>> Hello everyone. On today's panel, the theme is Drug Discovery and how Artificial Intelligence can make a difference. On the panel today, we are honored to have Dr. Ryan Yates, principal scientist at The National Center for Natural Products Research, with a focus on botanicals specifically the pharmacokinetics, which is essentially how the drug changes over time in our body and pharmacodynamics which is essentially how drugs affects our body. And of particular interest to him is the use of AI in preclinical screening models to identify chemical combinations that can target chronic inflammatory processes such as fatty liver disease, cognitive impairment and aging. Welcome, Ryan. Thank you for coming. >> Good morning. Thank you for having me. >> The other distinguished panelist is Dr. Rangan Sukumar, our very own, is a distinguished technologist at the CTO office for High Performance Computing and Artificial Intelligence with a PHD in AI and 70 publications that can be applied in drug discovery, autonomous vehicles and social network analysis. Hey Rangan, welcome. Thank you for coming, by sparing the time. We have also our distinguished Chris Davidson. He is leader of our HPC and AI Application and Performance Engineering team. His job is to tune and benchmark applications, particularly in the applications of weather, energy, financial services and life sciences. Yes so particular interest is life sciences he spent 10 years in biotech and medical diagnostics. Hi Chris, welcome. Thank you for coming. >> Nice to see you. >> Well let's start with your Chris, yes, you're regularly interfaced with pharmaceutical companies and worked also on the COVID-19 White House Consortium. You know tell us, let's kick this off and tell us a little bit about your engagement in the drug discovery process. >> Right and that's a good question I think really setting the framework for what we're talking about here is to understand what is the drug discovery process. And that can be kind of broken down into I would say four different areas, there's the research and development space, the preclinical studies space, clinical trial and regulatory review. And if you're lucky, hopefully approval. Traditionally this is a slow arduous process it costs a lot of money and there's a high amount of error. Right, however this process by its very nature is highly iterate and has just huge amounts of data, right it's very data intensive, right and it's these characteristics that make this process a great target for kind of new approaches in different ways of doing things. Right, so for the sake of discussion, right, go ahead. >> Oh yes, so you mentioned data intensive brings to mind Artificial Intelligence, you know, so Artificial Intelligence making the difference here in this process, is that so? >> Right, and some of those novel approaches are actually based on Artificial Intelligence whether it's deep learning and machine learning, et cetera, you know, prime example would say, let's just say for the sake of discussion, let's say there's a brand new virus, causes flu-like symptoms, shall not be named if we focus kind of on the R and D phase, right our goal is really to identify target for the treatment and then screen compounds against it see which, you know, which ones we take forward right to this end, technologies like cryo-electron, cryogenic electron microscopy, just a form of microscopy can provide us a near atomic biomolecular map of the samples that we're studying, right whether that's a virus, a microbe, the cell that it's attaching to and so on, right AI, for instance, has been used in the particle picking aspect of this process. When you take all these images, you know, there are only certain particles that we want to take and study, right whether they have good resolution or not whether it's in the field of the frame and image recognition is a huge part of this, it's massive amounts of data in AI can be very easily, you know, used to approach that. Right, so with docking, you can take the biomolecular maps that you achieved from cryo-electron microscopy and you can take those and input that into the docking application and then run multiple iterations to figure out which will give you the best fit. AI again, right, this is iterative process it's extremely data intensive, it's an easy way to just apply AI and get that best fit doing something in a very, you know, analog manner that would just take humans very long time to do or traditional computing a very long time to do. >> Oh, Ryan, Ryan, you work at the NCNPR, you know, very exciting, you know after all, you know, at some point in history just about all drugs were from natural products yeah, so it's great to have you here today. Please tell us a little bit about your work with the pharmaceutical companies, especially when it is often that drug cocktails or what they call Polypharmacology, is the answer to complete drug therapy. Please tell us a bit more with your work there. >> Yeah thank you again for having me here this morning Dr. Goh, it's a pleasure to be here and as you said, I'm from the National Center for Natural Products Research you'll hear me refer to it as the NCNPR here in Oxford, Mississippi on the Ole Miss Campus, beautiful setting here in the South and so, what, as you said historically, what the drug discovery process has been, and it's really not a drug discovery process is really a therapy process, traditional medicine is we've looked at natural products from medicinal plants okay, in these extracts and so where I'd like to begin is really sort of talking about the assets that we have here at the NCNPR one of those prime assets, unique assets is our medicinal plant repository which comprises approximately 15,000 different medicinal plants. And what that allows us to do, right is to screen mine, that repository for activities so whether you have a disease of interest or whether you have a target of interest then you can use this medicinal plant repository to look for actives, in this case active plants. It's really important in