(upbeat music) >> Announcer: Live from Atlanta, Georgia, it's theCUBE. Covering Citrix Synergy Atlanta 2019. Brought to you by Citrix. >> Hello, and welcome to theCUBE's coverage of Citrix Synergy 2019 from Atlanta, Georgia. I'm Lisa Martin with my co-host Keith Townsend, the CTO Advisor. Keith, it's so great to see you. >> Lisa, good to be on the show with you again. >> So we're going to geek out the next two days. >> Oh isn't it so good? >> We've been geeking out already just coming from the keynote. This is ... >> Yeah This is, it was really good there was meat, there was announcements, there was news, partnerships. Citrix is a 30 year old company, who's done a lot in the last 12/18 months, to transform. From rebranding, product names, et cetera, lots of launches and announcements. And something that really peaked my interest as a marketer this morning, is hearing the influence of consumerization. Them talking about leveraging Citrix Workspace, and the things that they have done to beef it up which we'll talk about, to deliver a stellar employee experience, to delight the users. And those are words that we hear often in the marketing space, like customer lifetime value, they talked about the employee lifetime value because employee attraction, talent attraction and retention, is critical for every business. Really meaty stuff. What was some of your take on some of the announcements on Workspace? >> So I was really interested because as I'm coming off of SAP SAPPHIRE, where I'm accustomed to hearing terms like customer experience, employee experience, you know, the kind of X-data versus O-data conversation. We heard a lot of that here today. And it's weird coming from an infrastructure company. Citrix in the past I like to put into a box, it's about VDI, application virtualization and networking, and that's pretty much the conversation, it stayed at the IT infrastructure leader perspective. Today we heard a lot that broke out of that, and it was going into the C-Suite and delivering not just technology results, but business results. There was a lot about making transformation real. >> You're right it was about making it real, and if you think at the end of the day, I think I heard a stat the other day, that by 2020, which is literally around the corner, 50% of workers are going to be remote. You and I are great examples of that, we're on the road all the time, we have multiple devices we need to have connectivity that ... to all the apps, SAS apps, mobile apps, web, that allow us to be productive from wherever we are, done in a way that our employers, are confident there is security behind this. But delivering that exceptional employee experience is absolutely business critical. They gave some stats today about the trillions of dollars that are spent, or rather work that's lost, with employees that have so many apps each day that they're working with that really distract from their actual day to day function. >> Yeah I think one of the stats that they gave from an ambitious perspective, they want to give one day back to every employee, 20% of their time, back, I think the stat you referred to some seven trillion dollars of productivity is lost from just hunting and pecking inside of applications. Both of us work remotely, you work from your tablet, I work from a tablet or my phone a lot. Because I just, you know, it's low power to, it lasts the day, but yeah I still need to edit video, I need to sign invoices, I need to create statements that worked. I need to be just as creative on the road as I am at home. It helps me to compete against larger competitors, but more importantly, offer a different customer experience and this is what Citrix was talking about today, was more than just VDIs, about picking up any device asking basic logical questions like what is the status of the latest deal, the big deal, and getting that status from Salesforce without again hunting and pecking, from whatever device you're on. >> Which is critical, especially to have that seamless experience going from desktop to mobile. I think they also said ... there was a lot of stats this morning, which I really geek out on. But that the average person is using seven to 10 apps a day and I loved the video that they showed this morning that really brought that to life. Looking at a senior marketing manager for some enterprise company, who, as she starts her day, there's 10 minutes that goes by which is lie, oh, I forgot I got to log into Workday and request my PTO, oh, one of my employees needs me to approve an expense report, and oh, my boss wants to know about this big deal that's closed. And the time that is spent logging into different applications is really as you mentioned that number seven trillion dollars lost, what they're doing with Citrix, with the intelligent, the workspace intelligence experience is bringing all of that to the end user. So it's much more an activities focus rather than an app focus experience. And I loved what you said that they're very ambitiously aiming to give each person back one day a week, yes please. I will take that. In any organization. >> So I was at a government conference a few weeks ago and they talked very much about this CFO of GSA presented to a crowd of fellow government workers, and they were talking about eliminating waste, they were talking about automating processes, taking the PDF, taking a document and scanning it into a system, and then kicking off a real workflow. And this is done, the industry's been working on this problem for the past 10 years, it's called RPA, Robotic Process Automation. One of Citrix's partners and I guess now competitors in that space just received $560,000,000 in funding, in a single round, to enable artificial intelligence to do this. What I thought was interesting, is that Citrix didn't use the term bots, I think other than one time ... >> Lisa: That's right. ... on the stage. But these are essentially bots, that take redundant processes, automates them, to ultimately add value. I'm anxious to dive in and talk about how Citrix is taking stuff like, they mentioned Mainframe, AS/400 applications, integrating that in Salesforce without having this huge multi-million dollar project to re-write these core business applications and processes. So, you know it's a really exciting time in the industry Citrix has really stepped up in saying, you know what, we won't settle for just having a good business, and this application virtualization and network space, we're going to go all in. >> So one of the things I saw in Twitter this morning, is you and I are both tweeting during the keynote, which we just came from is you talked about PRA right away on Twitter and it's something that you heard instinctively with what they were saying. What are your thoughts as to why RPA as a term wasn't discussed? Did you think it's the type of audience that's here? Is it just not a term that resonates as well as AI and machine learning, which are buzz words at every event we go to? >> And I think a good portion of that is a mix. We're at a conference that's very IT-centric. Citrix is a you know, one of the core IT infrastructure vendors. So when you throw out a term like Robotic