 Live from Miami, Florida, it's theCUBE. Covering IBM's data in AI forums, brought to you by IBM. We're back in Miami. Welcome everybody watching theCUBE, the leader in live tech coverage. We're here at the IBM data in AI forum. Wow, what a day, 1700 customers, a lot of hands-on labs, sessions. What used to be the IBM Analytics University is sort of morphed into this event now. You see the buzz is going on. Elise DeGalion is here. She's the vice president of global sales for IBM data in AI. Welcome to theCUBE. Thank you for coming on. Thank you very much. So this event is buzzing. It really is. It's happening, it doubled from last year almost and congratulations. Well, thank you very much. We have lots of countries represented here. We have customers from small to large, every industry represented. And I can see a marked difference in the conversations in just a year around how customers want to figure out how to embark on this journey to AI. So why do they come here? What's the primary motivation for them? Well, I think one, IBM recognized as the leader in AI and we just came out in the IDC survey as the three-time leader, recognized leader in AI. And when they come here, they know they're going to hear from other clients who have embarked on similar journeys. They know they're going to have access to experts, hands-on labs. And we bring our entire IBM team that's focused on data and AI to this event. So it's intimate, it's high-skilled, it's high energy, and they are learning a ton while they're here. Yeah, a lot of content and you're educating, but you're also trying to inspire people. I mean, Ray Zahab this morning, he wrote this book, but he's this extreme, extreme, extreme, like ultra marathoner, which I thought was a great talk this morning. And then I thought a good job of sort of connecting his talk of anything's possible to now bringing AI into the equation. What are you hearing from customers in terms of what they want to make possible and what's that conversation like in the field? Well, it's interesting because there is a huge recognition that every client that I talk to and they all want to understand this, that they have to be transforming their businesses on this journey to AI. So they all recognize that they need to start. Now, what I find when I talk to clients is that they're all coming in at different entry points. There's a maturity curve. So some are figuring out, how do I move away from just Excel spreadsheets? I'm still running my business on Excel. And these are banks in major countries that are operating on Excel spreadsheets. And they're looking at niche competitors, digital banks that are entering the scene, and if they don't change the way they operate, they're not going to survive. So a lot of companies are coming in knowing that they're low on the maturity curve and they better do something to move up that curve pretty fast. Some are in almost the second turn of the crank where they've invested in a lot of the AI technologies, they've built data science platforms, and now they're figuring out, how do they get that next rev of productivity improvement? How do they come up with that next business idea that's going to give them that competitive advantage? So what I find is every client is embarking on this journey, which is a big difference where I think we were even a year, 18 months ago, where they were sort of just, okay, this is interesting. Now they're, I better do something. So you're a resource, you know, as the head of global sales for this group. So when you talk to customers that are immature, if I hear you right, they're saying, help us get started because we're going to fall behind. We're inefficient right now. We're drowning in spreadsheets. Data, our data quality is not where it needs to be. Help, where do we start? What do you tell them? Well, one, we have a formula that we've proven works with clients. We bring them into our garages, where we will do design thinking, architectural workshops, and we figure out a use case because what we try not to do with our clients is boil the ocean. We want them to have something that they can prove success around very quickly, create that minimal viable product, bring it back to the business so that the business can see, oh, I understand, and then evolve that use case. So we will bring technical specialists, we will bring folks that are our own data scientists to these garage environments, and we will work with them on building out this first use case. Explain the garage a little bit more. Is that, those are sort of centers of excellence around the world, or how do I tap them as a customer? Is it a freebie? Is it for pay? Is it like the data science elite team? How does it all work? Well, it is, there are a number of physical locations and it's open to all clients. We have created these with co-leadership from across the entire IBM company. So our services organization, our cloud and cognitive organization, all play a role in these garages. So we have a formal structure where a team can engage through a request process into the garages. We will help them define the use case they want to bring into the garage. We will bring them in for a period of time and provide the resources and capabilities and skills. And that's not charged to the client. So we're trying to get them started. Now they'll take that back to their company and then they will look at follow-on opportunities and those may work out to be different services, opportunities as they move forward. But we're on that get started phase. Yeah, I mean you're a for-profit company so it's great to have a lost leader. But the line outside the door at the garage must be huge for people that want to get in. How are you managing the demand? Well, we're increasing obviously our capacity around the garages. And we're still making customers aware of the garages. So they're still, because it's a commitment on their side. They just can't come in and kick the tires. We asked them to bring their line of business along with their technical teams into the garages. Because that's where you get the best product coming out of it. When you know you've got something that's going to solve a business problem but you have to have buy-in from both sides. I want to ask you about the AI ladder. You know, Rob Thomas has been using this construct for a while. It didn't just come out of thin air. I'm sure there was a lot of customer input, a lot of debate about what should be on the ladder. When I first heard of the AI ladder, it was data in IA, analytics, ML, and AI. Sort of the building, the technical or technology building blocks. It's now become verbs, which I love. Which is collect, organize, analyze, and infuse. Which is all about operationalizing and scaling. How is that resonating with customers and how do they fit into that methodology or framework? Well, I'll tell you, I use that framework with every single client. And I describe that there is a set of steps. You know, obviously to the ladder that every customer has to embark upon. And it starts with some very basic principles. And as soon as you start with the very basic principles, every client is like, of course. Like, it seems so obvious that first and foremost, you have to date as the foundation, right? AI is not created out of someone in a back room. The foundation to AI is information