today's environment of drug discovery to really understand what are the actives in these different medicinal plants which leads me to the second unique asset here at the NCNPR and that is our what I'll call a plant deconstruction laboratory so without going into great detail, but what that allows us to do is through a how to put workstation, right, is to facilitate rapid isolation and identification of phytochemicals in these different medicinal plants right, and so things that have historically taken us weeks and sometimes months, think acetylsalicylic acid from salicylic acid as a pain reliever in the willow bark or Taxol, right as an anti-cancer drug, right now we can do that with this system on the matter of days or weeks so now we're talking about activity from a plant and extract down to phytochemical characterization on a timescale, which starts to make sense in modern drug discovery, alright and so now if you look at these phytochemicals, right, and you ask yourself, well sort of who is interested in that and why, right what are traditional pharmaceutical companies, right which I've been working with for 20, over 25 years now, right, typically uses these natural products where historically has used these natural products as starting points for new drugs. Right, so in other words, take this phytochemical and make chemicals synthetic modifications in order to achieve a potential drug. But in the context of natural products, unlike the pharmaceutical realm, there is often times a big knowledge gap between a disease and a plant in other words I have a plant that has activity, but how to connect those dots has been really laborious time consuming so it took us probably 50 years to go from salicylic acid and willow bark to synthesize acetylsalicylic acid or aspirin it just doesn't work in today's environment. So casting about trying to figure out how we expedite that process that's when about four years ago, I read a really fascinating article in the Los Angeles Times about my colleague and business partner, Dr. Rangan Sukumar, describing all the interesting things that he was doing in the area of Artificial Intelligence. And one of my favorite parts of this story is basically, unannounced, I arrived at his doorstep in Oak Ridge, he was working Oak Ridge National Labs at the time, and I introduced myself to him didn't know what was coming, didn't know who I was, right and I said, hey, you don't know me you don't know why I'm here, I said, but let me tell you what I want to do with your system, right and so that kicked off a very fruitful collaboration and friendship over the last four years using Artificial Intelligence and it's culminated most recently in our COVID-19 project collaborative research between the NCNPR and HP in this case. >> From what I can understand also as Chris has mentioned highly iterative, especially with these combination mixture of chemicals right, in plants that could affect a disease. We need to put in effort to figure out what are the active components in that, that affects it yeah, the combination and given the layman's way of understanding it you know and therefore iterative and highly data intensive. And I can see why Rangan can play a huge significant role here, Rangan, thank you for joining us So it's just a nice segue to bring you in here, you know, given your work with Ryan over so many years now, tell I think I'm also quite interested in knowing a little about how it developed the first time you met and the process and the things you all work together on that culminated into the progress at the advanced level today. Please tell us a little bit about that history and also the current work. Rangan. >> So, Ryan, like he mentioned, walked into my office about four years ago and he was like hey, I'm working on this Omega-3 fatty acid, what can your system tell me about this Omega-3 fatty acid and I didn't even know how to spell Omega-3 fatty acids that's the disconnect between the technologist and the pharmacologist, they have terms of their own right since then we've come a long way I think I understand his terminologies now and he understands that I throw words like knowledge graphs and page rank and then all kinds of weird stuff that he's probably never heard in his life before right, so it's been on my mind off to different domains and terminologies in trying to accept each other's expertise in trying to work together on a collaborative project. I think the core of what Ryan's work and collaboration has led me to understanding is what happens with the drug discovery process, right so when we think about the discovery itself, we're looking at companies that are trying to accelerate the process to market, right an average drug is taking 12 years to get to market the process that Chris just mentioned, Right and so companies are trying to adopt what's called the in silico simulation techniques and in silico modeling techniques into what was predominantly an in vitro, in silico, in vivo environment, right. And so the in silico techniques could include things like molecular docking, could include Artificial Intelligence, could include other data-driven discovery methods and so forth, and the essential component of all the things that you know the discovery workflows have is the ability to augment human experts to do the best by assisting them with what computers do really really well. So, in terms of what we've done as examples is Ryan walks in and he's asking me a bunch of questions and few that come to mind immediately, the first few are, hey, you are an Artificial Intelligence expert can you sift through a database of molecules the 15,000 compounds that he described to prioritize a few for next lab experiments? So that's question number one. And he's come back into my office and asked me about hey, there's 30 million publications in PubMag and I don't have the time to read everything can you create an Artificial Intelligence system that once I've picked these few molecules will tell me everything about the molecule or everything about the virus, the