than my sort of organic ways of growing a network could be, right, through people point clicking. That's innovation. >> IBM is an interesting company because it is a company with a lot of legacy, but I think gets, as you just described it, but you look at the top five companies by market value today, they're six, 700-billion dollar market companies, they are data companies not just with a lot of data, but they've put data at the core, so it's Amazon, it's Apple, it's Facebook, it's Google, et cetera. They've put data at the core whereas most organizations, I'm sure many that you deal with, they have human expertise built around other assets that aren't data. It might be factories, it might be the bottling plants, et cetera. So there's a gap, I don't know, machine, AI gap between sort of those that are innovating today, now granted the stock market can change and, >> Sure. >> Who knows, maybe the oil companies will be back involved, not to drop but how do you deal, how do you advice your clients on how to close that gap? That seems like a huge challenge. >> Well it is a huge challenge, and I think, going back to the three elements, it would be very easy for you to dive bomb into a transformation effort and say, I'm going to go and get some smart computers and hire a bunch of people that know machine intelligence and natural language process, and all that stuff, and put them in a room, and go create some applications, the bottom line is, that's not unimportant. You got to get your hand on the mountain and start climbing, but the data piece, I mean, if you don't understand how data is going to be relevant to your business and to your clients and their clients, right, in the future, you lose. And the reason why those five that you talked about earlier are so successful is they think a couple of steps ahead on the data strategy, right, and they're not thinking about, most organizations by the way, they'll say we want a data strategy and then they'll relegate the strategy thinking part to their businesses which are bifurcated, and they look at the world in silos. And they're doing exactly what they should do which is take care of those businesses, but when you step back into those five companies you've talked about, they step back from those silos and say, what is the enterprise implications, and how do I create new businesses with correlations of data that I didn't have before? I think that requires a whole different level of strategy. It's C-suite and board that has to guide those kinds of decisions. You don't see a lot of people really getting their hands dirty around intense forward-thinking data strategies at the enterprise level like we're talking about here. >> You believe we are entering or going to enter shortly a productivity renaissance. >> I agree, yes. >> That's sort of I'm talking about our off-camera conversation. Explain why you think that, compare it to sort of the Industrial Revolution. Take us through your scenario. >> Sure. So, I mean, when you think about labor, I mean, what are the things that I think those three elements will give us as a society, as a global community, is a pretty big S curve jump in labor productivity. In fact we have at KPMG some efforts to quantify what that might be, looking at what we call frontier firms, and applying those practices back to incumbents. 90% of most industry players is saying what are those differences that we can model. The fact of the matter is when you go back to the Mechanical Revolution, the Industrial Revolution, people did everything by hand prior, right. Equipment helped them do things whether it was, even the printing press saw changes in society and labor, but when you start to getting into heavy manufacture in the Industrial Revolution, productivity was enhanced dramatically, and instead of putting all of these people who were doing things by hand out of business and out of work, it actually created more jobs, a lot more jobs, and a lot more wealth for society. I think we're heading for a similar S-curve change with smart computers, cloud, and with data. And that the roboticism of people is going to be automated, and people are going to be allowed to practice and use what's between their ears a lot more. That's going to create value, insight, new questions to be asked. I mean, how many times have you ever heard this? Every time you answer a question on something that's very important, you want to understand there's two more questions to be asked. Medicine is that way for sure. But you're going to start to see massive advancement in areas where people have had to use a lot of cognitive skills, right. It's severely under-leveraged because they were doing so much roboticism and doing things that computers can start to do now. So I think you're going to start to see a renaissance, if you will, of people using their nogers in ways we haven't seen before, and that's going to change the dynamics of productivity and labor in a way that's going to create wealth for everyone. >> And it's going to change industry. So, okay, so I got a bunch of questions for you