Process Automation you constantly, you instantly think, you know, gain of productivity from me or your level maybe, but from an IT infrastructure practitioner perspective, Robotic Processing Automation has a resonance with being equal to eliminating jobs. If, you know, you're going to automate the integration between VMware VSphere and Citrix desktop virtualization and that administration piece, which these solutions definitely can do that, what's left for me to do the work on. If you're going to automate the provisioning of DNS and IP addressing and all these mundane tasks that administrators probably spend 50-60% of their day doing, you know what, that's threatening. To say that you know what, we're going to give you the same tools that we give to make the workspace available today from an application perspective and to tackle that from the concept of this is just extending that ideal and you're a what, your job and what you do today to adding true business value, I think it was smart on their part to kind of avoid the bot conversation. >> Okay, I'm glad that you shared that insight, that makes perfect sense. So, PJ Hough was up there, the Chief Product Officer, who's going to be on tomorrow, talking about what Citrix is doing to distill apps and make this experience much more personalized. And of course he was joined on stage with a big Microsoft announcement today. I think I've been to so many shows this year I've lost count but I think Satya Nadella has either been on stage, he was at Dell Technologies World with Michael Dell and Pat Gelsinger, or in a video like he was today. So the partnership with Microsoft expanding here a little bit of a teaser at Microsoft Ignite a couple of months ago. Gimme your thoughts on what Microsoft, I should say what Citrix is doing to facilitate their users being much more proficient at using Microsoft Team, which I believe the gentleman from Microsoft said there's over 300,000 active users already. Fastest growing product in Microsoft's history. >> So when you talk about collaboration, you can't collaborate without these tools, whether Teams, Slack, whatever, it's become an integral part of how we communicate, how we interact, I know a lot of friends that I have are moving from Slack to Teams, just because of the integration with Office365 they can collaborate around, and I think here on theCUBE we talk about data as being the key. You have to talk about data. One of the things that was prepared to go kind of head on with Citrix today, and tomorrow about, was about data. You know it's great to present applications, but how are you helping to help users collaborate and use and access data and the combination of RPA with the intelligent experi- intelligent, it's going to take us some time to used to this ... >> I keep wanting to say enterprise. >> Yeah enterprise >> Intelligent experience >> Experience product, with Teams, with the Azure announcement, integration with Azure and full support of the Citrix platform inside Azure will just make the employee experience at least potentially seamless, a lot more seamless, I'm super excited about, you can't tell in my voice, I haven't gotten excited about Citrix in a long time. And this is the first time they've had theCUBE at Synergy since 2011, I think it was a great time to reignite that partnership, and this coverage is going to be an interesting two days. >> It is. So we talked about digital workspace, the other two areas of Citrix's business that you touched on a little bit, security and analytics. Let's talk about the security piece first as it relates to Microsoft Teams and Azure. SD-WAN is becoming more and more absolutely critical to ensure that because as people we are the number one threat vector in any organization. Not that we're all bad actors. >> Keith: Right. >> But because we need to get things done, as frictionless or seamless, as you said, as possible, and efficiently as possible. What did you hear today with respect to security, that might really make some of those IT folks take notice? >> Well, we want to work from any device. Like, I want to be able to, ideally if I say, you know what, I want to pick up a new Surface tablet, when I go to Atlanta I don't want to pack my iPad. I want to be able to pick that up, and work. If I go to a kiosk, I want to be able to, even if it's running Windows XP, I want to be able to do my work, I want to be able to do my work from any device. This is a nightmare for system administrators to say how do I control security, while making the experience frictionless? Those two things don't seem to go together. So Citrix, whether it's with this new announcement with Microsoft with Teams, it's traditional applications around SD-WAN, enabling access from remote locations, and Citrix is kind ... this is their bread and butter, offering remote access to applications securely and fast, this is you know, Citrix is starting to formulate a really great end to end story about making applications, data and more importantly, business answers and capability available anywhere securely, so it's a great story. >> It really is. So if you're excited, you already know how excited I am. I think we're going to have a fantastic day today, and tomorrow. We've got a whole bunch of the C-Suite from Citrix on, we're also going to be talking with some partners and customers, and interestingly as a marketer this peaked my interest as well, they have the innovation awards. There are three finalists, we will be talking with all three over the next two days, and this is a customer awards program, that anybody can vote on. So I haven't seen that before, so I'm excited to understand how Citrix is enabling them to have this great employee experience which is more and more critical as the shortages and the gaps are becoming more and more prevalence. And also, how these customers are reacting to just some of the news announced today, with Microsoft, the intelligent enterprise, and how they see their employees, and attracting and retaining top talent as actually really mission critical. So we're going to have fun Keith. >> I agree. >> All right, you're watching Keith Townsend and Lisa Martin live from theCUBE, we are on the show floor at Citrix Synergy 2019 from Atlanta, Georgia. Stick around, Keith and I will be right back with our first guest after a short break. (upbeat electronic music)

