>>Hello and welcome to the Cube's coverage here at AWS Reinvent 2022. This is the Executive Summit with Accenture. I'm John Furry, your host of the Cube at two great guests coming on today, really talking about the future, the role of humans. Radically human is gonna be the topic. Paul Dardy, the group Chief Executive Technology and CTO at Accenture. And Jim Wilson, global managing director of thought Leadership and Technology research. Accenture. Gentlemen, thank you for coming on the cube for this conversation around your new hit book. Radically human. >>Thanks, John. It's great to, great to be with you and great, great to be present at reinvent. >>You know, we've been following you guys for many, many years now, over a decade. You always have the finger on the pulse. I mean, and as these waves come in, it's really important to understand impact. And more than ever, we're kind of in this, I call it the systems thinking, revolutions going on now where things have consequences and, and machines are now accelerating their role. Developers are becoming the front lines of running companies, seeing a massive shift. This new technology is transforming the business and shaping our future as, as humans. And so I love the book. Very, very strong content, really. Right on point. What was the motivation for the book? And congratulations. But, you know, I noticed you got the, the structure part one and part two, This book seems to be packing a big punch. What's, what was the motivation and, and what was some of the background in, in putting the book together? >>That's a great question, John, and I'll start, and then, you know, Jim, my co-author and, and part colleague and partner on this, on the book and join in too. You know, the, if you step back from the book itself, we'd written a first book called, you know, Human Plus Machine, which talked about the, you know, focused a lot on artificial intelligence and talked about the potential and future of artificial intelligence to create a more human future for us with the Human plus machine pairing. And then, you know, when we started, you know, working on the next book, Covid was, you know, it was kinda the Covid era. Covid came online as, as we were writing the book. And, but that was causing really an interesting time in technology for a lot of companies. I mean, think back to what you were doing, you know, once Covid hit, every company became more dependent on technology. >>Technology was the lifeline. And so Jim and I got interested in what the impacts of that were on companies ba, you know, and what was different from the first, you know, research we had done around our first book. And what we found, which was super interesting, is that, is that, you know, pre pandemic, the, the leading companies, the digital leaders that were applying cloud data, AI, and related technologies faster, we're outperforming others by a factor of two x. And that was before the pandemic. After the pandemic. We redid the research and the gap widen into five x. And I think that's, and, and that's kind of played a lot into our book. And we talk about that in the opening of our book. And the message message there is exactly what you said is technology is not just the lifeline, you know, from the pandemic, but now technology is the heart and soul of how companies are driving innovation, how they're responding to global crises around, you know, inflation energy, supply chain crisis because of the war in Ukraine, et cetera. >>And companies need the technology more than ever. And that's what we're writing about in, in Radically Human. And we're taking a step beyond our previous book to talk about what we believe is next. And it's really cloud data and ai and the metaverse that signal out is three trends that are really driving transformative change for companies. And the first part of the book, to your question on the structure, talks about the roadmap to that. We talked about the ideas framework, five areas where you need to change your thinking, flip your assumptions on how to apply technology. And then the second part of the book talks about the differentiators that we believe are gonna set companies apart as they look to, you know, to implement this technology and transform their companies for the future. >>Jim, weigh in on this. Flipping the script, flipping the assumptions. No, >>You, you, you used a really important word there, and that is systems. I think when we think about artificial intelligence, and when Paul and I have now talking to companies, a lot of executives think of AI as kind of a point solution. They don't think of about AI in terms of taking a systems approach. So we were trying to address that, all right, if you're gonna build a roadmap, a technology roadmap for applying intelligent technologies like artificial intelligence, how do you take a holistic systematic view? And that's really the, the focus of the first section of the book. And then as Paul mentioned, how do you take those systems and really differentiate using your talent, focusing on trust, experiences and sustainability. >>You know, I like this, I like how it reads. It's almost like a masterclass book because you kind of set the table. It's like, cuz people right now are like in the mode of, you know, what's going on around me. I'm been living through three years of covid. But coming out the other side, the world looks radically different. Humans are much more important. Automation's great, but people are finding out that the human's key, but people are trying to figure out where I am, where am I today. So I think the first part really to me hits home, like, here's the current situation and then part two is, here's how you can get better. And it's not just about machines, machines, machines and automation, automation, automation. We're seeing examples where the role of the human, the person in society, whether it's individually or as part of a group, are really now key assets in that kind of this new workforce or this new production system or you know, society. >>Yeah. And just to take a couple examples from the book and highlight that, I think you're exactly right. And that's where, you know, radically human, you know, the title came from. And you know, the, what's happening with technology is that technology itself is becoming more human like in its capability. When you think about the power of the transformer technologies and other things that we're reading about a lot and, and that, you know, the whole hypothesis, you know, or premise of the book I should say, is that the more humanlike the technology is, the more radically human or the more radical the, you know, the, the the, the human potential improvement is the more, the bigger the opportunity. It's pairing the two together rather than, as you said, just looking at the automation or the machine side of it. That's really the radical leap. And one thing Jim and I, you know, talk about, you know, talked about, you know, talked about in context of the book is companies really often haven't been radical enough in applying technology to really get to dramatic gains that they can get. >>Just a couple examples from the ideas