then. >> Steve: Yep. >> Here we go. And I asked this earlier but I didn't really get an answer. Will machines? >> Steve: From me or from somebody else? >> No, from somebody else. >> Steve: Okay. >> Will machines make better diagnoses than doctors and when? >> I mean, what's the regression line? I mean, the samples said, I think today you'll find machines giving better diagnoses than doctors in some cases. >> Dave: Okay. >> I don't know where the regression line sits today, but if you look at the productivity of doctors going a hundredfold, and the morals scattering around lung cancer, it's impressive. >> Dave: Yeah. >> And do you want a doctor involved? Yes, you do, because part of it is in an orthodoxy of trust which by the way ten years ago, you wouldn't put your credit card online to buy anything, right. It's the same kind of orthodoxy. But I do think that machines can read so much more data, interpolate so many more correlations than people that when you add that to an oncologist for example and cancer, you have a super oncologist capabilities which is really what you're looking for. We're not looking to replace the oncologist per se, what we're looking to do is get the productivity of the oncologist from two to 200. >> I was talking about diagnoses. So you would say yes, okay. >> Yep. >> Will large retail stores mostly disappear in your opinion? >> No, I think they'll change. I think that the customer experience is still, we're still people, we need physical space, and we need physical things to touch, smell, and feel. I think those things will change, but we'll still need experiences. >> I'm going to keep going 'cause Steve's playing along. Will driving and owning your own car become an exception? >> Yes. >> Okay. >> I can elaborate if you want. >> Please, yeah, go ahead. >> So, I mean, the first, I mean, we actually did at KPMG a study called islands of autonomy which modeled LA and San Diego, Atlanta and Chicago, and we modeled how do people move. And we did this for a reason because autonomous vehicles are often times amalgamated as one thing. Oh well autonomous vehicle is coming so you better sell your sports cars and your SUVs, not so fast. The reality is mobility is very different based on where you are. If you're in the middle of Kansas or something, you're going to need a truck to run around in your farm, but if you're in LA or Atlanta or Chicago, you're going to move with autonomy, with autonomous vehicles, and then you're going to really enable mobility as a service very clearly, but differently. The way people move in these cities is different, and if the US auto industry understands those differences, and extrapolates those to a global marketplace, they're going to be very advantaged as mobility as a service becomes real, but the first car that goes, I hate all of the viewers that love this category, but sedan is the first cars to go. I would say sports cars, I race cars, so I love sports cars. People still ride horses today but they don't need them for transportation. And SUVs, right, specialty vehicles that you may, it may not, the economies may not be there, but as we know transportation and car ownership, it's going to change fundamentally, and that's going to have a massive effect on FS, right, insurance companies, banks that are doing loans today. It's going to have a big effect on healthcare. Mobility as a service is going to transcend to healthcare, mobile healthcare in ways that we can't see. >> You got great perspective. I got one more for you, maybe a couple more. Do you think traditional banks will lose control over payment systems? >> Well, a lot of them are already nervous about that, wouldn't you think? >> Yeah, but it hasn't happened yet though. >> I understand, the bottom line is no 'cause I think the traditional banks are getting smarter and they're leveraging their own innovation horsepower to understand things like Blockchain, and how to incorporate those things into their business models. So the answer is I think the way they do, look, banks exist because of one reason, trust. They have trusted brands, right. As long as they can stay current enough to be relevant to your banking needs, you're going to stay with that trusted brand. I think the trick for banks is how do they move fast enough, leverage the technologies that make your life easier, and not waiting three or four days for bank clearing of a check, for example. >> That's they say if you're-- >> And get to that trust in a new way. >> Unless you're a Bitcoin millionaire or a billionaire. >> You still need a bank. >> Maybe somewhere down the line. >> Yeah. >> Okay, last one, I promise. Will robots and maybe even RPA reverse offshore manufacturing advantages? >> Yes. >> Can you elaborate and give us a sense of-- >> I think, first of all, if you really look at what RPA is doing in many ways, is disintermediating the value of geographic location in many ways, right. So where I may have had, again this is important that you understand, so I can still go offshore today and get labor arbitrage and get margin, but I'm not