>> From Times Square, in the heart of New York City, it's theCUBE. Covering Imagine 2018. Brought to you by, Automation Anywhere. >> Welcome back everybody, Jeff Frick here with theCUBE. We're in Manhattan, New York City, at Automation Anywhere's Imagine Conference 2018. About 1,100 professionals really talking about the future of work bots, and really how automation is gonna help people do the mundane a little bit easier, and hopefully free us all up to do stuff that's a little bit more important, a little higher value. We're excited to have our next guest, he's Craig Le Clair, the VP and Principal Analyst from Forrester, and he's been covering this space for a long time. Craig, great to see ya. >> Yeah, nice to see you, thanks for having me on. >> So, first off, just kind of general impressions of the event? Have you been to this before? It's our first time. >> Yes, I did a talk here last year, so it was a little bit smaller then. There's obviously more people here today, but it's pretty much, I think it was in Brooklyn last year. >> It was in Brooklyn, okay. >> So, this is an upgrade. >> So, RP Robotic Process Automation, more affectionately, probably termed as bots. >> Yeah. >> They're growing, we're seeing more and more time and our own interactions with companies, kind of on the customer service side. How are they changing the face of work? How are they evolving as really a way for companies to get more leverage? >> Yeah, so I'll make one clarification of your sentence, and that's, you know, bots do things on behalf of people. What we're talking to in a call center environment is a chat bot. So, they have the ability to communicate or really, I would say, attempt to communicate with people. They're not doing a very good job of it in my view. But, bots work more in the background, and they'll do things for you, right? So, you know, they're having a tremendous effect. I mean, one of the statistics I was looking at the other day, per one billion dollars of revenue, the average company had about 150 employees in finance and accounting ten years ago. Now, instead of having 120 or 130, it's already down to 70 or 80, and that's because the bots that we're talking about here can mimic that human activity for posting to a general ledger, for switching between applications, and really, move those folks on to different occupations, shall we say. >> Right, right. >> Yeah. >> Well it's funny, Jeff Immelt just gave his little keynote address, and he said, "This is the easiest money you'll find in digital transformation is implementing these types of technology." >> Yeah, it's a good point, and it was a great talk, by the way, by Jeff. But, you know, companies have been under a lot of pressure to digitally transform. >> Right. >> You know, due to really the mobile, you know, mobile peaked around 2012, and that pushed everyone into this gap that companies couldn't really deal with the consumer technology that was out there, right? So then you had the Ubers of the world and digital transformation. So, there's been a tremendous focus on digital transformation, but very little progress. >> Right. >> When we do surveys, only 11% are showing any progress at all. So, along comes this technology, Robotic Process Automation that allows you to build bots without changing any of the back end systems. There's no data integration. You know, there's no APIs involved. There's no big transformation consultants flying in. There's not even a Requirements Document because you're gonna start with recording the actual human activity at a work station. >> Right. >> So, it's been an elixir, you know, frankly for CIOs to go into their boss and say, "You know what, we're doing great, you know, I've just made this invoice process exist in a lot better way." You know, we're on our path to digital transformation. >> And it's really a different strategy, because, like you said, it's not kind of rip and replace the old infrastructure, you're not rewriting a lot of applications, you're really overlaying it, right? >> Which is one of the potential downfalls is that, you know, sometimes you need to move to that new cloud platform. You don't want, to some extent, the technology institutionalizes what could be a very bad process, one that needs to be modernized, one that needs to be blown up. You know, we're still using the airline reservation systems from 1950s, and layers, and layers, and layers and layers built upon them. At some point, you're gonna have to design a new experience with new technology, so there's some dangers with the seduction of building bots against core systems. >> Right, so the other thing that's happening is the ongoing, I love Moore's Law, it's much more about an attitude then the physics of a microprocessor, but you know, compute, and store, and networking, 5Gs just around the corner, cloud-based systems now really make that available in a much different way, and as you said, mobile experience delivers it to us. So as those continue to march on and asymptomatically approach zero and infinite scale, we're not there yet, but we're everyday getting a little bit closer. Now we're seeing AI, we're seeing machine-learning, >> Yes. >> We're seeing a new kind of class of horsepower, if you will, that just wasn't available before at the scale it's at today. So, now you throw that into the mix, these guys have been around 14 years, how does AI start to really impact things? >> It's a fascinating subject and question. I mean, we're, at Forrester, talking about the forces of automation. And, by the way, RPA is just a subset of a whole set of technologies: AI, you mentioned, and AI is a subset of automation, and there's Deep Learning, is a subset of AI and you go on and on, there are 30, 40 different automation technologies. And these will have tremendous force, both on jobs in the future, and on shifting control really to machines. So, right now, you can look at this little bubble we had of consumer technology and mobile, shifting a lot of power to the consumer, and that's been great for our convenience, but now with algorithms being developed that are gonna make more and more decisions, you could argue that the power is going to shift back to those who own the machines, and those who own the algorithms. So, there's a power shift, a control shift that we're really concerned about. There's a convergence of the physical and digital world, which is IOT and so forth, and that's going to drive new scale in companies, which are gonna further dehumanize some of our life, right? So that affects, it squeezes humans out of the process. Blockchain gets rid of intermediaries that are there to really transfer ideas and money and so forth. So, all of these forces of automation, which we think is gonna be the next big conversation in the industry, are gonna have tremendous effect societally and in business. >> Right. Well, there's certainly, you know, there's the case where you just you can't necessarily rescale a whole class of an occupation, right? The one that we're all watching for, obviously, is truck drivers, right? Employs a ton of people, autonomous vehicles are right around the corner. >> Right. >> On the other hand, there's going to be new jobs that we don't even know what they're gonna be yet, to quote all the graduating seniors, it's graduation season, most of them are going to work in jobs that don't even exist 10 years from now. >> Correct, correct, very true. >> And the other thing is every company we talk to has got tons of open reqs, and they can't get enough people to fulfill what they need, and then Mihir, I think touched on an interesting point in the keynote, where, ya know, now we're starting to see literal population growth slow down in developed countries, >> Yes. >> Like in Japan is at the leading edge, and you mentioned Europe, and I'm not sure where the US is, so it's kind of this interesting dichotomy: On one side, machines are going to take more and more of our jobs, or more and more portions of our job. On the other hand, we don't have