 Live from Las Vegas, it's theCUBE. Covering InterConnect 2017, brought to you by IBM. Okay, welcome back everyone. We are live at the Mandalay Bay for IBM InterConnect 2017, theCUBE's exclusive coverage. I'm John Furrier with Dave Vellante, my co-host. Our next guest is Jim Casey and Michael Gill-Fix. Michael's the VP of Process Transformation and Jim is an offering manager at IBM. Guys, welcome to theCUBE. Thank you. Thank you. So you guys had a big announcement on Monday, the digital assistant. So I've been craving a digital assistant since the little Microsoft, little icon would pop up. You're talking about Cliff. Yeah, Cliff, the Cliff man. We don't have that now. To me, that was once called the digital assistant. It was a help button. But this is now, digital assistant is really automation and you guys got a whole nother take on this. It's totally cloud, cloud first. What's the digital assistant product that you guys announced to take us through that? So here is our vision. What we found is in the modern digital workplace, everyone is struggling to just keep up pace. Too many sources of information and the information is buried everywhere. It's buried in emails, in spreadsheets, in documents. Many corporations have undertaken a BI project. In fact, there's an explosion of all these different dashboards that has all kinds of business data that they could go and see. So no one has the time to read all these things. Meanwhile, everyone in the modern world is trying to do 50 things at once and it's hard to figure out what is the best time to progress something and make progress. Our vision, so what we thought is, what if we'd be great if I could program this assistant programmable by everyday business users to watch for the things that matter to me and figure out when I should take action or take automated action on my behalf to save me time. So it's an interface, so it's software interface, cloud-based SaaS, and the backend, does the user have to, what's the persona of the user using your product? Well, we want them to be used by non-developers, non-technical users, and so we thought really carefully about how you can teach your assistant these notions of skills. Really pointed tasks that can really make your life easier on a daily basis, and they can pick anything that they like working with, that they can connect to, get the information from, and effectively assemble into these pointed tasks at the same time. And the data sources are whatever I want them to be, so explain how that works. Yeah, it can connect to common SaaS applications. Those could be things like productivity suites, like G Suite, they can be things like CRM systems, like Salesforce, campaign management systems, like Marketo, and that's just in the beta that we just launched, and of course in the future they'll be able to connect into their on-premise systems as well. So is it to replace the dashboards and all the wrangling that goes on? Most business users will have either a department that does all the data science or data prep for them, wrangling data sets, and then they get reports or spreadsheets or some BI dashboard. Yeah, we wanted the assistant to push the work to the user instead of the user having to go and spend time watching all these dashboards that really they just didn't have time to do. And so the assistant takes all the heavy lifting of watching the data for you, figures out when action is needed, and then taps you on the shoulder. So Ginni Rametti was talking about that your customers want to own the data. So that's a great purpose, but we buy into that mission. But a lot of the data is spread all over the place. So one of the problems that we're seeing in the big data world now IoT complicates even further is the data is everywhere, scattered, and the tools might have stacks and data wrangling within tools. So you have complexity out there just on the scaffolding of how the data is managed. Is that part of the problem that you guys helped solve? Because that seems to be a pain point. Yeah, and I think the amount of time that people spend just searching and aggregating and gathering information so they can figure out what to do, it's staggering. And when you think about the, it takes about two to three hours often for people to gather all the information that they need in order to make a real significant decision every day, daily operations, you're spending time in your email, you're building spreadsheets. Think of all the time you spend building a spreadsheet wrangling data, it's a productivity killer. And so a lot of the use cases that we look for, we'll ask our clients, show me the ugliest spreadsheet that you use on a day-to-day basis for your business operations. That's usually a starting point, or show me how many dashboards are you looking at? And what are the decisions that you make off of that? That's the stuff that we want to collapse into what the assistant came up with. So I got a use case for you. I mean, I'm a walking, I mean, I'm like everybody, right? So I've got my email, I've got five or six spreadsheets, Google Docs that I'm in every day all day. Maybe there's a base camp, maybe there's a Slack, right? I'm in Salesforce, right? And then I got my social. Tool overdose. You just described the typical modern environment. All fragmented tools. And I'm in there and I'm like, which browser is it? Oh, is it in Firefox? I'll put my Safari stuff, I'll put it over here, and I'll put my email in Mozilla. Okay, and it's just awful. It's a bloody nightmare, I get lost. I got to back up, hit the escape key and go, okay, where am I? How do I find it again? It's connecting the dots. Okay, so explain now how you can help me. So think of the things that you're looking for and all those different data sources. We're seeing the trend now. It's not about how can I just connect to things. It's how can I connect the dots? It's the actual business data inside of there. And how do I put that in a context that's relevant to you, like what you're trying to do? Great example, we're working with one client who they're moving and a lot of people are doing this. They're moving from a point in time sale to being as a service. And in that kind of scenario, relationships with your clients really matter. And preventing customer churn is really important. So they have people who are responsible for making sure that people are not going to churn. That's a lot of dots to connect. So with the digital business assistant, what we do is we look for those patterns that are really common, that predict churn, but those things are scattered across your sales systems, your marketing systems, website traffic, social