and data. Yet, every customer, every customer struggles with that data is coming from multiple systems, multiple sources, that they can't get to the data fast enough. They're shipping data around an organization. It's not managed. And yet, they know that in five years, the data they think they need today is going to be completely different in it could be 12 months, but certainly in the future. So how do you build out an architecture that allows them to build that now, but have the agility to grow as the requirements change? You start with that basic discussion and they're like, well, of course, so that's collect. And then you bring it up and you talk about how do you govern that data? How do you know where that data originated? Who is the owner? How do you know what that data means? What system did it come from? What's, you know, who has access to it? How do you create that set of governed data? Well, of course, every client recognizes they have that set of issues. So I could continue working my way up the ladder and every client realizes that, okay, here's where I am today. What you just painted for me is absolutely what I need to focus on and address. Now, help me get from A to B. So I'm really interested in this discussion because it sounds like you're a very disciplined sales leader. So you said you use the ladder with virtually every client and I presume your sales teams use the ladder. So you train your salespeople how to converse the ladder and then the other observation, I'd love your thoughts on this is every step of the ladder has these questions. So you're asking customers questions and I'm sure it catalyzes conversation. The answers to which you have solutions presumably for many of them, but I wonder if you could talk about that. Just in terms of- Well, let me talk about the ladder and how we're using it with our Salesforce because it was a unifying approach, not just within our own team, our data and AI team, but outside of data and AI. Because not only did we explain it to clients this way, but to the rest of IBM, our business partners, our whole ecosystem. So unifying in that we started every single conversation with our sales team on enabling them on how do they talk to their clients, our materials, our use cases, our references, our marketing campaigns. We tied everything to this unified approach and it's made a huge difference in how we communicate our value to clients and explain this journey to AI in comprehensive steps that everyone could understand and relate to. Love it. How is the portfolio evolving to map into that framework and what can we expect going forward? What can you share with us, Elise? Well, the other amazing feat I'll call it that we produced around this is I'll talk to a client and I'll describe these capabilities. And then I will say to a customer, you don't have to do every one of these things that I've just described, but you can implement what you need when you need it because we have built all of this into a unified platform called Cloud Pack for Data. And it's a modern data platform. It's built on an open infrastructure, built on Red Hat OpenShift so that you can run it on your own premises as a private cloud or on public clouds, whether that be IBM or Amazon or Azure. It allows you to have a framework, a platform built on this open, modern infrastructure with access to all these capabilities I've just described as services. And you decide, completely open, what services you need to deploy when you grow the platform as you need it. And oh, by the way, if you don't have the Red Hat OpenShift environment set up, we'll package that in a system and I will roll in the system to you and allow you to have access to the capabilities in hours. How's the Red Hat conversation going? I would imagine a lot of the traditional IBM customers are stoked. You just picked up Red Hat, very innovative company, open source mindset. At the same time, I would imagine a lot of Red Hat customers saying, is IBM really going to let them keep their culture? How's that conversation going in the field? Well, I will tell you, we've been 100% consistent in terms of everything that you've heard Ginny and Arvin Krishna talk about and the fact that we are going to maintain their culture, keep them as that separate entity inside of IBM. It's absolutely perpetrated throughout the entire IBM company. We have a lot to learn from them as I'm sure they have to learn from us, but it truly is operating and I see it in the clients that I'm working with as a real win-win. If you had to take one thing away from this event that you want customers to remember, what would it be? Start now. Because if you don't begin on this journey to AI, you will find yourselves fighting against new competitors, increasing costs, you have to improve productivity. Every client is embarking on this journey to AI. Start now. And when you were talking about the maturity model, and one of those levels was folks that had started already and they wanted to get to the next level. When you go into those clients, do you discern a different sort of attitude? We've started, we're down the path. Do they have more of a spring in their step or they like chomping at the bit to really go faster and extend their lead relative to the competition? What's the dynamic like in those accounts? That's a great question because I was with a client this afternoon, a large manufacturer of goods, and they are at this turning point where they did kind of phase one. They implemented Cloudpack for data, and they did it to just join some of their disparate systems. Now, I mean, I barely got a word in because he was so excited because he's like, now what I'm going to do is I'm going to figure out where my factories should go based on where my products are selling. So he's now looking at how he can change his whole distribution process as a result of getting access to this data and analytics that he never had before. And I was like, okay, well just tell me how I can help you. And he was like way ahead. So this was the big kickoff day. I know yesterday there was sort of deep learning, hands-on stuff, big keynotes today. We're only here for one day. What are we going to miss? What's happening tomorrow? Well, it's a bit of a repeat of today. So we'll have another keynote tomorrow from Beth Smith who runs our Watson business for IBM. We'll have more hands-on labs. We have a lot of customer presentations where they're sharing their best practices. Lots of fun. Where do you want to see this event go? And what's next in IBM Eventland? Well, the feedback from last year, this year, says we have to do this again next year. It will be bigger because I think this year proves that it's already doubled and we'll probably see a similar dynamic. So why fully expect us to be here? Well, maybe not here. We're sort of outgrowing this hotel. But doing this event again next year. AI, machine learning, automation. I'll throw in cloud. These are the hottest topics going. Elise, thanks very much for coming to theCUBE. It was great to have you. Thank you. It's a great meeting with you. Thank you for watching everybody. That's a wrap from Miami. Go to siliconangle.com, check out all the news. Thecube.net is where you'll find all these videos and follow the Twitter handles at theCUBE 365. I'm Dave Vellante. We're out. We'll see you next time.