framework, the eye and ideas is each of the, the ideas framework is the first part of the book, The five areas to flip your Assumptions, The eye stands for intelligence. And we're talking about more, more human and less artificial in terms of the intelligence techniques, things like common sense learning and other techniques that allow you to develop more powerful ways of engaging people, engaging humans in the systems that we build, using the kind of systems thinking that Jim mentioned. And you know, things like emotional ai, common sense ai, new techniques in addition to machine the big data driven machine learning techniques which are essential to vision and solving big problems like that. So that's, that's just an example of, you know, how you bring it together and enable that human potential. >>I love the, we've been, >>We've >>Go ahead Jim. >>I was gonna say we've been used to adapting to technology, you know, and you know, contorting our fingers to keyboards and and so on for a long time. And now we're starting to see that technology is in fact beginning to adapt to us and become more natural in many instances. One point that we make is now in the human technology nexus. In fact, the human is in the ascended. That's one of the, one of the big ideas that we try to put out there in this book. >>You know, I love the idea of flipping the script, flicking assumptions, but, but ideas framework is interesting. I for intelligence, D for data, E for expertise, A for architecture, s for strategy, notice the strategies last. Normally in the old school days, it's like, hey, strategy first and execution really kind of interesting kind of how you guys put that together. It kind of feels like business is becoming agile and iterative and it's how it's gonna be forming. Can you guys, I mean that's my opinion, but I think, you know, observing how developers becoming much more part of, of the app. I mean, if you take digital transformation to its conclusion, the application is the company, It's not a department serving the business, it is the business, therefore developers are running the business, so to speak. This is really radical. I mean, this is kind of how I'm seeing it. What's your reaction to that? Do you see similar parallels to this transformation? If you take it down to a conclusion, strategy is just what you do after you get the outcomes you need. Is that, can you, what's your reaction to that? >>Yeah, yeah, I think, I think one of the most lasting elements of the book might be that chapter on strategy in, in my opinion, because you need to think about it differently. The old, old way of doing strategy is dead. You can't do it the way you used to do it. And that's what we tried to, you know, to lay out with the, the essence ideas, you know, the strategy and the, the, the fun. You know, the, the subtitle that chapter is is we're all technology companies now. And if you're a technology driven company, the way you need to think about and every company is becoming, That's what I hear when I talk to these suites and CEOs and boards, is everybody's recognizing the essential world that technology plays and therefore they need to, to master technology, well, you need to think about strategy differently than because of the pace of technology innovation. >>And so you need to throw out the old way of doing it. We suggest three new archetypes of how to do strategy that I think are really report it's about continuous strategy in all cases. Yet an example is one of the techniques we talk about forever beta, which is, you know, think about a Tesla, you know, companies that, you know, it's never quite done. They're always improving and the product is designed to be connected and improving. So it changes along, you know, the product and the strategy along how you deploy it to consumers changes as you go. And that's an example of a very different approach to strategy that we, we believe is essential to consider as you look at the future. Yeah, those multi-month strategy sessions, you know, might play out over two or three quarters of going away. And strategy and execution are becoming almost simultaneous these days. As Paul was saying, >>It's interesting because that's the kind of the trend you're seeing with more data, more automation. But the human plays a much critical role. And, and just as a side on the Tesla example, you know, is well documented, I think I wrote about in a post just this week that during the model three Elon wanted full automation and had to actually go off script and get to humans back in charge cuz it wasn't working properly. Now they have a balance. But that brings up the, the part two, which I like, which is, you know, this human piece of it, you know, we always talk about skills gaps, there's not enough people to do this, that and the other thing. And talent was a big part of that, that second half, you know, trust, talent experiences, that's the more the person's role, either individually as part of a collective group is talent. The scarce resource now where that's the, that's the goal, that's the, the key because I mean, it all could point to that in a way, you know, skills gap kind of points to, hey, you know, humans are valuable, in fact the value's going up if it's properly architected. What's your reaction to that, guys? Because I think, you know, that's something that is not kind of nuance point, but it's a feature, not a bug maybe, I don't know. What's your thoughts? >>Yeah, it's, go ahead Jim. I was gonna say it, you know, we're, we're dramatically underestimating the amount of focus we need to put on talent. That's why we start off that second part of the book. You know, really zooming in on talent. I think, you know, you might think that for every, you know, a hundred dollars that you put into a technology initiative, you know, you might put 50 or 75 into reskilling initiatives to really compliment that. But what we're seeing is companies need to be much more revolutionary in their focus on talent. We saw a, a economic analysis recently that pointed out that for every $1 you spend on technology, you are likely gonna need to spend about $9 on intangible human capital. That means, you know, on talent, on, on getting the best talent on reskilling and on changing processes and work tasks. So there's a lot of work that needs to be done. Really that's human focus. It's not just about adopting the technology. Certainly the technology's critical, but we're underestimating the amount of focus that needs to go into the talent factors. >>That's a huge point. >>I think some of the elements of talent that become really critical that we, we talked about in the book are, are becoming a talent creator. We believe that the successful companies of the future are gonna be able not, not just to post, you know, post a job opening and hire, hire people in because there's not gonna be enough. And a lot of the jobs that companies are creating don't exist, you know, cause the technology changing so fast. So companies that succeed are gonna know how to create talent, bring in people, apprentices and such and, and, and, you know, shape to tail as they go. We're doing a significant