media even. And we're able to combine all those things into a really consumable component called the skill. And then that individual person that's responsible for this set of customers can tailor it to their needs. So it's kind of like how we buy a suit. When you go in and buy a suit, you don't get just the fabric laid out on the table and they cut it, right? Most people don't anyway, right? I buy this on the rack. I want it. I want it. I want to get it. I got to walk out of here with you. It's too long, right? They make a couple of adjustments. Hey, I'll take that suit up there. What's on the mannequin? They make a few adjustments, and it's yours. Software should be the same way. You should be able to configure software in a few clicks. That's the whole thing. I mean, I joke about the mannequin, but that's really kind of what, that hangs the perfect use case. So that would be automated example of an assistant model for you guys. Sometimes you just want everything to hang together for you. And sometimes you might want to go in and go look at the data. Yeah. And we see this across a lot of different industries. So things like customer service and sales and marketing, but we also see it in, let's say I'm a field technician, right? And I got to go out to an oil field. And how do I know all the different patterns of information that might predict whether or not I need to, what I need to do when I'm out there? And so you monitor my patterns, my behavior, and then ultimately train the model? Well, you program it. You tell it what to watch for you. So to give you an example of the kind of use case, you know, to pick a specific use case, and we shared this again in sort of our unveiling on Monday. You know, we shared the idea of a sales rep who was pursuing a given opportunity and thinking about all the factors that went into their success. And you know, that sales rep has several different things they need to use to really maximize their chance of closing that deal. So one is they need to be responsive to their customer. And you know, like many different corporations out there who sell many different products and service, while you're busy working on the new opportunity, you got to service the old. So when some issue comes up, you have to be responsive to it. Well, it's really hard while you're busy working on all these opportunities to make sure that the issue is being resolved, that you're, you know, being responsive to your customer. Meanwhile, everybody in the corporation is coming up with new opportunities, new marketing brochures, new values in the product. And so is your rep knowledgeable about the latest and greatest products? So we imagine that you could teach your assistant how to watch some of this stuff for you and really help you to close your opportunity. And a very pointed example, the kinds of things that you watch for you, I should be able to say something like, okay, if I have an active opportunity and then my customer goes and opens a service support ticket and that service support ticket hasn't been resolved in a week. And meanwhile, I got a bunch of email coming from that client of tone angry. Notice the cognitive part there about this particular product. And meanwhile I'm on the road and I'm not checking my email. Well, I have a catastrophe waiting to happen. So I can program my assistant to watch for these kinds of things. And just do push notifications. Exactly. So you can then have it pushed to you. Well, here's all the information about the active service thing. Here's how long it was sitting there waiting for resolution. This is what's happened since and you can immediately take action. So you're orchestrating basically signals that the user connects. Like a Google alert on search is a trivial example, right? Someone types, a result comes on Google and you get an email. Here you're kind of doing an orchestration level. You tell your assistant to proactively watch it for you. And that's a unique technology that we developed in-house because it's watching all these events and mapping the enterprise and figuring out when that thing becomes actionable. And the user would know where to look because like Dave Spreadsheet might say, hey, cash balance or sales trend, this rep. And then something happens. He can get that push to him from three different disparate siloed apps. That's pretty much what it is. That's right. Okay, so give us a status on the beta right now. It's a beta, so it's signup required. Okay, and the requirements to implement it if you get through the beta is just log into a portal with a SAS model and then do the connectors. Yeah. So the first thing you do, you go to ibm.com slash assistant. You can sign up to- That by the way, might be the easiest URL I think we've ever come up with. Yep. I'm pretty sure. I believe that one was going to be memorable, so. Yeah, so you just go to that site. You sign up, you give us a little bit of information. Your email, how to contact you and we'll put you on the waiting list. And what we're going to be doing is opening up more seats as we go through over the next couple of weeks. And then we plan in the near term here to make it available as an open beta that you could see, and you'll see that inside a blue mix as a tile inside a blue mix. And here's the thing. We're doing something really different in the marketplace. This is a very different kind of offering. Really targeted again at non-technical people. There's proactive situational awareness that your assistant can do. Uses your data, built-in intelligence, intelligence that can customize to the way you work, guide you to the next best action. We have an incredible vision for this. The idea behind the beta is to start getting feedback. We worked very closely with early customers in the initial design and development. We want to open that up and get even more feedback and ideas on this kind of technology. How is this different from Watson's discovery services that they have? I can imagine that you're building on Watson. Is it the cognitive piece within IBM or is this kind of, I mean, how would a customer figure that out or just more about it? Yeah, so I can give you an example. So we have one of our prototypes that where we're actually taking some of the components of Watson discovery service and we package that up as a skill inside of your assistant. And it's a specific implementation. So what it allows you to do in this case is it'll look at your email and it'll look for specific entities like a customer that matters to you. And if I get three emails of negative sentiment from a customer where I also have an open opportunity in the last week, that's a pattern