unknown virus that shows up, right. Just trying to understand what are some ways in which he can augment his expertise, right. And then the third question, I think he described better than I'm going to was how can technology connect these dots. And typically it's not that the answer to a drug discovery problem sits in one database, right he probably has to think about uniproduct protein he has to think about phytochemical, chemical or informatics properties, data and so forth. Then he talked about the phytochemical interaction that's probably in another database. So when he is trying to answer other question and specifically in the context of an unknown virus that showed up in late last year, the question was, hey, do we know what happened in this particular virus compared to all the previous viruses? Do we know of any substructure that was studied or a different disease that's part of this unknown virus and can I use that information to go mine these databases to find out if these interactions can actually be used as a repurpose saying, hook, say this drug does not interact with this subsequence of a known virus that also seems to be part of this new virus, right? So to be able to connect that dot I think the abstraction that we are learning from working with pharma companies is that this drug discovery process is complex, it's iterative, and it's a sequence of needle in the haystack search problems, right and so one day, Ryan would be like, hey, I need to match genome, I need to match protein sequences between two different viruses. Another day it would be like, you know, I need to sift through a database of potential compounds, identified side effects and whatnot other day it could be, hey, I need to design a new molecule that never existed in the world before I'll figure out how to synthesize it later on, but I need a completely new molecule because of patentability reasons, right so it goes through the entire spectrum. And I think where HP has differentiated multiple times even the recent weeks is that the technology infusion into drug discovery, leads to several aha! Moments. And, aha moments typically happened in the other few seconds, and not the hours, days, months that Ryan has to laboriously work through. And what we've learned is pharma researchers love their aha moments and it leads to a sound valid, well founded hypothesis. Isn't that true Ryan? >> Absolutely. Absolutely. >> Yeah, at some point I would like to have a look at your, peak the list of your aha moments, yeah perhaps there's something quite interesting in there for other industries too, but we'll do it at another time. Chris, you know, with your regular work with pharmaceutical companies especially the big pharmas, right, do you see botanicals, coming, being talked about more and more there? >> Yeah, we do, right. Looking at kind of biosimilars and drugs that are already really in existence is kind of an important point and Dr. Yates and Rangan, with your work with databases this is something important to bring up and much of the drug discovery in today's world, isn't from going out and finding a brand new molecule per se. It's really looking at all the different databases, right all the different compounds that already exist and sifting through those, right of course data is mind, and it is gold essentially, right so a lot of companies don't want to share their data. A lot of those botanicals data sets are actually open to the public to use in many cases and people are wanting to have more collaborative efforts around those databases so that's really interesting to kind of see that being picked up more and more. >> Mm, well and Ryan that's where NCNPR hosts much of those datasets, yeah right and it's interesting to me, right you know, you were describing the traditional way of drug discovery where you have a target and a compound, right that can affect that target, very very specific. But from a botanical point of view, you really say for example, I have an extract from a plant that has combination of chemicals and somehow you know, it affects this disease but then you have to reverse engineer what those chemicals are and what the active ones are. Is that very much the issue, the work that has to be put in for botanicals in this area? >> Yes Doctor Goh, you hit it exactly. >> Now I can understand why a highly iterative intensive and data intensive, and perhaps that's why Rangan, you're highly valuable here, right. So tell us about the challenge, right the many to many intersection to try and find what the targets are, right given these botanicals that seem to affect the disease here what methods do you use, right in AI, to help with this? >> Fantastic question, I'm going to go a little bit deeper and speak like Ryan in terminology, but here we go. So with going back to about starting of our conversation right, so let's say we have a database of molecules on one side, and then we've got the database of potential targets in a particular, could be a virus, could be bacteria, could be whatever, a disease target that you've identified, right >> Oh this process so, for example, on a virus, you can have a number of targets on the virus itself some have the spike protein, some have the other proteins on the surface so there are about three different targets and others on a virus itself, yeah so a lot of people focus on the spike protein, right but there are other targets too on that virus, correct? >> That is exactly right. So for example, so the work that we did with Ryan we realized that, you know, COVID-19 protein sequence has an overlap, a significant overlap with previous SARS-CoV-1 virus, not only that, but it overlap with MERS, that's overlapped with some bad coronavirus that was studied before and so forth, right so knowing that and it's actually broken down into multiple and Ryan I'm going to steal your words, non-structural proteins, envelope proteins, S proteins, there's a whole substructure that you can associate an amino acid sequence with, right so on the one hand, you have different targets and