amount of that in our own company. They're gonna be learning based organizations where you'll differentiate, you'll get the best employees if you provide better learning environments because that's what you know, employees want. And then democratizing access to technology, You know, things like, you know, Amazon's honey code is an example, you know, low code, no code development to spread, you know, development to wider pools of people. Those types of things are really critical, you know, going forward to really unlock the talent potential. And really what you end up with is, yeah, the, the human talent's important, but it's magnified to multiplied by the power of people, you know, giving them in essence superpowers in using technology in new >>Ways. I think you nailed it, That's super important. That point about the force multiplier, when you put things in combination with it's group constructs, two pizza teams, flexing, leveraging the talent. I mean, this is kind of a new configuration. You guys are nailing it there. I love that piece. And I think, you know, groups and collectives, you're gonna start to see a lot more of that. But again, with talent comes trust when you start to have these kind of, you know, ephemeral and or forming groups that are forming production systems or, or, or experiences. So trust comes up a lot. You guys see the metaverse as an important part there. Obviously Metaverse is a pretext to the virtual world where we're gonna start to create these group experiences and create new force multipliers. How does the Metaverse play into this new radically human world and and what does it mean for the future of business? >>Yeah, I think the Metaverse is radically, you know, kind of misunderstood to use the word title, word of a, when we're not with the title of our book, you know, and we believe that the metaverse does have real big potential, massive potential, and I think it'll transform the way we think about digital more so than we've changed our thinking on digital in the last 10 years. So, you know, that that's the potential of the metaverse. And it's about, it's not just about the consumer things, it's about metaverse in the enterprise. It's about the new products you create using distributed ledger and other technologies. And it's about the industrial metaverse of how you bring digital twins and augmented workers online in different ways. And so I, I believe you know that it is, has tremendous potential. We write about that in the book and it really takes radically human to another level. >>And one way to think about this is cloud is really becoming the operating system of business. You, you have to build your enterprise around the cloud as you go forward that's gonna shape the way you do business. AI becomes the insight and intelligence in how you work, you know, in infused with, you know, the human talent and such as we said. And the metaverse then reshapes the experience layers. You have cloud AI building on top of this metaverse providing a new way to, to generate experiences for, for employees, citizens, consumers, et cetera. And that's the way it unfolds. But trust becomes more important because the, just as AI raises new questions around trust, you know, every technology raises new questions around trust. The metaverse raises a whole new set of questions. And in the book we outline a five part framework or or five, you know, essential, you know, parts of the framework around how you establish trust as you implement these new technologies. >>Yeah, we're seeing that, you know, about three quarters of companies are really trying to figure out trust, you know, certainly with issues like the metaverse more broadly across their it, so they're, you know, they're focusing on security and privacy transparency, especially when you're talking about AI systems. Explainability. One of the, you know, the more surprising things that we learned when doing the book, when we're doing the research is that we saw that increasingly consumers and employees want systems to be informed by kind of a sense of humanity. So one company that we've been looking at that's been developing autonomous vehicles, self-driving car systems, the, they're, they're actually training the system by emulating human behavior. So kind of turning the cameras on test drivers to see how they learn and then training the AI kind of using that sense of humanity cuz you know, the other drivers on the road find human behavior more trustworthy. And similarly, that system is also using explainable AI to actually show which human behaviors that that AI system is learning from or some really interesting innovations kind of happening in that trust space. John, >>Jim, I think you bring up a great point that's worth talking more about because you know, you're talking about how human behaviors are being put into the, the design of new things like machines or software. And we're living in this era of cloud scale, which is compressing this transformation timeline and you know, we've been calling it super cloud, some call it multicloud, but it's really a new thing happening where you're seeing an acceleration of the transformation. We think it's gonna happen much faster in the next five to 10 years. And so that means these new things are emerging, not just, hey, I'm running a virtual event with Chad and some video, you know, it's, it's group behavior, it's group con groups, convening, talking, getting things done, you know, debating doing things differently. And so this idea of humans informing design decisions or software with low code no code, this completely changes strategy. I mean this is a big point of the book. >>Yeah, no, I go back to, you know, one of the, the, the, the e and the ideas frameworks is expertise. And we talk about, you know, from machine learning to machine teaching, which, which is exactly that, you know, it's, you know, machine learning is, you know, maybe humans tag data and stuff and feed into algorithms. Machine teaching is how do you really leverage the human expertise in the systems that you develop with ai? One of the examples we give is one of the, the large consumer platforms that uses human designers to give the system a sense of aesthetic design and product design. A very difficult thing, especially with changing fashion interest and everything else to code in algorithms and to even have AI do, even if you have fast amounts of data, but with the right human insight and human expertise injected in, you can create, you know, amazing new capability that responds to consumers in a much more powerful way. And that's an example of what you just said, John, bringing the two together. >>Well you, what's interesting is that I wanna to get your thoughts as we can wrap up here soon. How do you apply all these human-centric technologies to the future of business? As you guys talk to leaders in, in the enterprise of their businesses, as they look at the horizon, they see the, the future, they gotta start thinking about things like generative AI and how they can bring some of these technologies to the table where, you know, we were, we were talking about if open source continues to grow the