 from theCUBE studios in Palo Alto and Boston. It's theCUBE, covering IBM Think. Brought to you by IBM. Hi everybody, this is Dave Vellante of theCUBE. Welcome back to the continuous coverage that we're running here at the IBM Think Digital 2020 Experience. I'm with Riddica Gunner, who's a longtime CUBE alum. She's the vice president for data and AI expert labs and learning. Riddica, always a pleasure. I wish we were seeing each other face to face in San Francisco, but we'll have to make the best. Always a pleasure to be with you, Dave. So listen, we last saw each other in Miami at an IBM data event. You hear a lot of firsts in the industry. You hear about cloud first, you hear about data first, you hear about AI first. I'm really interested in how you see AI first. I mean, customers, they want to operationalize AI. They want to be data first. They see cloud as basic infrastructure to get there, but ultimately they want insights out of data and that's where AI comes in. What's your point of view on this? I think any client that's really trying to establish how to be able to develop a AI factory in their organization so that they're embedding AI across the most pervasive problems that they have in their org, they need to be able to start first with the data. That's why we have the AI ladder where we really think the foundation is about how clients collect their data, organize their data, analyze it, infuse it in the most important applications and of course use that whole capability to be able to modernize what they're doing. So we all know to be able to have good AI, you need a good foundational information architecture. And thus a lot of the first steps we have with our clients is really starting with the data, doing an analysis of where are you with the data maturity? Once you have that, it becomes easier to start applying AI and then to scale AI across the business. So unpack that a little bit. I mean, talk about some of the critical factors and the ingredients that are really necessary to be successful. What are you seeing with customers? Well, to be successful with a lot of these AI projects I mentioned it starts with the data and when it comes to those kind of characteristics you would often think that the most important thing is the technology, it's not. That is a myth, it's not the reality. What we found is some of the most important things start with really understanding and having a sponsor who understands the importance of the AI capabilities that you're trying to be able to drive through a business. So do you have the right hunger and curiosity across your organization from top to bottom to really embark on a lot of these AI projects? So that's the cultural element I would say that you have to be able to have. And that embeds within it like the skills, capabilities that you need to be able to have not just by having the right data scientists or the right data engineers but by having every person who's going to be able to touch these new applications and to use these new applications understand how AI is going to impact them. And then it's really about the process piece. I always talk about AI is not a thing it's an ingredient that makes everything else better. And that means that you have to be able to change your processes. Those same applications that had DevOps processes to be able to put it in production need to really consider what it means to have something that's ever changing like AI as part of that which is also really critical. So I think about it as it is foundation in the data the cultural changes that you need to have from top to bottom of the organization which includes the skills and then the process components that you need to be able to change. So you're really talking about like DevOps for AI data ops I think is a term that's going to gaining popularity. I think you guys have applied some of that in internally is that right? Yeah, it's about the operations of the AI lifecycle and how you can automate as much of that as possible apply AI there as much as possible. And that's where a lot of our investments in the data and AI space are going into how do you use AI for AI to be able to automate that whole AI life cycle that you need to be able to have in it? Absolutely. So I've been talking a lot of CXO CEO CIOs we've held some CISO and CIO round tables with our data partner ETR. And one of the things that's clear is they're accelerating certain things as a result of COVID-19. There's certainly much more receptive to cloud. Of course, first thing you heard from them was a pivot to work from home infrastructure. Many folks weren't ready, so okay. But the other thing that they've said is even in some hard hit industries we've essentially shut down all spending with the exception of very, very critical things including interestingly our digital transformation. And so they're still on that journey. They realize the strategic imperative and they don't want to lose out. In fact, they want to come out of this stronger. AI is a critical part of that. So I'm wondering what you've seen specifically with respect to the pandemic and customers, how they're approaching AI whether or not you see it accelerating or sort of on the same track what are you seeing out there with clients? This is where in pandemics, in areas where we face a lot of uncertainty I am so proud to be an IBMmer. We actually put out an offer when the pandemic started in the March timeframe to many of our organizations and communities out there to be able to use our AI technologies to be able to help citizens really understand how COVID-19 was going to affect them. What are the symptoms? Where can I get tested? Will there be school tomorrow? And we've helped hundreds of organizations and not only in the public sector and the healthcare sector across every sector be able to use AI capabilities like what we have with Watson Assistant to be able to understand how COVID-19 is impacting their constituents. As I mentioned, we have hundreds of them. So one example was children's healthcare of Atlanta where they wanted to be able to create an assistant to be able to help parents really understand what symptoms are and how to handle diagnoses. So we have been leveraging a lot of AI technologies especially right now to be able to help not just citizens and other organizations in the public and healthcare sector but even in the consumer sector really understand how they can use AI to be able to engage with their constituents a lot more closely and that's one of the areas where we have done quite a bit of work and we're seeing AI actually being used at a much more rapid rate than ever before. Well, I'm excited about this because everybody talking about the recovery, what does the recovery look like? Is it V shape? Nobody really expects that anymore but maybe a U shape. But the big concern