again, since we did the work it's 160 different targets even on the COVID-19 mark, right and so you find a match, that we say around 36, 37 million molecules that are potentially synthesizable and try to figure it out which one of those or which few of those is actually going to be mapping to which one of these targets and actually have a mechanism of action that Ryan's looking for, that'll inhibit the symptoms on a human body, right so that's the challenge there. And so I think the techniques that we can unrule go back to how much do we know about the target and how much do we know about the molecule, alright. And if you start off a problem with I don't know anything about the molecule and I don't know anything about the target, you go with the traditional approaches of docking and molecular dynamics simulations and whatnot, right. But then, you've done so much docking before on the same database for different targets, you'll learn some new things about the ligands, the molecules that Ryan's talking about that can predict potential targets. So can you use that information of previous protein interactions or previous binding to known existing targets with some of the structures and so forth to build a model that will capture that essence of what we have learnt from the docking before? And so that's the second level of how do we infuse Artificial Intelligence. The third level, is to say okay, I can do this for a database of molecules, but then what if the protein-protein interactions are all over the literature study for millions of other viruses? How do I connect the dots across different mechanisms of actions too? Right and so this is where the knowledge graph component that Ryan was talking about comes in. So we've put together a database of about 150 billion medical facts from literature that Ryan is able to connect the dots and say okay, I'm starting with this molecule, what interactions do I know about the molecule? Is there a pretty intruding interaction that affects the mechanism of pathway for the symptoms that a disease is causing? And then he can go and figure out which protein and protein in the virus could potentially be working with this drug so that inhibiting certain activities would stop that progression of the disease from happening, right so like I said, your method of options, the options you've got is going to be, how much do you know about the target? How much do you know the drug database that you have and how much information can you leverage from previous research as you go down this pipeline, right so in that sense, I think we mix and match different methods and we've actually found that, you know mixing and matching different methods produces better synergies for people like Ryan. So. >> Well, the synergies I think is really important concept, Rangan, in additivities, synergistic, however you want to catch that. Right. But it goes back to your initial question Dr. Goh, which is this idea of polypharmacology and historically what we've done with traditional medicines there's more than one active, more than one network that's impacted, okay. You remember how I sort of put you on both ends of the spectrum which is the traditional sort of approach where we really don't know much about target ligand interaction to the completely interpretal side of it, right where now we are all, we're focused on is, in a single molecule interacting with a target. And so where I'm going with this is interesting enough, pharma has sort of migrate, started to migrate back toward the middle and what I mean by that, right, is we had these in a concept of polypharmacology, we had this idea, a regulatory pathway of so-called, fixed drug combinations. Okay, so now you start to see over the last 20 years pharmaceutical companies taking known, approved drugs and putting them in different combinations to impact different diseases. Okay. And so I think there's a really unique opportunity here for Artificial Intelligence or as Rangan has taught me, Augmented Intelligence, right to give you insight into how to combine those approved drugs to come up with unique indications. So is that patentability right, getting back to right how is it that it becomes commercially viable for entities like pharmaceutical companies but I think at the end of the day what's most interesting to me is sort of that, almost movement back toward that complex mixture of fixed drug combination as opposed to single drug entity, single target approach. I think that opens up some really neat avenues for us. As far as the expansion, the applicability of Artificial Intelligence is I'd like to talk to, briefly about one other aspect, right so what Rang and I have talked about is how do we take this concept of an active phytochemical and work backwards. In other words, let's say you identify a phytochemical from an in silico screening process, right, which was done for COVID-19 one of the first publications out of a group, Dr. Jeremy Smith's group at Oak Ridge National Lab, right, identified a natural product as one of the interesting actives, right and so it raises the question to our botanical guy, says, okay, where in nature do we find that phytochemical? What plants do I go after to try and source botanical drugs to achieve that particular end point right? And so, what Rangan's system allows us to do is to say, okay, let's take this phytochemical in this case, a phytochemical flavanone called eriodictyol and say, where else in nature is this found, right that's a trivial question for an Artificial Intelligence system. But for a guy like me left to my own devices without AI, I spend weeks combing the literature. >> Wow. So, this is brilliant I've learned something here today, right, If you find a chemical that actually, you know, affects and addresses a disease, right you can actually try and go the reverse way to figure out what botanicals can give you those chemicals as opposed to trying to synthesize them. >> Well, there's that and there's the other, I'm going to steal Rangan's thunder here, right he always teach