I want to know about, right? Or we can start to correlate with all sorts of different things. So I think what you're going to see is these skills that we make available with the digital business assistant really up, take consumability of these really, really powerful technologies around cognitive and cloud. We take that to the next level. That's the key. How do we make Watson tailorable and put it in the hands of every knowledge worker in every company? So I presume you guys are dog fooding this personally. Is that right? We have plans to do that, yes. Oh, you haven't started yet. Sampling our own champagne. But we are, yeah. So we will be, we will be there. We created that champagne. We're beer drinkers. I said we're going back to dog food. We beer, we should drink it over here. We beer beer, there you go. We created that with Oliver Boosman. Remember, anyway. So get back to the status of the product. So it's got some Watson capability, but this is for the user to use. I don't get IT involved. That's right, right. This is where the user takes a personal productivity approach. Right. And you guys are bringing some Watson. A user may not even know that they're using some of these Watson capabilities. To the end user, it's what do you want it to do for me? You well, I want it to tell me if I think a customer might be upset with me. Well, that might be a combination of a lot of different things, but it just makes it really consumable and easy for me. I'm going to sit with an IBM because now there's like, because this is a really cool user tool. So it's just part of Watson. We think so. Is it part of the Watson team? Well, obviously our organization really doesn't matter. I mean, we're working with teams across IBM as a whole. I mean, it's a great opportunity to take this technology and really reach a whole set of new use cases, I think, across the company. And we want to integrate Watson technology to, like we were saying, really make it easy for the end user to go and access it. Any plans for around developer outreach? Well, we will. I think, you know, later this year, one of the things we envisioned really early on is that people are going to want to have pre-built skill sets. And that's a great opportunity to build an incredibly powerful ecosystem. And we've been in discussion with a lot of our partners about how to do that. Well, you guys are API based. This is a beautiful thing. Well, we're going to start to open up some SDKs to, you know, our partners, to others. And that's going to allow them to extend the assistant and really create even more powerful industry content. You know, the business model of reducing the steps it takes to do something and saving people time, making it easy to use, is a magical formula of success. And not even just, not even just less steps. It's less time reading things, less time sifting through information so you can spend time on stuff that matters. Just email by itself. I mean, Dave, your example is the best. I mean, cause I know, we live that. And we have a multitude of tools. And sometimes it just organically goes to one guy likes, you know, this tool set, or now I got. So guys, do you want to do the deal now? Right, that's what I'm saying. They should be signing up. So do we get paid to do it? Yeah, both sides, no. We have a testimony. If you can't get it, how can we do it? We'll kick the tires on it. And the thing that gets my excitement is potential for API integration. Cause if I know I can take the automation to a whole other level and the use cases start to patternize in the enterprise, then it's interesting. All right guys, thanks so much. What's going on here for the show? What else is happening for you guys? Share some stories for the folks who aren't here. They're watching on IBM Go right now. What's the vibe at the show this week? Well, look, it's been a great vibe. We've had a chance to share some incredible success stories. So, you know, in addition to the unveiling of this particular product, on Monday we had a chance for one of our marquee clients to share their story. And I'll tell you a little bit about what they did. It was at the National Health Service at the UK, part of their blood and transplant. And we were fortunate enough to have Aaron Powell, who's the chief digital officer there, share their story of using process technology to improve the speed at which they get organs in the hands of recipients. And they did it on the cloud. And the results they obtained were unbelievable. So the before and after, they had a staff at 2 a.m., writing lists of high-risk patients and how to map their donors. And he kidded us not that when someone's priority changed they would wipe the board and re-set things. And these are people's lives that are at stake in the matching process. And their tire of human error is huge. Human error, absolutely. And by the way, when you look at the N10 process there was something like 90 steps, if I remember, 96 steps, I think, end to end. All of which were very manual and air-prone. And air-prone means risk. And they were able to improve organ allocation by 3x. So 3x faster. They automated something like 58% of the steps, reducing propensity for manual error. And what he shared in his story is they, successfully a few months ago, did the first heart transplant on the cloud. Wow, that's amazing. So it was an amazing, amazing story. That's a great story. And did he say that in the session? He did, actually. He said that and some other great things. That's actually a good thing to chase down for a great blog post. That would be fun. It was phenomenal. So we're going to post actually the video of it online so people can also see him live presenting his story. It was unbelievable. I mean, the other thing that they could apply there is to blockchain. I mean, some of the blockchain stuff coming out is going to be really interesting. And we're working very closely with that team to really leverage this kind of process technology, take people's business operations and connect that into this future network that's going to power business. CRM is the human supply chain. I mean, but now you send it out to the internet of things. I mean, it's interesting how this could play out. Guys, thanks so much for coming to CUBE. Thanks for sharing the insight. Congratulations on the launch. I just signed up for the beta while we were talking. You too. We're in. Let us cut the line. We need it. We need it. Perfect use case. We need help. It's theCUBE, of course. No help here. Great guests here on theCUBE. I'm John Furrier, Dave Vellante. More great coverage. Stay with us. Day three of Interconnect 2017. We'll be right back.