people have is this W shape recovery. And I'm hopeful that machine intelligence and data can be used to just help us really understand the risks and also getting out good quality information I think is critical. Different parts of the country and the world are going to open at different rates but we're going to learn from those experiences and we need to do this in near real time. I mean, things change. Certainly for a while they were changing daily. They kind of still are. Maybe we're on a slower, maybe it's three or four times a week now but that pace of change is critical and machines are the only way to keep up with that. I wonder if you could comment. Well, machines are the only way to keep up with it. Not only that, but you want to be able to have the most up to date relevant information that's able to be communicated to the masses in ways that they can actually consume that data. And that's one of the things that AI and a lot of the assistant technologies that we have right now are able to do. You can continually update and train them such that they can continually engage with that end consumer and that end user and be able to give them the answers they want. And you're absolutely right, Dave, in this world, the answers change every single day and that kind of workload and demand, you can't leave that alone to human laborers. Even human laborers need an assistant to be able to help them answer because it's hard for them to keep up with what the latest information is. So using AI to be able to do that is absolutely critical. And I want to stress, I said machines, you can't do it without machines, and I believe that, but machines are a tool for humans to ultimately make the decisions in a crisis like this. Because you see, I mean, I know we have a global audience, but here in the United States you got, you have 50 different governors making decisions about when and how, certainly the federal government's putting down guidelines, but the governor of Georgia is going to come back differently than the governor of New York, different from the governor of California. They're going to make different decisions and they need data and AI and machine intelligence will inform that, ultimately, their public policy is going to be dictated by a combination of things, which obviously includes machine intelligence. Absolutely. I think we're seeing that, by the way, I think many of those governors have made different decisions at different points, and therefore their constituents need to really have a place to be able to understand that as well. Well, you're right. I mean, the citizens ultimately have to make the decision. Well, the governor said it's safe to go out, but I'm going to do some of my own research and just like if you're investing in the stock market, you got to do your own research. It's your health and you have to decide, and to the extent that firms like IBM can provide that data, I think it's critical. Where does the cloud fit in all this? I mentioned the cloud before. I mean, it seems to be critical infrastructure to get the information out fast and scale. Talk about that. All of the capabilities that we have, they run on the IBM cloud. And I think this is where, you know, when you have data that needs to be secured and needs to be trusted, and you need these AI capabilities, a lot of the solutions that I talked about, the hundreds of implementations that we have done over the past, just six weeks, if you kind of take a look at it, six to eight weeks, all of that on the IBM public cloud. And so cloud is a thing that facilitates that, and it facilitates it in a way where it is secure, it is trusted, and it has the AI capabilities that augment it. Pritikha, there's learning in your title. Where do people go to learn more? How can you help them learn about AI and how to get started or keep going? Well, you know, we think about a lot of these technologies as it isn't just about the technology, it is about the expertise and the methodologies that we bring to bear. You know, when you talk about data and AI, you want to be able to blend the technology with the expertise, which is why my title is expert labs that come directly from the labs and we take our learnings through thousands of different clients that we've interacted with, working with the technologies in the lab, understanding those outcomes and use cases and helping our clients be successful with their data and AI projects. So that's what we do. That's our mission. Love doing that every day. Well, I think this is important because, I mean, a company, an organization the size of IBM, a lot of different parts of that organization. So I would advise our audience to challenge IBM and say, okay, you've got that expertise. How are you applying that expertise internally? I mean, I've talked to Inderpal Bhandari about how the data science is being applied within IBM, how that's then being brought out to customers. So you've actually, you've got a Petri dish inside this massive organization. And it sounds like through the expert labs and sort of learning centers, you're sort of more than willing to and aggressively actually sharing that with clients. Yeah, I think it's important for us to not only eat our own dog food, so you're right, Inderpal, the CIO office, CTO office, we absolutely use our own technologies to be able to drive the insights we need for our large organization. And through the learnings that we have, not only from ourself, but from other clients, we should help our clients and our communities and our organizations progress their use of their data. And there we really firmly believe this is the only way not only these organizations will progress, that society as a whole will progress. So we feel like it's part of our mission, part of our duty to make sure that it isn't just a discussion on the technology, it is about helping our clients and the community get to the outcomes that they need to using AI. Well, I'm glad you invoked the dog fooding, because we use that terminology a lot. A lot of people, marketing people, sit back and say, no, no, it's sipping our champagne. Well, to get to champagne takes a lot of work. The grapes at the early stages don't taste that good. There's a pain that you have to go through. And so that's why I think it's a sort of an honest metaphor. But, Ritika, you've been a friend of theCUBE. We've been on this data journey together for many, many years. I really appreciate you coming on back on theCUBE. Always a pleasure. I'm sharing with the thank audience. Great to see you. Stay safe. And hopefully we'll see you face to face soon. All right, thank you. All right, take care, my friend. And thank you for watching, everybody. This is Dave Vellante for theCUBE. You're watching IBM Think 2020, the digital version of Think. We'll be right back right after this short break.