me, Ryan, don't forget everything we talk about has properties, plants have properties, chemicals have properties, et cetera it's really understanding those properties and using those properties to make those connections, those edges, those sort of interfaces, right. And so, yes, we can take something like an eriodictyol right, that example I gave before and say, okay, now, based upon the properties of eriodictyol, tell me other phytochemicals, other flavonoid in this case, such as that phytochemical class of eriodictyols part right, now tell me how, what other phytochemicals match that profile, have the same properties. It might be more economically viable, right in other words, this particular phytochemical is found in a unique Himalayan plant that I've never been able to source, but can we find something similar or same thing growing in, you know a bush found all throughout the Southeast for example, like. >> Wow. So, Chris, on the pharmaceutical companies, right are they looking at this approach of getting, building drugs yeah, developing drugs? >> Yeah, absolutely Dr. Goh, really what Dr. Yates is talking about, right it doesn't help us if we find a plant and that plant lives on one mountain only on the North side in the Himalayas, we're never going to be able to create enough of a drug to manufacture and to provide to the masses, right assuming that the disease is widespread or affects a large enough portion of the population, right so understanding, you know, not only where is that botanical or that compound but understanding the chemical nature of the chemical interaction and the physics of it as well where which aspect affects the binding site, which aspect of the compound actually does the work, if you will and then being able to make that at scale, right. If you go to these pharmaceutical companies today, many of them look like breweries to be honest with you, it's large scale, it's large back everybody's clean room and it's, they're making the microbes do the work for them or they have these, you know, unique processes, right. So. >> So they're not brewing beer okay, but drugs instead. (Christopher laughs) >> Not quite, although there are pharmaceutical companies out there that have had a foray into the brewery business and vice versa, so. >> We should, we should visit one of those, yeah (chuckles) Right, so what's next, right? So you've described to us the process and how you develop your relationship with Dr. Yates Ryan over the years right, five years, was it? And culminating in today's, the many to many fast screening methods, yeah what would you think would be the next exciting things you would do other than letting me peek at your aha moments, right what would you say are the next exciting steps you're hoping to take? >> Thinking long term, again this is where Ryan and I are working on this long-term project about, we don't know enough about botanicals as much as we know about the synthetic molecules, right and so this is a story that's inspired from Simon Sinek's "Infinite Game" book, trying to figure it out if human population has to survive for a long time which we've done so far with natural products we are going to need natural products, right. So what can we do to help organizations like NCNPR to stage genomes of natural products to stage and understand the evolution as we go to understand the evolution to map the drugs and so forth. So the vision is huge, right so it's not something that we want to do on a one off project and go away but in the process, just like you are learning today, Dr. Goh I'm going to be learning quite a bit, having fun with life. So, Ryan what do you think? >> Ryan, we're learning from you. >> So my paternal grandfather lived to be 104 years of age. I've got a few years to get there, but back to "The Infinite Game" concept that Rang had mentioned he and I discussed that quite frequently, I'd like to throw out a vision for you that's well beyond that sort of time horizon that we have as humans, right and that's this right, is our current strategy and it's understandable is really treatment centric. In other words, we have a disease we develop a treatment for that disease. But we all recognize, whether you're a healthcare practitioner, whether you're a scientist, whether you're a business person, right or whatever occupation you realize that prevention, right the old ounce, prevention worth a pound of cure, right is how can we use something like Artificial Intelligence to develop preventive sorts of strategies that we are able to predict with time, right that's why we don't have preventive treatment approach right, we can't do a traditional clinical trial and say, did we prevent type two diabetes in an 18 year old? Well, we can't do that on a timescale that is reasonable, okay. And then the other part of that is why focus on botanicals? Is because, for the most part and there are exceptions I want to be very clear, I don't want to paint the picture that botanicals are all safe, you should just take botanicals dietary supplements and you'll be safe, right there are exceptions, but for the most part botanicals, natural products are in fact safe and have undergone testing, human testing for thousands of years, right. So how do we connect those dots? A preventive strategy with existing extent botanicals to really develop a healthcare system that becomes preventive centric as opposed to treatment centric. If I could wave a magic wand, that's the vision that I would figure out how we could achieve, right and I do think with guys like Rangan and Chris and folks like yourself, Eng Lim, that that's possible. Maybe it's in my lifetime I got 50 years to go to get to my grandfather's age, but you never know, right? >> You bring really, up two really good points there Ryan, it's really a systems approach, right understanding that things aren't just linear, right? And as you go through it, there's no impact to anything else, right taking that systems approach to understand every aspect of how things are being impacted. And then number two was really