rethinking the business. What I really want to do is own, I want to have more control and I want to have more flexibility and growth in that back office function. So it would behoove when you think about our RPA, and bring in our RPA technology so I have it one onshore, two, leverage the data more securely potentially, and then leverage that data as part of my lake to say how do I use that data to correlate to get to what I really need which is customer relevance at the front office, right. So, look, I think that this whole notion of you're in a different country, and therefore the labor pools are different, and therefore their arbitrage will get benefits from that, those days are over. I mean, it's just a question of when does it die. >> Dave: The data value offsets that arbitrage advantage. >> Well, forget that. The arbitrage is dead itself because the machines, >> Yeah, yeah, right. >> You're talking about orders that have made it to a cheaper per unit cost for an RPA, for a bot to do something than it is for a person that has to eat, sleep, take vacation, and get sick, and all that stuff. And so no matter where they are in the world. So what I would say is that notion is dead. It's just not buried. And overtime we're going to migrate again to machines doing all that robotic stuff. But, again, those people, they're going to do different things. It's not like we're going to see hordes, hundreds of thousands and millions of people not be able to work, I think they're going to be doing different things using their heads in different ways. >> Lisa: I like that answer. >> That's a plan. >> Dave: It's good. >> There's a price somewhere? >> I'm absolutely wrong, I just don't know how wrong, right. >> Well, it's fun to think about, and you provided some context. It was very useful. So, thank you. >> And I imagine folks that are attending your session at IBM Think on Wednesday are going to hear a little bit more into that. So thanks for sharing. >> We going to see some specifics, yeah. >> Thanks for sharing your insights, Steve, and for joining us on theCUBE. You guys, the innovation equation is changing, and I thank you for letting me sit between a very innovative and informative conversation. >> Thank you both. It was fun. >> Thanks Steve. >> For Dave Vellante, I am Lisa Martin. You're watching theCUBE live on Day One of IBM Think 2018. Head over to thecube.net to watch all of our videos with our guests, and siliconanglemedia.com for all the written articles about that. Also check out Wikibon, find out what our analysts are saying about all things digital transformation, Blockchain, AI, ML, et cetera. Dave and I are going to be right back after a short break with our next guest. We'll see you then. (upbeat music)

>> Live from San Francisco, it's theCUBE covering IBM Think 2019. Brought to you by IBM. >> Welcome back, we're here at Moscone North. You're watching theCUBE, the leader in live tech coverage. This is day four of our wall to wall coverage of IBM the Think. The second annual IBM Think, first year at Moscone. Dave Vellante here with Stu Miniman. Eric Herzog is here, he's the CMO of IBM Storage and Sam Werner is the VP of Offering Management for Storage Software at IBM. Guys welcome back to theCUBE. Always good to see ya both. >> Thanks >> Thank you. >> So we were joking yesterday and today, of course multi cloud, the clouds opened, it's been raining, it's been sunny today, so multi cloud is all the rage. Evidently you guys have done some work in multi cloud. Some research that you can share with us. >> Yeah, so couple things. First of all, the storage vision in multi cloud at IBM for years. We work with all the cloud providers including IBM cloud, but we work with Amazon and we work with Azure, we work with Google cloud and in fact our Spectrum Protect, modern data protection product, has about 350 small and medium cloud providers across the world that use it for the engine for their back up as a service. So we've been doing that for a long time, but I think what you're getting is, what we found in a survey multi cloud and I actually had had a panel yesterday and all three of my panelists, including Aetna, use a minimum of five different public cloud providers. So what we're seeing is hybrid is a subset of that, right? On and off, but even if someone is saying, I'm using cloud providers, they're using between five and 10, not counting software as a service because many of the people in the survey didn't realize software as a service is theoretically a type of cloud deployment, right? >> So that's obviously not just the big three or the big five, we're talking about a lot of small guys. Some of the guys maybe you could have used in your Spectrum Protect for back up, local cloud providers, right? And then add sas to that, you could probably double or triple it, right? >> Right, well we've have been very successful with sas providers so for example, one of people on the panel, a company called Follett, they're a privately held, in the mid close to a billion dollars, they provide services to universities and school