people to do those jobs necessarily anyway, not necessarily today, but down the road, and you know, will we get to more of this nirvana-state where people are being used to do higher-value types of activities, and we can push off some of this, the crap and mundane that still, unfortunately, takes such a huge portion of our day to day world? >> Yeah, yeah. So, one thought that some of us believe at Forrester, I being one of them, is that we're at a, kind of, neutral right point now where a lot of the AI, which is really the most disruptive element we're talking about here, our PA is no autonomous learning capability, there's no AI component to our PA. But, when AI kicks in, and we've seen evidence of it as we always do first in the consumer world where it's a light version of AI in Netflix. There's no unlimited spreadsheets sitting there figuring out which one to watch, right? They're taking in data about your behavior, putting you in clusters, mapping them to correlating them, and so forth. We think that business hasn't really gotten going with AI yet, so in other words, this period that you just described, where there seems to be 200,000 people hired every month in the ADP reports, you know, and there's actually 50,000 truck driver jobs open right now. And you see help-wanted signs everywhere. >> Right, right. >> We think that's really just because business hasn't really figured out what to do with technology yet. If you project three or four years, our projections are that there will be a significant number of, particular in the cubicles that our PA attacks, a significant number of dislocation of current employment. And that's going to create this job transformation, we think, is going to be more the issue then replacement. And if you go back in history, automations have always led to transformation. >> Right. >> And I won't go through the examples because we don't have time, but there are many. And we think that's going to be the case here in that automation dividends, we call them, are going to be, are being way underestimated, that they're going to be new opportunities, and so forth. The skills mis-match is the issue that, you know, you have what RPA attacks are the 60 million that are in cubicles today in the US. And the average education there is high school. So, they're not gonna be thrown out of the cubicles and become data scientists overnight, right? So, there's going to be a massive growth in the gig economy, and there's an informal and a formal segment of that, that's going to result in people having to patch together their lives in ways they they hadn't had before, so there's gonna be some pain there. But there are also going to be some strong dividends that will result from this level of productivity that we're gonna see, again, in a few years, cause I think we're at a neutral point right now. >> Well, Amara's Law doesn't get enough credit, right? We overestimate in the short-term, and then underestimate the long-term needs affect. >> Absolutely. >> And one of the big things on AI is really moving from this, in real time, right? And all these fast databases and fast analytics, is we move from a world where we are looking in the rear view mirror and making decisions on what happened in the past to you know, getting more predictive, and then even more prescriptive. >> Yes. >> So, you know, the value unlock there is very very real, I'm never fascinated to be amazed by how much inefficiency there still is every time we go to these conferences. (Craig laughs) You know we thought we solved it all at SAP and ERP, that was clearly-- >> Clearly not the case. Funny work to do. >> But, it's even interesting, even from last year, you mentioned that there the significant delta just from year to year is pretty amazing. >> Yes, I've been amazed at the level of innovation in the core digital worker platforms, the RPA platforms, in the last year has been pretty amazing work. What we were talking about a year ago when I spoke at this conference, and what we're talking about now, the areas are different. You know, we're not talking about basic control of the applications of the desktop. We're talking about integration with text analytics. We're talking about comp combining process mining information with desktop analytics to create new visions of the process. You know, we weren't talking about any of that a year ago. We're talking about bot stores. They're out there, and downloadable robots. Again, not talking about last year at all. So, just a lot of good progress, good solid progress, and I'm very happy to be a part of it. >> And really this kind of the front end scene of so much of the development is manifested on the front end, where we used to always talk about citizen developers back in the day. You know, Fred Luddy, who was just highlighted Service Now, most innovative company. That was his, you know, vision of Citizen Developer. And then we've talked about citizen integrators, which is really an interesting concept, and now we're talking about really citizens, or analysts, having the ability via these tools to do integrations and to deliver new kind of work flows that really weren't possible before unless you were a hardcore programmer. >> Yeah, although I think that conversation is a little bit premature in this space, right? I think that most of the bot development requires programming skills today, and they're going to get more complicated in that most of the bot activities today are doing, you know, three decisions or less. Or they're looking at four or five apps that are involved, or they're doing a series of four or five hundred clicks that they're emulating. And the progression is to get the digital workers to get smarter and incorporating various AI components, so you're going to have to build, be able to deal statistically with algorithm developments, and data, and learning, and all of that. So, it's not.... The core of this, the part of it that's going to be more disruptive to business is going to be done by pretty skilled developers, and programmers, and data scientists, and statistical, you know, folks that are going through. But, having said that, you're going to have a digital workforce that's got to be managed, and you know, has to be viewed as an employee at some level to get the proper governance. So you have to know when that digital worker was born, when they were hired, who do they report to, when were they terminated, and what their performance review is. You gotta be doing performance reviews on the digital workers with the kind of dashboard analytics that we have. And that's the only way to really govern, because the distinction in this category is that you're giving these bots human credentials, and you're letting them access the most trusted application boundaries, areas, in a company. So, you better treat them like employees if you want proper governance. >> Which becomes tricky as Mihir said when you go from one bot to ten bots to ten thousand. Then the management of this becomes not insignificant. >> Right. >> So Craig, I want to give you the last word. You said, you know, big changes since last year. If we sit down a year from now, 2019, _ Oh. >> Lord knows where we'll