kind of the downstream, really we've been discussing the drug discovery process a lot and kind of the kind of preclinical in vitro studies and in vivo models, but once you get to the clinical trial there are many drugs that just fail, just fail miserably and the botanicals, right known to be safe, right, in many instances you can have a much higher success rate and that would be really interesting to see, you know, more of at least growing in the market. >> Well, these are very visionary statements from each of you, especially Dr. Yates, right, prevention better than cure, right, being proactive better than being reactive. Reactive is important, but we also need to focus on being proactive. Yes. Well, thank you very much, right this has been a brilliant panel with brilliant panelists, Dr. Ryan Yates, Dr. Rangan Sukumar and Chris Davidson. Thank you very much for joining us on this panel and highly illuminating conversation. Yeah. All for the future of drug discovery, that includes botanicals. Thank you very much. >> Thank you. >> Thank you.

>> Announcer: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a Cube Conversation. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're in our Palo Alto Studios today, having a Cube Conversation, you know, with the COVID situation going on we've had to change our business and go pretty much 100% digital. And as part of that process, we wanted to reach out to our community, and talk to some of the leaders out there, because I think leadership in troubling times is even more amplified in it's importance. So we're excited to be joined today by two leaders in our community. First one being John Chambers, a very familiar face from many, many years at Cisco, who's now the founder and CEO of JC2 Ventures. John, great to see you. >> Jeff, it's a pleasure to be with you again. >> Absolutely. And joining him is Umesh Sachdev, he's the co-founder and CEO of Uniphore. First time on theCUBE, Umesh, great to meet you. >> Jeff, thank you for having me, it's great to be with you. >> You as well, and I had one of your great people on the other day, talking about CX, and I think CX is the whole solution. Why did Uber beat cabs, do you want to stand on a corner and raise your hand in the rain? Or do you want to know when the guy's going to come pick you up, in just a couple minutes? So anyway, welcome. So let's jump into it. John, one of your things, that you talked about last time we talked, I think it was in October, wow how the world has changed. >> Yes. >> Is about having a playbook, and really, you know, kind of thinking about what you want to do before it's time to actually do it, and having some type of a script, and some type of direction, and some type of structure, as to how you respond to situations. Well there's nothing like a disaster to really fire off, you know, the need to shift gears, and go to kind of into a playbook mode. So I wonder if you could share with the viewers, kind of what is your playbook, you've been through a couple of these bumps. Not necessarily like COVID-19, but you've seen a couple bumps over your career. >> So it's my pleasure Jeff. What I'll do is kind of outline how I believe you use an innovation playbook on everything from acquisitions, to digitizing a company, to dealing with crisis. Let's focus on the playbook for crisis. You are right, and I'm not talking about my age, (John laughing) but this is my sixth financial crisis, and been through the late 1990s with the Asian financial crisis, came out of it even stronger at Cisco. Like everybody else we got knocked down in the 2001 tech bubble, came back from it even stronger. Then in 2008, 2009, Great Recession. We came through that one very, very strong, and we saw that one coming. It's my fourth major health crisis. Some of them turned out to be pretty small. I was in Mexico when the bird pandemic hit, with the President of Mexico, when we thought it was going to be terrible. We literally had to cancel the meetings that evening. That's why Cisco built the PLAR Presence. I was in Brazil for the issue with the Zika virus, that never really developed much, and the Olympics went on there, and I only saw one mosquito during the event. It bit me. But what I'm sharing with you is I've seen this movie again and again. And then, with supply chain, which not many people were talking about yet, supply chain crisis, like we saw in Japan with the Tsunami. What's happening this time is you're seeing all three at one time, and they're occurring even faster. So the playbook is pretty simple in crisis management, and then it would be fun to put Umesh on the spot and say how closely did you follow it? Did you agree with issues, or did you disagree, et cetera, on it. Now I won't mention, Umesh, that you've got a review coming up shortly from your board, so that should not affect your answer at all. But the first playbook is being realistic, how much was self-inflicted, how much was market. This one's largely market, but if you had problems before, you got to address them at the same time. The second thing is what are the five to seven things that are material, what you're going to do to lead through this crisis. That's everything from expense management, to cash preservation. It's about how do you interface to your employees, and how do you build on culture. It's about how do you interface to your customers as they change from their top priority being growth and innovation, to a top priority being cost savings, and the ability to really keep their current revenue streams from churning and moving. And it's about literally, how do make your big bets for what you want to look like as you move out of this market. Then it's how do you communicate that to your employees, to your shareholders, to your customers, to your partners. Painting the picture of what you look like as you come out. As basic as that sounds, that's what crisis management is all about. Don't hide, be visible, CEOs should take the role on implementing that playbook. Umesh to you, do you agree? And have fun with it a