districts and they have a software package for universities for the bookstores to manage the textbooks and another software as a service for school districts across the United States. They have 1,500 and it's all software service. No on prem licensing and that's an example. That's in my mind, that's a cloud deployment, right? >> Ginni talked Tuesday about chapter two how chapter one was kind of, I call it commodity cloud, but you know, apps that are customer facing, chapter two, a lot of chapter two anyways, is going to be about hybrid and multi cloud. I feel like to date it's largely been, not necessarily a purposeful strategy to go multi cloud, it's just we're multi vendor. Do you see customers actually starting to think about a multi cloud strategy? If so, what's behind that and then more specifically, what are you guys doing from a software stand point to support that? >> Yeah, so in the storage space where we are, we find customers are now trying to come up with a data management strategy in a multi cloud model, especially as they want to bring all their data together to come up with insights. So as they start wanting to build an AI strategy and extend what they're doing with analytics and try to figure out how to get value out of the data they're building a model that's able to consolidate the data, allow them to ingest it and then actually build out AI models that can gain insights from it. So for our software portfolio, we're working with the different types of service providers. We're working closely with all the big cloud providers and getting our software out there and giving our customers flexible ways to move and manage their data between the clouds and also have clear visibility into all the data so they can bring it together. >> You know, I wonder sort of what the catalyst is there? I wrote an article that's going up on SiliconANGLE later and I talked about how the first phase was kind of tire kicking of cloud and then when the down turn hit, people went from capex to opex. It was sort of a CFO mandate and then coming out of the down turn, the lines of business were like, whoa agility, I love this. So shadow IT and then IT sort of bought in and said, "we got to clean up this mess." and that seems to be why, at least one catalyst, for companies saying, "hey, we want a single data management strategy." Are you seeing that or is there more to it? >> Well I think first of all, we're absolutely seeing it and there's a lot of drivers behind it There's absolutely IT realizing they need to get control over this again. >> Governance, compliance, security, edix >> And think about all the new regulations. GDPR's had a huge impact. All a sudden, these IT organizations need to really track the data and be able to take action on it and now you have all these new roles in organizations, like data scientists who want to get their hands on data. How do you make sure that you have governance models around that data to ensure you're not handing them things like pi? So they realized very quickly that they need to have much better control. The other thing you've seen is, the rise of the vulnerabilities. You see much more public attacks on data. You've seen C level executives lose their jobs over this. So there's a lot more stress about how we're keeping all this data safe. >> You're right. Boards are gettin' flipped and it's a big, big risk these days >> Well the other thing you're seeing is legal issues. Canada, the data has to stay in Canada. So if you're multi national and you're a Japanese company, all your Canadian offices, the data has to be some cloud of ours got an office in Canada. So if you're a Japanese headquarter company, using NTT cloud, then you got to use IBM or Amazon or Azure, 'cause you have to have a data center inside the country just to have the cloud data. You also have shier maturity in the market. I would argue, the cloud used to be called the web and before it was the web, it was called the internet and so now that you're doing that, what happens in the bigger companies, procurement is involved, just the way they've been involved in storage servers and networking for a long time. Great you're using CISCO for the network. You did get a quote from HP or using IBM storage, but make sure you get at least one other quote so as that influences aside from definitely getting the control is when procurement get involved, everything goes out for RFP or RFQ or at ten dure, as they say in Europe and you have to have multiple vendors and you sometimes may end up for purely, we need the way to club 'em on price so we need IBM cloud and Microsoft so we can keep 'em honest. So when everyone rushed the cloud, they didn't necessarily do that, but now that it's maturing >> Yeah, it's a sign of maturity. >> It's a sign of maturity that people want to control pricing. >> Alright, so one of the other big themes we've been talking a lot about this week is AI. So Eric talks about, when we roll back the clock, I think back to the storage world, we've been talking about intelligence in storage for longer than my career. So