way it's going, there might not be any code to write, it just writes itself at some point. So you got supply chain issues with security. These are, these are new things you guys are hitting on this in the book where these are new dynamics, new power dynamics in how things get built. So if you're a business owner and leader, this is a new opportunity, a challenge, certainly that is an opportunity. How, how do you apply all this stuff for, for business >>Now? I'll go first then Jim Canad. But the, the first thing I think starts with, with recognizing the role that technology does play and investing accordingly in it. So the right, you know, technology, talent, you know, rethinking the way you do strategy as we talked about earlier and recognizing how you need to build a foundation. That's why, you know, the fact you're at reinvent is so important because companies are, you know, again rebuilding that, that operating system of their business in the cloud. And you need that, you know, as the foundation to go forward, to do, you know, to, to build the other, other types of capabilities. And then I think it's developing those talent systems as well. You know, do you, do you have the right the, do you have the right talent brand? Are you attacking the right, attracting the right employees? Are you developing them in the right way so that you have the right future talent going forward and then you marry the two together and that's what, you know, gives you the radically human formula. >>Yeah. When, you know, when we were developing that first part of the book, Paul and I did quite a bit of, of research, and this was ju and Paul kind of alluded to that research earlier, but one of the things that we saw in really the first year of the pandemic was that there was a lot of first time adoption of intelligent technologies like artificial intelligence. You know, one statistic is that 70% of, there was a, there was a of companies that had never tried AI before, went ahead and tried it during the pandemic. So first time adoption rates were way up, but the thing is companies are not, or we're not trying to do it themselves and to, you know, to necessarily, you know, build an it, a AI department. They were partnering and it's really important to, to find a partner, often a cloud partner as a way to get started, start small scale and then scale up doing experiments. So that was one of the, that was one of the key insights that we had. You don't need to do it all yourself. >>If you see the transformation of just aws, we're here at reinvent just since we've been covering the events since 2013, every year there's been kind of a thematic thing. It was, you know, startups, enterprise now builders and now, now change your company this year it's continuing that same thing where you're starting to see new things happen. It's not just lift and shift and, and running a SaaS application on the cloud. People are are changing and refactoring and replatforming, categorical applications in for this new era. And you know, we're calling it super cloud super services, super apps cuz they're different. They're doing different things in leveraging large scale CapEx, large scale talent pools or talent pools in certain ways. So this is real, something's happening here and you know, we've been talking about a lot lately, so I have to ask you guys, how does a company know if they're radical enough? Like when, what is radical? How do, how can I put a pin in that say that could take a temperature or we like radical enough what some tell signs can you guys share for companies that are really leaning into this new next inflection point because there are new things happening. How do you know if you're, you're you're pushing the envelope radical enough to, to take advantage? >>Yeah, I think one, yeah, I was gonna say one of the, one of the tests is is you know, the impact on your business. You have to start by looking at all this in the context of your business and is it really taking you to another level? You said it perfectly, John, it used to be we used to talk about migration and workloads to the cloud and things like that. Yeah. That that's still something you need to do. But now we, our focus, you know, with a lot of our customers is on how do you innovate and grow your business in the cloud? What's, what is, you know, how, how, what's the platform you know, that you're using to, you know, for your, the new digital products and services you're offering to your consumers. I mean it is the business and I think that's the test. Whether being radical, you know, radical enough is on the one hand, is this really, are you really using the technology to drive differentiation and real growth and change in your business? And are you equipping, you know, people, your human talent with the capabilities they need to perform in very different ways? And those are the the two tests that I would give. Totally agree. >>Yeah. You know, interesting enough, we, you know, we, we love this topic and guys, again, the book is spot on. Very packs a big punch on content, but very relevant in today. And I think, you know, one of the things we're looking at is that people who do things differently take advantage of some of these radical approaches like ideas your framework and understand where they are and what's available and what's coming around the corner. They stand out in the, in the pack or create new business opportunities because the CapEx is taken care of. Now you got your cloud, I mean some, you're building clouds on top of clouds or, or something's happening. You can, I think you see it like look at like companies like Snowflake, it's a data warehouse on the cloud. What does that mean? They didn't build a cloud, they used Amazon. So you're starting to see these new things pop up. >>Yeah, and that's a good example and it sounds like a simple thing, data warehouse in the cloud, but the new business capability that a technology like that allows the portability of being able to connect and use data across cloud environments and such is, is, is is tremendously powerful. And I think that's why, you know, you talk about companies doing things differently, that's why it's great again that you're at reinvents. If you look at the index of our book, you'll see, you'll see AWS mentioned a number of times cuz we tell a lot of cus of cus customer and company stories about how they're leveraging aws, AWS capabilities in cloud and AI to really do transformative things in your, in their business. And I, I think that's what it's, that's what it's all about. >>Yeah, and one of the things too in the book, it's great cuz it has kind of a, the systems thinking it's got really relevant information but you know, you guys have seen the, seen the movie before. I think one of the wild cards in this era is global. You know, we're global economy, you've got regions, you've got data sovereignty, you're seeing, you know, all kinds of new things, emerging thoughts on the global impact cuz you, you take your book and you overlay that to business. Like you gotta, you gotta operate all over the world as a human issue. It's a geography issue. What's your guys take on the