 from the CUBE studios in Palo Alto and Boston. It's theCUBE, covering IBM Think, brought to you by IBM. We're back, and this is Dave Vellante. You're watching theCUBE and we're covering wall-to-wall the IBM 2020 Think digital experience. Rob Thomas is here as the Senior Vice President of Cloud and data platform, Rob. Always a pleasure to see you. I wish it were face-to-face, but hey, we're doing the best we can. As you say, doing the best we can. Great to see you, Dave. Hope family, safe, healthy, happy as best you can be. Yeah, Ditto, back at you, Rob. And congratulations on the new role. Look at you and theCUBE. We've been riding this data wave for quite some time now. It's really been incredible. It really is. And last year I talked to you about how clients were slowly making progress on data strategy starting to experiment with AI. We've gotten to the point now where I'd say it's game on for AI, which is exciting to see. And that's a lot of what the theme of this year's Think is about. Yeah, and I definitely want to dig into that, but I want to start by asking you sort of the moves that you saw and are seeing your clients make with regard to the COVID-19 crisis, maybe how you guys are helping them and very interested in what you see as sort of long-term and even quasi-permanent as a result of this. I would first say it this way. I'm not sure the crisis is going to change businesses as much as it's going to be accelerating what would have happened anyway. Regardless of the industry that you're in, we see clients aggressively looking at how do we get the digital faster? How do we automate more than we ever had before? There's the obvious things like business resiliency, business continuity, managing the distributed workforce. So to me, what we've seen is really about an acceleration, not necessarily in a different direction, but an acceleration on the thing is that that we're already kind of in the back of their minds or in the back of their plans. Now that as we've come to the forefront and I'm encouraged because we see clients moving at a rate and pace that we've never seen before, that's ultimately going to be great for them, great for their businesses. And so I'm really happy to see that. You guys have used Watson to really try to get some good high fidelity answers out to citizens. I wonder if you could explain that initiative. Well, we've had this application called Watson Assistant for the last few years and we've been supporting banks, airlines, retailers, companies across all industries and helping them better interact with their customers. And in some cases, employees. We took that same technology and as we saw the whole COVID-19 situation coming, we said, hey, we can involve Watson Assistant to serve citizens. And so it started by, we started training the models which are intent based models in Watson Assistant on all the publicly available data from the CDC, as an example. And we've been able to build a really powerful virtual agent to serve really any citizen that has questions about COVID-19 and what they should be doing. And the response has been amazing. I mean, in the last two weeks, we've gone live with 20 organizations, many of which are state and local governments, but also businesses, city of Austin, children's healthcare of Atlanta, state and local governments in Spain and Greece, all over the world. And in some instances, these clients have gotten live in less than 24 hours, meaning they have a virtual agent that can answer any question, they can do that in less than 24 hours. It's actually been amazing to see. So proud of the team that built this over time. And it was kind of proof of the power of technology when we're dealing with any type of a challenge. You know, I had a conversation earlier with Jamie Thomas about quantum and was asking her sort of how your clients are using it. And the examples that came up were financial institutions, pharmaceutical, you know, battery manufacturers, airlines. And so it strikes me when you think about machine intelligence and AI, the type of AI that you're doing at IBM is not consumer oriented AI. It's really designed for businesses. And I wonder if you could sort of add some color to that. Yeah, let's distinguish the difference there because I think you said it well. Consumer AI is smart speakers, things in our home, you know, music recommendations, photo analysis, and that's great and it enriches all of our personal lives. AI for business is very different. This is about how do you make better predictions? How do you optimize business processes? How do you automate things that maybe your employees don't want to do in the first place? Our focus in IBM as part of what we've been doing with Watson is really anchoring on three aspects of AI. Language, so understanding language because the whole business world is about communication and language. Trust, meaning trusted AI, you understand the models, you understand the data. And then third, automation. And the whole focus of what we're doing here in the virtual think experience is focused on AI for automation, whether that's automating business processes or the new announcement this week, which is around automating IT operations for a CIO. You've talked over the years about this notion of an AI ladder, you actually wrote a book on it. But it's been hard for customers to operationalize AI. We talked about this last year at Think. What kind of progress have we made in the last 12 months? There's been a real recognition of this notion that your AI is only as good as your data and we use the phrase there's no AI without IA, meaning information architecture. It's all the same concept, which is that your data has to be ready for AI if you want to get successful outcomes with AI. And the steps of those ladders around how you collect data, how you organize data, how you analyze data, how you infuse that into your business processes and seeing major leaps forward in the last nine months where organizations are understanding that connection