little bit, I like the give and take. >> I want to see the playbook, do you have it there, just below the camera? (Jeff laughing) >> I have it right here by my side. I will tell you, Jeff, in crisis times and difficult times like these, you count all the things that go right for you, you count your blessings. And one of the blessings that I have, as a CEO, is to have John Chambers as my mentor, by my side, sharing not just the learning that he had through the crisis, but talking through this, with me on a regular basis. I've read John's book more than a few times, I bet more than anybody in the world, I've read it over and over. And that, to me, is preparation going into this mode. One of the things that John has always taught me is when times get difficult, you get calmer than usual. It's one thing that when you're cruising on the freeway and you're asked to put the brakes, but it's quite another when you're in rocket ship, and accelerating, which is what my company situation was in the month of January. We were coming out of a year of 300% growth, we were driving towards another 300% growth, hiring tremendously, at a high pace. Winning customers at a high pace, and then this hit us. And so what I had to do, from a playbook perspective, is, you know, take a deep breath, and just for a couple of days, just slow down, and calmly look at the situation. My first few steps were, I reached out to 15 of our top customers, the CEOs, and give them calls, and said let's just talk about what you're seeing, and what we are observing in our business. We get a sense of where they are in their businesses. We had the benefit, my co-founder works out of Singapore, and runs our Asia business. We had the benefit of picking up the sign probably a month before everyone else did it in the U.S. I was with John in Australia, and I was telling John that "John, something unusual is happening, "a couple of our customers in these countries in Asia "are starting to tell us they would do the deal "a quarter later." And it's one thing when one of them says it, it's another when six of them say it together. And John obviously has seen this movie, he could connect the dots early. He told me to prepare, he told the rest of the portfolio companies that are in his investment group to start preparing. We then went to the playbook that John spoke of, being visible. For me, culture and communication take front seat. We have employees in ten different countries, we have offices, and very quickly, even before the governments mandated, we had all of them work, you know, go work from home, and be remote, because employee safety and health was the number one priority. We did our first virtual all-hands meeting on Zoom. We had about 240 people join in from around the world. And my job as CEO, usually our all-hands meeting were different functional leaders, different people in the group talk to the team about their initiatives. This all-hands was almost entirely run by me, addressing the whole company about what's going to be the situation from my lens, what have we learned. Be very factual. At the same time, communicating to the team that because of the fact that we raised our funding the last year, it was a good amount of money, we still have a lot of that in the bank, so we going to be very secure. At the same time, our customers are probably going to need us more than ever. Call centers are in more demand than ever, people can't walk up to a bank branch, they can't go up to a hospital without taking an appointment. So the first thing everyone is doing is trying to reach call centers. There aren't enough people, and anyways the work force that call centers have around the world, are 50% working from home, so the capacity has dropped. So our responsibility almost, is to step up, and have our AI and automation products available to as many call centers as we can. So as we are planning our own business continuity, and making sure every single employee is safe, the message to my team was we also have to be aggressive and making sure we are more out there, and more available, to our customers, that would also mean business growth for us. But first, and foremost is for us to be responsible citizens, and just make it available where it's needed. As we did that, I quickly went back to my leadership team, and again, the learning from John is usually it's more of a consensus driven approach, we go around the table, talk about a topic for a couple of hours, get the consensus, and move out of the room. My leadership meetings, they have become more frequent, we get together once a week, on video call with my executive leaders, and it's largely these days run by me. I broke down the team into five different war rooms, with different objectives. One of them we called it the preservation, we said one leader, supported by others will take the responsibility of making sure every single employee, their families, and our current customers, are addressed, taken care of. So we made somebody lead that group. Another group was made responsible for growth. Business needs to, you know, in a company that's growing at 300%, and we still have the opportunity, because call centers need us more than ever, we wanted to make sure we are responding to growth, and not just hunkering down, and, you know, ignoring the opportunity. So we had a second war room take care of the growth. And a third war room, lead by the head of finance, to look at all the financial scenarios, do the stress tests, and see if we are going to be ready for any eventuality that's going to come. Because, you know, we have a huge amount of people, who work at Uniphore around the world, and we wanted to make sure their well being is taken care of. So from being over communicative, to the team and customers, and being out there personally, to making sure we break down the teams. We have tremendous talent, and we let different people, set of people, run different set of priorities, and report back to me more frequently. And now, as we have settled into this rhythm, Jeff, you know, as we've