global impact? >>Well that's, that's why the, the, you gotta think about cloud as as one technology, you know, we talked about in the book and cloud is a lot, I think a lot of people think, well clouds it's almost old news. Maybe it's been around for a while. As you said, you've been going to reinvent since 2013. You know, cloud is really just getting, you know, just getting started. And, and it's cuz the reasons you said, when you look at what you need to do around sovereign cloud capability, if you're in Europe for many companies it's about multi-cloud capabilities. You need to deploy, you know, differently in different, in different regions. And they need to, in some cases for good reason, they have hybrid, hybrid cloud, you know, capability that they, they match on their own. And then there's the edge capability which is comes into play in, in different ways. >>And, and so the architecture becomes very complex and we talk the A in and ideas is architecture. We talk about all this and how you need to move from the old conception of architecture, which was more static and mod and you know, just modularity was kind of the key thing you thought about. It's more the idea of a living system, of living architecture that's, that's expanding and is what's much more dynamic. And I think that's the way you need to think about it as you manage in a global environment today with the, with the pace of technology advancement. >>You know, the innovation is here. It's not stopping. How do you create some defacto standards while not stunting the innovation is gonna be a big discussion as these new flipped assumptions start to generate more activity. It's gonna be very interesting to watch. Gentlemen, thank you so much for spending the time here on the queue as we break down your new book, Radically Human and how it, how business leads can flip the script on their business assumptions and put ideas and access to work. This is a big part of the cloud show at reinvent. Thanks so much for, for sharing and congratulations on a great book. >>You know, Thanks John. And just one point I'd add is that one of the, the things we do talk about in talent is the need to reskill talent. You know, people who need to, you know, be, be relevant to the rapidly changing future. And that's one area where I think we all as institutions, as communities and individuals need to do more is to help those who need to reskilling. And the final point I mentioned is that we mentioned at the end of the book that all proceeds for the book are being donated to not NGOs and nonprofits that are focused on reskilling. Those who need a skill refresh in light of the radically human new, you know, change in technology that's happening >>Great by the book proceeds go to a great cause and it's a very relevant book if you're in the middle of this big way that's coming. This is a great book. There's a guidepost and also give you some great ideas to, to reset re flip the scripts. Refactor, re-platform. Guys, thanks for coming on and sharing, really appreciate it. Again, congratulations. >>Thanks, John. John, great discussion. >>Okay, you're watching the Cube here, covering the executive forum here at AWS Reinvent 22. I'm John Furrier, your host with aen. Thanks for watching.

(upbeat pop music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CubeConversation. >> Hello, everyone welcome to this CUBE Conversation here in Palo Alto, California, I'm John Furrier, co-host of theCUBE. We are here forth Inhi Cho Suh General Manager of IBM Watson, Customer Engagement, Former Cube alumni, I think she's been on dozens of times. Great to see you again. Welcome to our Palo Alto Studios. >> Yeah, great being here, John. >> So, we haven't chatted in awhile. IBM thing just happened, a little bit of a rainy event, here in February. Interesting change over since we last talked, but first give an update on what you're up to these days, what group are you leading, what's new? >> Okay, well first of all, I'm here based in California, which I'm excited about, and I lead our Watson West office, which is our Watson headquarters, here on the west coast, in downtown San Francisco, and we hosted our Think Conference, and at Think I lead with, in IBM, what we call our Watson Customer Engagement Business Unit, which is really the business applications, of how we apply Watson and other disruptive tech to a line of business audiences, both SAS and on premise software, so really excited about the areas of applying AI and machine learning as well as Blockchain to things like supply chain, and logistics, to order management, to next generation of retail. A lot of new, exciting areas. >> Yeah, we've had many conversations over the years from big data to as your career spanned across IBM, and you have a much more horizontal view of things, now. You're horizontally scalable, as we say in the cloud world. What's your observation of the trends these days? Because there's a lot waves. Actually, the waves that you guys announced, was the IBM, Watson NE ware and the cloud private ware. Marvin and I had an amazing conversation that video went viral. This is now getting a big tailwind for IBM. What's your thoughts in general about the overall ecosystem, because you're here in Silicon Valley, you've seen the big waves, you've got another big data world, cloud is here, multi cloud. What's your thoughts on the big mega-trends? >> Yeah, that's a good question. I think the first chapter of cloud, everyone ran to public cloud. When you look at it through the lens of enterprise, though, the hot topic right now in the second chapter is really about not just public cloud, but multi-cloud, hybrid cloud. Meaning, whether it's a private, public, it's about thinking about the applications and the nature of the applications and regardless of where the data sits, what are the implications of actually getting work done? Through, kind of, new container services, new ways of microservices in the development, of how APIs are integrated, and so, the hot topic right now is definitely hybrid cloud, multi cloud. And the work we've done to certify, what we call, IBM cloud private really enables us to not just take any business application to any cloud in our cloud, as well, but actually to enable Watson and Watson based applications also across multi cloud environments. >> So, chapter two, Jenny mentioned that in her key notes, I want to dig into that because we've been talking a lot about multi cloud architecture, and one of the big debates has been, in the industry, oh, don't pick a soul cloud. I've been writing a bunch of content about that at this DOD jedi deal with Amazon and Oracle, fighting for it out there, but that's also happening at the enterprise, but the reality is, everyone has multiple clouds. If you've got a sales force or if you've got this and that and the other thing, you probably have multiple clouds, so it's not so much soul cloud vs. as it is, workloads having a cloud for the right job and that seems to be validated at IBM Think, in talking to the top technical people and in the industry. They all say, pick the right cloud for the job. And we've heard that before in Big