be. What are we gonna talk about? What do you see as kind of the next, you know, 12-month progression? >> You know, I hope we don't go to Jersey after Brooklyn, New York, and-- >> Keep moving. >> I see Jersey over there, but it's where it belongs, you know, across the river. I'm from Jersey, so I can say that. You know, I think next year we're gonna see more integration of AI modules into the digital worker. I think with a lot of these explosive markets, like RPA is, there's always a bit of cooling off period, and I think you're going to see some tapering off of the growth of some of the platform companies, AA, but also their peers and compatriots. That's natural. I think that the area has been a little bit, you know, analysis and tech-industry loves change. If there's no change, there's nothing for us to write about. So, we usually over-project. Now, in this case, the 2.8 billion-dollar market project five years out that I did is being exceeded, which is rare. But I expect some tapering off in a year where there's not a ceiling hit, but that, you know, you end up with going through these more simple applications that can be robotized easily. And now you're looking at slightly more complicated scenarios that take a little more, you know, AI and analytics embedded-ness, and require a little more care, they have a little more opaque, and a little more thought, and that'll slow things down a bit. But, I still think we're on our way to a supermarket and a lot of productivity here. >> So just a little less low-hanging fruit, and you gotta step up the game a little bit. >> I guess you could, you said it much simpler then I did. >> I'm a simple guy, Craig. >> But that's why you're the expert on this panelist. >> Alright, Craig, well thanks for sharing your insight, >> Alright. >> Really appreciate it, and do look forward to talking to you next year, and we'll see if that comes true. >> Alright, appreciate it, take care now. >> He's Craig Le Clair and I'm Jeff Frick. You're watching theCUBE from Automation Anywhere Imagine 2018.

>> Announcer: Live from New York, it's theCUBE. Covering machine learning everywhere. Build your ladder to AI. Brought to you by IBM. Welcome back to New York City. Along with Dave Vellante, I'm John Walls. We continue our coverage here on theCUBE of machine learning everywhere. Build your ladder to AI, IBM our host here today. We put together, occasionally at these events, a panel of esteemed experts with deep perspectives on a particular subject. Today our influencer panel is comprised of three well-known and respected authorities in this space. Glad to have Colin Sumpter here with us. He's the man with the mic, by the way. He's going to talk first. But, Colin is an IT architect with CrowdMole. Thank you for being with us, Colin. Jennifer Shin, those of you on theCUBE, you're very familiar with Jennifer, a long time Cuber. Founded 8 Path Solutions, on the faculty at NYU and Cal Berkeley, and also with us is Craig Brown, a big data consultant. And a home game for all of you guys, right, more or less here we are in the city. So, thanks for having us, we appreciate the time. First off, let's just talk about the title of the event, Build Your Path... Or Your Ladder, excuse me, to AI. What are those steps on that ladder, Colin? The fundamental steps that you've got to jump on, or step on, in order to get to that true AI environment? >> In order to get to that true AI environment, John, is a matter of mastering or organizing your information well enough to perform analytics. That'll give you two choices to do either linear regression or supervised classification, and then you actually have enough organized data to talk to your team and organize your team around that data to begin that ladder to successively benefit from your data science program. >> Want to take a stab at it, Jennifer? >> So, I would say, compute, right? You need to have the right processing, or at least the ability to scale out to be able to process the algorithm fast enough to be able to find value in your data. I think the other thing is, of course, the data source itself. Do you have right data to answer the questions you want to answer? So, I think, without those two things, you'll either have a lot of great data that you can't process in time, or you'll have a great process or a great algorithm that has no real information, so your output is useless. I think those are the fundamental things you really do need to have any sort of AI solution built. >> I'll take a stab at it from the business side. They have to adopt it first. They have to believe that this is going to benefit them and that the effort that's necessary in order to build into the various aspects of algorithms and data subjects is there, so I think adopting the concept of machine learning and the development aspects that it takes to do that is a key component to building the ladder. >> So this just isn't toe in the water, right? You got to dive in the deep end, right? >> Craig: Right. >> It gets to culture. If you look at most organizations, not the big five market capped companies, but most organizations, data is not at their core. Humans are at their core, human expertise and data is sort of bolted on, but that has to change, or they're going to get disrupted. Data has to be at the core, maybe the human expertise leverages that data. What do you guys seeing with end customers in terms of their readiness for this transformation? >> What I'm seeing customers spending time right now is getting out of the silos. So, when you speak culture, that's primarily what the culture surrounds. They develop applications with functionality as a silo, and data specific to that functionality is the component in which they look at data. They have to get out of that mindset and look at the data holistically, and ultimately, in these events, looking at it as an asset. >> The data is a shared resource. >> Craig: Right, correct. >> Okay, and again, with the exception of the... Whether it's Google, Facebook, obviously, but the Ubers, the AirBNB's, etc... With the exception of those guys, most customers aren't there. Still, the data is in silos, they've got myriad infrastructure. Your thoughts, Jennifer? >> I'm also seeing sort of a disconnect between the operationalizing team, the team that runs these codes, or has a real business need for it, and sometimes you'll see corporations with research teams, and there's sort of a disconnect between what the researchers do and what these operations, or marketing, whatever domain it is, what they're doing in terms of a day to day operation. So, for instance, a researcher will look really deep into these algorithms, and may know a lot about deep learning in theory, in theoretical world, and might publish a paper that's really interesting. But, that application part where they're actually being used every day, there's this difference there, where you really shouldn't have that difference. There should be more alignment. I think actually aligning those resources... I think companies are struggling with that. >> So, Colin, we were talking off camera about RPA, Robotic Process Automation. Where's the play for machine intelligence and RPA? Maybe, first