Sam, maybe you can tell us what's different about AI in storage than the intelligence we've been talking and what's the latest about how AI fits into the portfolio? >> Yeah, that's a great question and actually a lot of times we talk about AI and how storage is really important to make the data available for AI, but we're also embedding AI in our storage products. If you think about it, if you have a problem with your storage product, you don't just take down one application. You can take down an entire company, so you've got to make sure your storage is really resilient. So we're building AI in that can actually predict failures before they happen so that our storage never takes any outages or has any down time. We can also predict by looking at behavior out in the network, we can predict or identify issues that a host might be causing on the network and proactively tell a customer before they get the call that the applications are slowing down and we can point out exactly which host is causing the problem. So we're actually proactively finding problems out on the storage network before they become an issue. >> Yeah and Eric, what is it about the storage portfolio that IBM has that makes it a good solution for customers that are deploying AI as an application in use cases? >> Yeah so we look at all, so one is AI, in the box if you will, in the array and we've done a ton of work there, but the other is as the underlying foundation for AI workloads and applications so a couple things. Clearly, AI often is performance dependent and we're focused on all flash. Second thing as Sam already put it out, resilience and availability. If you're going to use AI in an automotive factory to control the supply chain and to control the actual factory floor, you can't have it go down because they could be out tens of millions, hundreds of millions of year just for that day of building Mercedes or Toyotas or whatever they're building if you have an automated factory. The other areas we've created what we call, the data pipeline and it involves three, four members of our storage software family. Our Spectrum Scale, a highly parallel file system that allows incredible performance for AI. Our Spectrum Discover which allows you to use meta data which is information about the data to more accurately plan and the AI software from any vendor can use an API and go in and see this meta data information to make the AI software more efficient that they would use. Our IBM Cloud Object Storage and our Spectrum Archive, you have to archive the data, but easily bring it back because AI is like a human. We are, smart humans are learning non-stop, whether you're five, whether you're 25, or whether you're 75, you're always learning. You read the newspaper, you see of course theCUBE and you learn new things, but you're always comparing that to what you used to know. Are the Russians our friends or our enemies? It depends on your point in time. Do we love what's going on in Germany? It depends on your point in time. In 1944, I'd say probably not. Today you'd say, what a great Democratic country, but you have to learn and so this data pipeline, this loop, our software is on our storage arrays and allows it to be used. We'll even sell the software without our storage arrays for use on any AI server platform, so that softwares really the huge differentiator for us. >> So can you, as a follow up to that, can you address the programmability of your portfolio? Whether it's through software or maybe the infrastructure as well. Infrastructure, I'm thinking infrastructure's code. You mentioned you know API's. You mentioned the ability to go into like Spectrum Discover for example, access meta data. How programmable is your infrastructure and how are you enabling that? >> I mean across our entire portfolio, we build restful API's to make our infrastructure completely extensible. We find that more and more enterprises are looking to automate the deployment of the infrastructure and so we provide API's for programming and deploying that. We're also moving towards containerizing most of our storage products so that as enterprises move towards cubernetes type clusters, we work with both Red Hat and with our own ICP and as customers move towards those deployment models and automate the deployment of their clusters, we're making all of our storage's available to be deployed within those environments. >> So do you see an evolution of the role of a storage admin, from one that's sort of provisioning luns to one that's actually becoming a coder, maybe learning Python, learning how to interact through API's, maybe even at some point developing applications for automation? Is that happening? >> I think there's absolutely a shift in the skills. I think you've got skills going in two directions. One, in the way of somebody else to administer hardware and replace parts as they fail. So you have lower skilled jobs on that side and then I believe that yes, people who are managing the infrastructure have to move up and move towards coding and automating the infrastructure. As