Data. Pick the right tool for the job. So, given that, workloads seem to be driving the demand for cloud. Since you're on the app side, how are you seeing that? Because the world's flipped. It used to be infrastructure and software enable the app's capabilities. Now the workloads have infrastructure as code, made with cloud, they're driving the requirements. This is a change over. >> It is a big change and part of, I would say, when people first ran to the cloud, and a lot of the public cloud services were digital SaaS services, where people were wanting to stitch multiple applications across clouds, and that became a challenge, so in this next iteration, that I'm seeing is, really, a couple things. One is, data gravity. So, where does the data actually reside, for the workload that's actually happening? Whether it's the transactions, whether it's customer information, whether it's product information, that's one piece. The second piece is a lot more analytics, right? And the spectrum of analytics running from traditional warehouse capabilities, to more, let's say, larger scale big data projects to full blown advanced algorithms and AI applications, is, people are saying, look, not only do I want to stitch these applications across multiple clouds; I also want to make sure I can actually tap into the data to apply new types of analytics and derive new services and new values out of relationships, understanding of how products are consumed, and so forth. So, for us, when we think about it is, we want to be able to enable that fluid understanding of data across the clouds, as well as protect and be thoughtful about the data privacy rights around it, compliance around GDPR, as well as how we think about the security aspects as well, for the enterprise. >> That is a great point. I think I want to drill down on the data piece, your background on data obviously is going to be key in your job now obviously, it's pretty obvious with Watson, but David Floyd, a wiki bonds research analyst, just posted a taxonomy of hybrid cloud research report that laid out the different kinds of cloud you could have. There's edge clouds, there's all kinds of things from public to edge, so when you look at that, you're thinking, okay, the data plain is the critical nature of the cloud. Now, depending on which cloud architecture for the use case, the workload, whatever, the data plain seems to be this magical opportunity. AI is going to have a big part of that. Can you just talk about how you guys see that evolving? Because, obviously, AI is a killer part of your strategy. This data piece is inter-operating across the clouds. >> Yes. >> Data management governs you're smiling, cause there's a killer answer coming. >> Totally. This is such a great set up. Actually, Ginni even said it in her keynote at Think, which was, you can't have an AI strategy without an information architecture strategy, which is an IA strategy, and information architecture is all about what you said: it's data preparation; understanding the foundation of it, making sure you've got the right governance structure, the integration of it, and then actually how you apply the more advanced analytics on top. So, information architecture and thinking about the data aspects in all kinds of data. Majority of the data actually sits behind, what I would say, the traditional public firewall. So, it sits behind the firewalls of our enterprise clients, like 80 plus percent of it, and then, many of the clients, we actually recently did a study, with about 5,000 senior executives, across many, many thousands of organizations, and 85% of them want to apply AI to improve their customer service, to improve the way they engage their clients and their products and services, so this is a huge opportunity right now for pretty much every organization to think through; kind of their data strategy. Their information architecture strategy, as part of their overall AI strategy. >> So, a question a got on twitter comes up a lot, and, also on my notes here, I wanted to ask you is, how can companies increase transparency trust and mitigate bias in AI? Because this comes up a lot and that's the questions that come in from the community is, Hey, I got my site, my apps running in Germany. I've got users over there, I'm global. I have to manage compliance, I got all this governess now, I'm over my shoulders, kind of a pain in the butt, but also I don't want to have the software be skewed on bias and other things, and then, I also get this whole Facebook dynamic going on, where it's like, I don't trust people holding my data. This is a big, huge issue. >> It is enormous. >> You guys are in the middle of it, what's your thoughts, what's the update, what's the dynamic and what's the solution? >> So, this is a big topic. I think we could do a whole episode just on this topic alone. So, trust and developing trust and transparency in AI should be a fundamental requirement across many, many different types of institutions. So, first of all, the responsibility doesn't sit only with the technology vendors; it's a shared responsibility across government institutions, the consumers, as well as the business leaders, in terms of how they're thinking about it. The more important piece, though, is when you think about the population that's available, that really understands AI, and they're actually coding and developing on it, is that we have to think about the diverse population that's participating in the governance of it, because you don't want just one tribe or one group that's coding and developing the algorithms, or deciding the decision models. >> Like the nerds or the geeks; they're a social aspect, society aspect as well, right? Social science. >> Exactly. I actually just did a recent conversational series with Northwestern Kellogg's business school, around the importance of developing trust and transparency, not only in the algorithms themselves, but the methodology of how you think about culture and value and ethics come into play through different lens, depending on the country you live in, as you kind of referenced, depending on your different values and religious backgrounds. It may because of different institutional and/or policy positions, depending on the nature, and so there has to be a general awareness of this that's thoughtful. Now, why I'm so excited about the work we're doing at IBM is we've actually launched a couple new initiatives. One is, what we call, AI OpenScale, which is really a platform and an opportunity to have the ability to begin to apply AI, see how AI operations and models function in production. We have methodologies in terms of engaging understanding fairness, so there's a 360 degree fairness kit, which is actually available in the open source world, there's a set of tools to understand and train people on recognizing bias, so even just definitions of, what do you mean by bias? It could be things like, group think, it could be, you're just self selecting on certain data sets to reinforce your hypotheses, it could be unconscious levels and it's not just traditionally socially oriented, types of bias. >> It could be data bias, too. It could be data