and then they're using that to really drive initiatives around AI. Let's talk about that a little bit more. There's this notion of AI ops. I mean, it's essentially that take the concept of DevOps and apply it to the data pipeline, if you will. I mean, everybody complains, data scientists complain they always spent all their time wrangling data, improving data quality. They don't have line of sight across their organization with regard to other data specialists, whether it's data engineers or even developers. Maybe you could talk a little bit more about that announcement and sort of what you're doing in that area. Sure, so first let me put a number on it because the numbers are amazing. Every year organizations lose $26.5 billion of revenue because of outages and IT systems. That is a staggering number when you think about it. And so then you say, okay, so how do you break down and attack that problem? Well, you have to get better at fixing problems or you have to get better at avoiding problems altogether. And as you may expect, it's a little bit of both. You want to avoid problems, obviously, but in an uncertain world, you're always going to deal with unforeseen challenges. So also the question becomes, how fast can you respond? And there's no better use of AI than to do something like those tasks, which is understanding your environment, understanding what the systems are saying through their data, and identifying issues before they become outages. And once there is an outage, how do you quickly triage data across all your systems to figure out where is the problem and how you can quickly address it? So we are announcing Watson AI Ops, which is the nervous system for a CIO. To manage all of their systems. What we do is we just collect data, log data from every source system. And we build a semantic layer on top of that. So Watson understands the systems, understands the normal behavior, understands the acceptable ranges, and then anytime something's not going like it should, Watson raises his hand and says, hey, you should probably look at this before it becomes a problem. We've partnered with companies like Slack. So the UI for Watson AI Ops is actually in Slack. So that companies can use, and employees can use a common collaboration tool to troubleshoot or look at any of their systems. It's really powerful, something we're really proud of. Well, this kind of leads me to my next question, which I mean, IBM got the religion 20 years ago on openness. I mean, I can trace it back to the investment you made in Linux way back when. And of course, there's a huge investment last year in Red Hat, open source company. So you just mentioned Slack, talk about open ecosystems and how that fits into your AI and data strategy. Well, if you think about it, if we're going to take on a challenge this grand, which is AI for all of your IT, by definition, you're going to be dealing with a whole ecosystem of different providers because every organization has a broad set of capabilities. We identified early on, that means that our ability to provide an open ecosystem, interoperability was going to be critical. So we're launching this product with Slack I mentioned, with Box, we've got integrations into things like PagerDuty, ServiceNow, really all of the tools of a modern IT architecture, where we can understand the data and help clients better manage those environments. So this is all about an open ecosystem, and that's how we've been approaching it from the start. It's really about data, applying machine intelligence or AI to that data, and about cloud for scale. So I wonder what you're seeing, just in terms of that sort of innovation engine. I mean, obviously it's got to be secure, but it seems like those are the pillars of innovation for the next 10 plus years. I think you're right. And I would say this whole situation that we're dealing with has emphasized the importance of hybrid deployment, because companies have IT capabilities on public clouds, on private clouds, really everywhere. And so being able to operate that as a single architecture is becoming very important. You can use AI to automate tasks across that whole infrastructure. That makes a big difference. And to your point, I think we're going to see a massive acceleration towards hybrid cloud deployments using AI, and this will be a catalyst for that. And so that's something we're trying to help clients with all around the world. You know, you wrote in your book that O'Reilly published that AI is the new electricity, and you talked about problems of not enough data. If your data's on-prem and you're only in the cloud, well, that's a problem, or too much data, how you deal with all that data, data quality. So maybe we could close on some of the things that you talked about in that book, maybe how people can get ahold of it or any other sort of actions you think people should take to get smart on this topic. Yeah, so look, really excited about this. Paul's a copulous friend of mine and a colleague. We've published this book working with O'Reilly called the AI ladder. And it's all the concepts we talked about in terms of how companies can climb this ladder to AI. And we go through a lot of different use cases, scenarios. I think anybody reading this is going to see their company in one of these examples. Our whole ambition was to hopefully plant some seeds of ideas for how you can start to accelerate your journey to AI in any industry right now. Well, Rob, it's always great having you on theCUBE. Your insights over the years, and you've been a good friend of ours. I really appreciate you coming on and best of luck to you, your family, your wider community, really appreciate it. Thanks, Dave, great to be here. And again, wish you and the whole CUBE team the best. And to all of our clients out there around the world, we wish you the best as well. All right, you're watching the CUBE's coverage of IBM Think 2020, the digital event. We'll be right back right after this short break. This is Dave Vellante.