of all, you could explain RPA. >> David, RPA stands for Robotic Process Automation. That's going to enable you to grow and scale a digital workforce. Typically, it's done in the cloud. The way RPA and Robotic Process Automation plays into machine learning and data science, is that it allows you to outsource business processes to compensate for the lack of human expertise that's available in the marketplace, because you need competency to enable the technology to take advantage of these new benefits coming in the market. And, when you start automating some of these processes, you can keep pace with the innovation in the marketplace and allow the human expertise to gradually grow into these new data science technologies. >> So, I was mentioning some of the big guys before. Top five market capped companies: Google, Amazon, Apple, Facebook, Microsoft, all digital. Microsoft you can argue, but still, pretty digital, pretty data oriented. My question is about closing that gap. In your view, can companies close that gap? How can they close that gap? Are you guys helping companies close that gap? It's a wide chasm, it seems. Thoughts? >> The thought on closing the chasm is... presenting the technology to the decision-makers. What we've learned is that... you don't know what you don't know, so it's impossible to find the new technologies if you don't have the vocabulary to just begin a simple research of these new technologies. And, to close that gap, it really comes down to the awareness, events like theCUBE, webinars, different educational opportunities that are available to line of business owners, directors, VP's of systems and services, to begin that awareness process, finding consultants... begin that pipeline enablement to begin allowing the business to take advantage and harness data science, machine learning and what's coming. >> One of the things I've noticed is that there's a lot of information out there, like everyone a webinar, everyone has tutorials, but there's a lot of overlap. There aren't that many very sophisticated documents you can find about how to implement it in real world conditions. They all tend to use the same core data set, a lot of these machine learning tutorials you'll find, which is hilarious because the data set's actually very small. And I know where it comes from, just from having the expertise, but it's not something I'd ever use in the real world. The level of skill you need to be able to do any of these methodologies. But that's what's out there. So, there's a lot of information, but they're kind of at a rudimentary level. They're not really at that sophisticated level where you're going to learn enough to deploy in real world conditions. One of the things I'm noticing is, with the technical teams, with the data science team, machine learning teams, they're kind of using the same methodologies I used maybe 10 years ago. Because the management who manage these teams are not technical enough. They're business people, so they don't understand how to guide them, how to explain hey maybe you shouldn't do that with your code, because that's actually going to cause a problem. You should use parallel code, you should make sure everything is running in parallel so compute's faster. But, if these younger teams are actually learning for the first time, they make the same mistakes you made 10 years ago. So, I think, what I'm noticing is that lack of leadership is partly one of the reasons, and also the assumption that a non-technical person can lead the technical team. >> So, it's just not skillset on the worker level, if you will. It's also knowledge base on the decision-maker level. That's a bad place to be, right? So, how do you get into the door to a business like that? Obviously, and we've talked about this a little bit today, that some companies say, "We're not data companies, we're not digital companies, we sell widgets." Well, yeah but you sell widgets and you need this to sell more widgets. And so, how do you get into the door and talk about this problem that Jennifer just cited? You're signing the checks, man. You're going to have to get up to speed on this otherwise you're not going to have checks to sign in three to five years, you're done! >> I think that speaks to use cases. I think that, and what I'm actually saying at customers, is that there's a disconnect and an understanding from the executive teams and the low-level technical teams on what the use case actually means to the business. Some of the use cases are operational in nature. Some of the use cases are data in nature. There's no real conformity on what does the use case mean across the organization, and that understanding isn't there. And so, the CIO's, the CEO's, the CTO's think that, "Okay, we're going to achieve a certain level of capability if we do a variety of technological things," and the business is looking to effectively improve some or bring some efficiency to business processes. At each level within the organization, the understanding is at the level at which the discussions are being made. And so, I'm in these meetings with senior executives and we have lots of ideas on how we can bring efficiencies and some operational productivity with technology. And then we get in a meeting with the data stewards and "What are these guys talking about? They don't understand what's going on at the data level and what data we have." And then that's where the data quality challenges come into the conversation, so I think that, to close that cataclysm, we have to figure out who needs to be in the room to effectively help us build the right understanding around the use cases and then bring the technology to those use cases then actually see within the organization how we're affecting that. >> So, to change the questioning here... I want you guys to think about how capable can we make machines in the near term, let's talk next decade near term. Let's say next decade. How capable can we make machines and are there limits to what we should do? >> That's a tough one. Although you want to go next decade, we're still faced with some of the challenges today in terms of, again, that adoption, the use case scenarios, and then what my colleagues are saying here about the various data challenges and dev ops and things. So, there's a number of things that we have to overcome, but if we can get past those areas in the next decade, I don't think there's going to be much of a limit, in my opinion, as to what the technology can do and what we can ask the machines to produce for us. As Colin mentioned, with RPA, I think that the capability is there, right? But, can we also ultimately, as humans, leverage that capability effectively? >> I get this question a lot. People are really worried about AI and robots taking over, and all of that. And I go... Well, let's think about the example. We've all been online, probably over the weekend, maybe it's 3 or 4 AM, checking your bank account, and you get an error message your password is wrong. And we swear... And I've been there where I'm like, "No, no my password's right." And it keeps saying that the password is wrong. Of course, then I change it, and it's still wrong. Then, the next