bias, right? >> Totally. Machine generated biases in IOT world, also. >> So, contextual and behavioral biases kind of kick into play here. >> Yeah, but it starts with transparency trust. It also starts with thoughtful governance, it starts with understanding in your position on policy around data privacy, and those things are things that should be educational conversations across the entire industry. >> How far along are we on the progress bar there? I mean, it seems like it's early and we seem to be talking for awhile, but it seems even more early than most people think. Still a lot more work. Your thoughts on where the progress bar is on this whole mash up of tech and social issues around bias and data? Where are we? >> We're really at the early stages, and part of the reason we're at the early stages is I think people have, so far, really applied AI in very simple task oriented applications. The more, what we call, broad AI, meaning multi task work flow applications are starting, and we're also starting seeing in the enterprise. Now, in the enterprise world, you can still have bias, so, for example, when you talked about data bias, one of the simple examples I use is, think about loan approvals. If one of the criteria may be based on gender, you may have a sensitivity around the lack of women owned business leaders, and that could be a scoring algorithm that says, hey, maybe it's a higher risk when in fact, it's not necessarily a higher risk, it's just that the sampling is off, right. So, that would be a detection to say, hey maybe you have sensitivity around that data set, because you actually have an insufficient amount of data. So, part of data detection and understanding biases; where you have sampling of data that's incorrect, where your segmentation could be rethought, where it may just require an additional supervision or like decision making criteria as part of your governance process. >> This is actually a great area for young people to get involved, whether at their universities or curriculum, this kind of seems to be, whether it's political science and/or data science kind of coming together, you kind of have a mash. What's your advice to people watching that might be either in high school, college, or rethinking their career, because this seems to be hot area. >> It is a hot area. I would recommend it for every student at every age, quite frankly and we're at such an early stage that it's not too late to join and you're not too young nor are you too old to actually get in the industry, so that's point one. This is a great time for everyone to get involved. The second piece is, I would just start with online courses that are available, as well as participate in communities and companies like IBM, where we actually make available on a number of our web based applications, that you can actually do some online training and courses to understand the services that we have, to begin to understand the taxonomy and the language, so a very simple set, would be like, learn the language of AI first, and then, as you're learning coding, if you're more technically inclined, there's just a myriad of classes available. >> Final question, before I move on to the topic around inclusion and diversity, machine learning is impacting all verticals. I was just in an interview, talking with Don En-ju-bin-ski, she's got a company where it's neuroscience and machine learning coming together. Machine learning's being impacted all over. We mentioned basic data bias, and machine learning can help there. Machine learning meets blank every vertical, every market, is being impacted machine learning, which will trigger some of the things you're seeing on the app side. Your thoughts, looking at where you've come from in your career at IBM to now, just the evolution of what machine learning has enabled, your thoughts on the impact of machine learning. >> Oh, it's exciting and I'll give you a real simple example, so one of the great things my own team actually did was apply machine learning to, everyone loves the holiday shopping period, right? Between Thanksgiving to New Years, so we actually develop, what we call, Watson Order Optimizer and one of my favorite brands is REI, so the recreational equipment incorporated company, they actually applied our Watson Order Optimizer to optimize in real time. The best place, let's say you want to order a kayak or a T-shirt or a hiking boot, but the best way to create the algorithms to ship from different stores, and shipping from stores, for most retailers, is a high cost variable, because you don't know what the inventory positions are, you don't necessarily know the movement of traffic into that store, you may not even know what the price promotions are, so what was exciting about putting machine learning algorithms to this was, we could actually curate things like shipping and tax information, inventory positions of products in stores, pricing, a movement of goods as part of that calculation. So, this is like a set of business rules that are automatically developed, using Watson, in a way that would be almost impossible for any human to actually come up with all of the possible business roles, right? Because this is such a complex situation, and then you're trying to do it at the peak time, which is, like Black Friday, Cyber Monday Weekend, so we were able to actually apply Watson Machine Learning to create the business roles for when it should be shipped from a warehouse or a particular store. In order to meet the customer requirement, which is the fulfillment of that brand experienced, or the product experienced, so my view is, there are so many different places across the industry, that we could actually apply machine learning to, and my team is really excited about what we've been doing, especially in the next generation of supply chain. >> And it's also causing students to be really attracted to computer science, both men and women. My daughter, who is a senior at Berkeley, is interested in it, so you're starting to see the impact of machine learning is hitting all main stream, which is a good segue to my next question, we've been very passionate, I know it's one of your passions is inclusion and diversity or diversity and inclusion, there's always debates: D before I or I before D? Some say inclusion and diversity or diversity and inclusion. It's all the same thing, there's just a lot of effort going on to bring the tech industry up to par with the reality of the world, and so you have a study out. I've got a copy here. Talk about this study: Women in Leadership and the Priority Paradox. Talk about the study; what was behind it and what were some of the findings? >> Sure, and I'm excited that your daughter, that's a senior in college, is going to be another woman that's entering the workforce, and especially being in tech, so the priority paradox is that we actually looked at over 2,300 organizations, these are some of the top institutions around the world, that are curating and attracting the best talent and skills. Now, when you look at that population, we were surprised to find out that you would think by 2019-2018 that only 18% of those