the amount of data grows, it becomes too difficult to manage it in the old manual ways of doing it. You need automation and intelligence in the storage infrastructure that can identify problems and readjust. For example, in our storage infrastructure, we have automated data placement that puts it on the correct tier. That use to be something a storage administrator had to do manually and figure out how to place data. Now the storage can do it themselves, so now they need to move up into the automation stack. >> Yeah, so we've been talking about automation and storage also for a lot of years. Eric, how are enterprises getting over that fear that either I'm going to lose my job or you know, this is my business we're talking about here. How do I let go and trust? I love, I saw downstairs, there was a in the automation booth for IBM, it was free the humans, so we understand that we need to go there. We can't not put automation with the scale and how things are moving, but what's the reality out in the field? >> So I think that the big difference is and this is going to sound funny, but the economic down turn of seven, eight and nine, when downturn hit and certainly was all over the IT press, layoff, layoff, layoff, layoff, layoffs, so we also know that storage is growing exponentially, so for example, if I'm Fortune 500 company x and I had 100 people doing storage across the planet. If I laid off 50 of them and now I'm recovered. I'm making tons of money, my IT budget is back up. I didn't go to the CIO and say, you can hire the 50 storage people back. You can hire 50 people back, but no more than five or six can be storage people. Everything else has to be dev ops or something else. So what that means is, they are managing an un-Godly amounts of more storage every year with essentially the same people they had in 2008 or maybe a tiny bit more. So what matters is, you don't manage a peta bite or in the old days, half a peta bite. Now, one storage admin or back up admin or anyone in that space, they want you to manage 20 peta bites and if you don't have automation, that will never happen. >> Stu and I were interviewing Steven Hill from KPMG yesterday and he was talking about the macro numbers show we're not (stutters) as globally and even in the US, we're not seeing productivity gains. I'm saying yeah, you're not looking at the storage business you know, right? Because if you look at anybody who's running storage, they're doing way more with much less, to your point. >> Which is why, so for example when Sam talked about our easy tier, we can tier, not only as AI base. So in the old days, when you guys weren't even born yet, when I was doing it. >> Well I don't know about that >> What was it? It was move the data after 90, so first it was manual movement, then it was set up something, a policy. Remember policy automation was the big deal 10 years ago? Automatically move the data when its 90, 60, or 30 days old. AI based, what we have an easy tier, automatically will determine what tier it should go on, whether when the data's hot or when the data's cold and on top of that, because we can tier over 440 arrays that are not IBM logo'd, multi vendor tiering, we can tier from our box to an EMC box. So if you have a flash array, you've got an old or all hard drive that you've moved into your back up in archive tier, we can automatically tier to that. We can tier from the EMC array out to the Cloud, but it's all done automatically. The admin doesn't do anything, it just says source and target and the AI does all the work. That's how you get the productivity that you're talking about, that you need in storage and back ups even worse because you got to keep everything now, which Sam mentioned GDPR, all these new regulations and the Federal Government its like keep the data forever. >> But in that case, the machine can determine whether or not it's okay to put it in the Cloud, if it's in Canada or Germany or wherever, the machine can adjudicate and make those decisions. >> And that's what the AI, so in that case you're using AI inside of the storage system versus what we talked about with our other software that makes our storage systems a great platform for other AI workloads that are not, if you will, AI for storage. AI for everything else, cars or hospitals or resume analysis. That's what the platform can, but we put all this AI inside of the system 'cause there aren't that big, giant, global, Fortune 500 has 55 storage admins and in 2007 or eight, they had 100, but they've quintupled the amount of storage easily if not 10x'd it, so who's going to manage that? Automation. >> Guys, good discussion. Not everyday, boring, old storage. It's talking about intelligence, real intelligence this time. Eric, Sam, thanks very much for coming to theCUBE. Great to see you guys again. >> Thank you. >> Thank you. >> You're welcome. Alright, keep it right there everybody. Stu and I will be back with our next guest shortly, right after this break. John Furrier is also here. IBM Think, Day four, you're watching theCUBE. Be right back. (tech music)