day when I login, I can login, same password, because they didn't put a great error message there. They just defaulted to wrong password when it's probably a server that's down. So, there are these basics or processes that we could be improving which no one's improving. So you think in that example, how many customer service reps are going to be contacted to try to address that? How many IT teams? So, for every one of these bad technologies that are out there, or technologies that are not being run efficiently or run in a way that makes sense, you actually have maybe three people that are going to be contacted to try to resolve an issue that actually maybe could have been avoided to begin with. I feel like it's optimistic to say that robots are going to take over, because you're probably going to need more people to put band-aids on bad technology and bad engineering, frankly. And I think that's the reality of it. If we had hoverboards, that would be great, you know? For a while, we thought we did, right? But we found out, oh it's not quite hoverboards. I feel like that might be what happens with AI. We might think we have it, and then go oh wait, it's not really what we thought it was. >> So there are real limits, certainly in the near to mid to maybe even long term, that are imposed. But you're an optimist. >> Yeah. Well, not so much with AI but everything else, sure. (laughing) AI, I'm a little bit like, "Well, it would be great, but I'd like basic things to be taken care of every day." So, I think the usefulness of technology is not something anyone's talking about. They're talking about this advancement, that advancement, things people don't understand, don't know even how to use in their life. Great, great is an idea. But, what about useful things we can actually use in our real life? >> So block and tackle first, and then put some reverses in later, if you will, to switch over to football. We were talking about it earlier, just about basics. Fundamentals, get your fundamentals right and then you can complement on that with supplementary technologies. Craig, Colin? >> Jen made some really good points and brought up some very good points, and so has... >> John: Craig. >> Craig, I'm sorry. (laughing) >> Craig: It's alright. >> 10 years out, Jen and Craig spoke to false positives. And false positives create a lot of inefficiency in businesses. So, when you start using machine learning and AI 10 years from now, maybe there's reduced false positives that have been scored in real time, allowing teams not to have their time consumed and their business resources consumed trying to resolve false positives. These false positives have a business value that, today, some businesses might not be able to record. In financial services, banks count money not lended. But, in every day business, a lot of businesses aren't counting the monetary consequences of false positives and the drag it has on their operational ability and capacity. >> I want to ask you guys about disruption. If you look at where the disruption, the digital disruptions, have taken place, obviously retail, certainly advertising, certainly content businesses... There are some industries that haven't been highly disruptive: financial services, insurance, we were talking earlier about aerospace, defense rather. Is any business, any industry, safe from digital disruption? >> There are. Certain industries are just highly regulated: healthcare, financial services, real estate, transactional law... These are very extremely regulated technologies, or businesses, that are... I don't want to say susceptible to technology, but they can be disrupted at a basic level, operational efficiency, to make these things happen, these business processes happen more rapidly, more accurately. >> So you guys buy that? There's some... I'd like to get a little debate going here. >> So, I work with the government, and the government's trying to change things. I feel like that's kind of a sign because they tend to be a little bit slower than, say, other private industries, or private companies. They have data, they're trying to actually put it into a system, meaning like if they have files... I think that, at some point, I got contacted about putting files that they found, like birth records, right, marriage records, that they found from 100-plus years ago and trying to put that into the system. By the way, I did look into it, there was no way to use AI for that, because there was no standardization across these files, so they have half a million files, but someone's probably going to manually have to enter that in. The reality is, I think because there's a demand for having things be digital, we aren't likely to see a decrease in that. We're not going to have one industry that goes, "Oh, your files aren't digital." Probably because they also want to be digital. The companies themselves, the employees themselves, want to see that change. So, I think there's going to be this continuous move toward it, but there's the question of, "Are we doing it better?" It is better than, say, having it on paper sometimes? Because sometimes I just feel like it's easier on paper than to have to look through my phone, look through the app. There's so many apps now! >> (laughing) I got my index cards cards still, Jennifer! Dave's got his notebook! >> I'm not sure I want my ledger to be on paper... >> Right! So I think that's going to be an interesting thing when people take a step back and go like, "Is this really better? Is this actually an improvement?" Because I don't think all things are better digital. >> That's a great question. Will the world be a better, more prosperous place... Uncertain. Your thoughts? >> I think the competition is probably the driver as to who has to this now, who's not safe. The organizations that are heavily regulated or compliance-driven can actually use that as the reasoning for not jumping into the barrel right now, and letting it happen in other areas first, watching the technology mature-- >> Dave: Let's wait. >> Yeah, let's wait, because that's traditionally how they-- >> Dave: Good strategy in your opinion? >> It depends on the entity but I think there's nothing wrong with being safe. There's nothing wrong with waiting for a variety of innovations to mature. What level of maturity, I think, is the perspective that probably is another discussion for another day, but I think that it's okay. I don't think that everyone should jump in. Get some lessons learned, watch how the other guys do it. I think that safety is in the eyes of the beholder, right? But some organizations are just competition fierce and they need a competitive edge and this is where they get it. >> When you say safety, do you mean safety in making decisions, or do you mean safety in protecting data? How are you defining safety? >> Safety in terms of when they need to launch, and look into these new technologies as a basis for change within the organization. >> What about the other side of that point? There's so much more data about it, so much more behavior about it, so many more attitudes, so on and so forth. And there is privacy issues and security issues and all that... Those are real