organizations actually had women in senior leadership positions, and what I categorize as senior leading positions, are in the see-swee, as vice presidents, maybe senior executives or senior managers; director level folks. So, that's one piece, which is, wow, given the size and the state where we are in the industry, only 18%: we could do better. Now, why do we believe that? The second piece is, you want the full population of the human capacity to think and creatively solve. Some of the world's biggest complex problems; you don't want a small population of the world trying to do this, so, the second piece of the paradox, which was the most surprising, is that 79% of these companies actually said that formalizing or prioritizing gender, fostering that kind of inclusive culture, was not a business priority, and that they had a harder time actually mapping that gap. Now, in the study, what we actually discovered though, was those companies, that did make it a priority, actually had first mover advantage, and making it a priority is quite simple. It's about understanding how to create that inclusive culture, to allow different perspectives and different experiences to be allowed in the co-creation and development. >> So, first mover advantage, in terms of what? >> Performance, actual business performance, so even though 80% of the organizations that we interviewed actually said that they've not made it a business priority, the 20% that did, we actually saw higher performance in their outcomes, in terms of business performance. >> So, this is actually a business benefit, too. I think your point is, the first mover advantage is saying, those companies that actually brought in the leadership to create that different perspective, had higher performance. >> Absolutely. >> We've talked about this before; one of the things I always say is that, tech is now mainstream, and it's 18% of the target audience of tech isn't the market, it's 50/50 or 51. Some say 51% women/men, so who's building the products for half the audience? So, again, this doesn't make any sense, so this is a good statistic. >> It is, and if you think about the students that are actually graduating out of graduate school, recently, there's actually more women graduating out of grad school than men. When you think about that population that's now entering the workforce, and what's actually happening through the pipeline, I think there's got to be thoughtful focus and programmatic improvements across the industry, around how to develop talent and make sure that different companies and organizations can move. Like you said, problem solve for creating new products that actually serve the world, not just serve certain populations, but also do it in a way that's thoughtful about, kind of, the makeup. >> And the mainstream and prep of tech obviously makes it more attractive, I mean, you're seeing a lot more women thinking about machines, like my daughter, the question is, how do they come in and not lose their footing, mentor-ship? So, what are the priorities that you see the industry needs to do? What are some of the imperatives to keep the pipeline and keep all the mentoring, obviously mentoring is hot, we see the networking built. >> Yeah, mentoring is huge. >> What's your thoughts on the best practices that you've been involved in? >> Some of the best practices we've actually done a number with an IBM, we've done a program called, Tech Re-Entry, so women that have decided to come back into the tech workforce, we actually have a 12 week internship program to do that. Another is a big initiative that we have around P-TECH, which is the next generation of workers aren't just going to have a formal college and or PHD masters type degrees. The next generation, which we're calling, is not necessarily a white collar, blue collar, what we're calling it is, new collar, meaning these are students that are able to combine their equivalent of a high school degree and early college education in one to be kind of, if you think about it, next generation of technical vocational schools, right? That quickly enter the workforce, are able to do jobs in terms of web development, in terms of cloud management, cloud services, it could be next generation of-- >> It's a huge skill gap opportunity, this is a big opportunity for people. >> It is, and we're seeing great adoption. We've seen it on a number of states across the US, this is an effort that we partner with, the states and the governors of each state, because public education has got to be done in a systematic way that you can actually sustain it for many, many years and this is something that we were excited about championing in the state of New York first. >> The ReEntry program and other things, I always tell myself, the technology is so new now you could level up a lot faster than, there's not that linear school kind of mentality, you don't need eight years to learn something. You could literally learn something pretty quickly these days because the gap between you and someone else is so short now, because it's all new skills. >> It's true, it's true. We talk about digital disruption through the lens of businesses, but there's a huge digital disruption through the lens of what you're talking about, which is our individual development and talent, and the ability to learn through so many different channels that's available now, and the focus around micro degrees, micro skills, micro certifications, there's so many ways for everyone to get involved, but I really do encourage everyone across every industry to have some knowledge and basis and understanding of tech, because tech will redefine how services and products are delivered across every category. >> And that's not male or female: that's just everyone. Again, back to technology for good, we can solve technology problems, You guys have been doing it at IBM, solve technology problems, but now the people problem is about getting people empowered, all gender, races, et cetera, the people getting the skills, getting employed, working for clouds, this is an opportunity. >> This is a huge opportunity. I think this is an exciting time. We feel like we're entering this next phase of, what I call, chapter two of cloud, this is chapter two of digital reinvention, of the enterprise, digital reinvention of the individual, actually, and it's an opportunity for every country, every population group to get involved, in so many new and creative ways, and we're at the early foundation stages in terms of both AI development, as well as new capabilities like Blockchain. So, it's an exciting time for everybody. >> Well, that's a whole nother topic. We'll have to bring you back, Inhi. Great to see you, in fact, welcome to Palo Alto. First time in our studio. Let's co-host something together, me and you. We'll do a series: John and Inhi. >> I would love that. That would be fun. I'm excited to be here. >> You can drop by our studio anytime now that you live in Palo Alto, we're neighbors. Inhi Cho Suh here, general manager IBM Watson, customer engagement, friend of theCUBE, here inside our studios, Palo Alto. I'm John Furrier, thanks for watching. (upbeat music)