challenges for any company, and becoming exponentially more important as more is at stake. So, how do companies address that? That's got to be absolutely part of their equation, as they decide what these future deployments are, because they're going to have great, vast reams of data, but that's a lot of vulnerability too, isn't it? >> It's as vulnerable as they... So, from an organizational standpoint, they're accustomed to these... These challenges aren't new, right? We still see data breaches. >> They're bigger now, right? >> They're bigger, but we still see occasionally data breaches in organizations where we don't expect to see them. I think that, from that perspective, it's the experiences of the organizations that determine the risks they want to take on, to a certain degree. And then, based on those risks, and how they handle adversity within those risks, from an experience standpoint they know ultimately how to handle it, and get themselves to a place where they can figure out what happened and then fix the issues. And then the others watch while these risk-takers take on these types of scenarios. >> I want to underscore this whole disruption thing and ask... We don't have much time, I know we're going a little over. I want to ask you to pull out your Hubble telescopes. Let's make a 20 to 30 year view, so we're safe, because we know we're going to be wrong. I want a sort of scale of 1 to 10, high likelihood being 10, low being 1. Maybe sort of rapid fire. Do you think large retail stores are going to mostly disappear? What do you guys think? >> I think the way that they are structured, the way that they interact with their customers might change, but you're still going to need them because there are going to be times where you need to buy something. >> So, six, seven, something like that? Is that kind of consensus, or do you feel differently Colin? >> I feel retail's going to be around, especially fashion because certain people, and myself included, I need to try my clothes on. So, you need a location to go to, a physical location to actually feel the material, experience the material. >> Alright, so we kind of have a consensus there. It's probably no. How about driving-- >> I was going to say, Amazon opened a book store. Just saying, it's kind of funny because they got... And they opened the book store, so you know, I think what happens is people forget over time, they go, "It's a new idea." It's not so much a new idea. >> I heard a rumor the other day that their next big acquisition was going to be, not Neiman Marcus. What's the other high end retailer? >> Nordstrom? >> Nordstrom, yeah. And my wife said, "Bad idea, they'll ruin it." Will driving and owning your own car become an exception? >> Driving and owning your own car... >> Dave: 30 years now, we're talking. >> 30 years... Sure, I think the concept is there. I think that we're looking at that. IOT is moving us in that direction. 5G is around the corner. So, I think the makings of it is there. So, since I can dare to be wrong, yeah I think-- >> We'll be on 10G by then anyway, so-- >> Automobiles really haven't been disrupted, the car industry. But you're forecasting, I would tend to agree. Do you guys agree or no, or do you think that culturally I want to drive my own car? >> Yeah, I think people, I think a couple of things. How well engineered is it? Because if it's badly engineered, people are not going to want to use it. For instance, there are people who could take public transportation. It's the same idea, right? Everything's autonomous, you'd have to follow in line. There's going to be some system, some order to it. And you might go-- >> Dave: Good example, yeah. >> You might go, "Oh, I want it to be faster. I don't want to be in line with that autonomous vehicle. I want to get there faster, get there sooner." And there are people who want to have that control over their lives, but they're not subject to things like schedules all the time and that's their constraint. So, I think if the engineering is bad, you're going to have more problems and people are probably going to go away from wanting to be autonomous. >> Alright, Colin, one for you. Will robots and maybe 3D printing, for example RPA, will it reverse the trend toward offshore manufacturing? >> 30 years from now, yes. I think robotic process engineering, eventually you're going to be at your cubicle or your desk, or whatever it is, and you're going to be able to print office supplies. >> Do you guys think machines will make better diagnoses than doctors? Ohhhhh. >> I'll take that one. >> Alright, alright. >> I think yes, to a certain degree, because if you look at the... problems with diagnosis, right now they miss it and I don't know how people, even 30 years from now, will be different from that perspective, where machines can look at quite a bit of data about a patient in split seconds and say, "Hey, the likelihood of you recurring this disease is nil to none, because here's what I'm basing it on." I don't think doctors will be able to do that. Now, again, daring to be wrong! (laughing) >> Jennifer: Yeah so--6 >> Don't tell your own doctor either. (laughing) >> That's true. If anything happens, we know, we all know. I think it depends. So maybe 80%, some middle percentage might be the case. I think extreme outliers, maybe not so much. You think about anything that's programmed into an algorithm, someone probably identified that disease, a human being identified that as a disease, made that connection, and then it gets put into the algorithm. I think what w6ll happen is that, for the 20% that isn't being done well by machine, you'll have people who are more specialized being able to identify the outlier cases from, say, the standard. Normally, if you have certain symptoms, you have a cold, those are kind of standard ones. If you have this weird sort of thing where there's n6w variables, environmental variables for instance, your environment can actually lead to you having cancer. So, there's othe6 factors other than just your body and your health that's going to actually be important to think about wh6n diagnosing someone. >> John: Colin, go ahead. >> I think machines aren't going to out-decision doctors. I think doctors are going to work well the machine learning. For instance, there's a published document of Watson doing the research of a team of four in 10 minutes, when it normally takes a month. So, those doctors,6to bring up Jen and Craig's point, are going to have more time to focus in on what the actual symptoms are, to resolve the outcome of patient care and patient services in a way that benefits humanity. >> I just wish that, Dave, that you would have picked a shorter horizon that... 30 years, 20 I feel good about our chances of seeing that. 30 I'm just not so sure, I mean... For the two old guys on the panel here. >> The consensus is 20 years, not so much. But beyond 10 years, a lot's going to change. >> Well, thank you all for joining this. I always enjoy the discussions. Craig, Jennifer and Colin, thanks for being here with us here on theCUBE, we appreciate the time. Back with more here from New York right after this. You're watching theCUBE. (upbeat digital music)