been in, at least in the Bay area here, we've been shelter in place for about a month now. As we are in the rhythm, we are beginning to do virtual happy hours, every Thursday evening. Right after this call, I get together with my team with a glass of wine, and we get together, we talk every but work, and every employee, it's not divided by functions, or leadership, and we are getting the rhythm back into the organization. So we've gone and adjusted in the crisis, I would say very well. And the business is just humming along, as we had anticipated, going into this crisis. But I would say, if I didn't have John by my side, if I hadn't read his book, the number of times that I have, every plane ride we've done together, every place we've gone together, John has spoken about war stories. About the 2001, about 2008, and until you face the first one of your own, just like I did right now, you don't appreciate when John says leadership is lonely. But having him by our side makes it easier. >> Well I'm sure he's told you the Jack Welch story, right? That you've quoted before, John, where Jack told you that you're not really a good leader, yet, until you've been tested, right. So you go through some tough stuff, it's not that hard to lead on an upward to the right curve, it's when things get a little challenging that the real leadership shines through. >> Completely agree, and Jack said it the best, we were on our way to becoming the most valuable company in the world, he looked me in the eye and said "John, you have a very good company." And I knew he was about to give me a teaching moment, and I said "What does it take to have a great one?" He said a near death experience. And I thought I did that in '97, and some of the other management, and he said, "No, it's when you went through something "like we went through in 2001, "which many of our peers did die in." And we were knocked down really hard. When we came back from it, you get better. But what you see in Umesh is a very humble, young CEO. I have to remember he's only 34 years old, because his maturity is like he's 50, and he's seen it before. As you tell, he's like a sponge on learning, and he doesn't mind challenging. And what what he didn't say, in his humbleness, is they had the best month in March ever. And again, well over 300% versus the same quarter a year ago. So it shows you, if you're in the right spot, i.e. artificial intelligence, i.e. cost savings, i.e. customer relationship with their customers, how you can grow even during the tough times, and perhaps set a bold vision, based upon facts and a execution plan that very few companies will be able to deliver on today. So off to a great start, and you can see why I'm so honored and proud to be his strategic partner, and his coach. >> Well it's interesting, right, the human toll of this crisis is horrible, and there's a lot of people getting sick, and a lot of people are dying, and all the estimations are a lot more are going to die this month, as hopefully we get over the hump of some of these curves. So that aside, you know, we're here talking kind of more about the, kind of, the business of this thing. And it's really interesting kind of what a catalyst COVID has become, in terms of digital transformation. You know, we've been talking about new ways to work for years, and years, and years, and digital transformation, and all these kind of things. You mentioned the Cisco telepresence was out years, and decades ago. I mean I worked in Mitsubishi, we had a phone camera in 1986, I looked it up today, it was ridiculous, didn't work. But now, it's here, right. Now working from home is here. Umesh mentioned, you know, these huge call centers, now everybody's got to go home. Do they have infrastructure to go home? Do they have a place to work at home? Do they have support to go home? Teachers are now being forced, from K-12, and I know it's a hot topic for you, John, to teach from home. Teach on Zoom, with no time to prep, no time to really think it through. It's just like the kids aren't coming back, we got to learn it. You know I think this is such a transformational moment, and to your point, if this goes on for weeks, and weeks, and months, and months, which I think we all are in agreement that it will. I think you said, John, you know, many, many quarters. As people get new habits, and get into this new flow, I don't think they're going to go back back to the old ways. So I think it's a real, you know, kind of forcing function for digital transformation. And it's, you can't, you can't sit on the sidelines, cause your people can't come to the office anymore. >> So you've raised a number of questions, and I'll let Umesh handle the tough part of it. I will answer the easy part, which is I think this is the new normal. And I think it's here now, and the question is are you ready for it. And as you think about what we're really saying is the video sessions will become such an integral part of our daily lives, that we will not go back to having to do 90% of our work physically. Today alone I've done seven major group meetings, on Zoom, and Google Hangouts, and Cisco Webex. I've done six meetings with individuals, or the key CEOs of my portfolio. So that part is here to stay. Now what's going to be fascinating is does that also lead into digitization of our company, or do the companies make the mistake of saying I'm going to use this piece, because it's so obvious, and I get it, in terms of effectiveness, but I'm not going to change the other things in my normal work, in my normal business. This is why, unfortunately, I think you will see, we originally said, Jeff, you remember, 40% maybe as high as 45% of the Fortune 500 wouldn't exist in a decade. And perhaps 70% of the start-ups wouldn't exist in a decade, that are venture capital backed. I now think, unfortunately, you're going to see 20-35% of the start-ups not exist in 2 years, and I think it's going to shock you with the number of Fortune 500 companies that do not make this transition. So where you're leading this, that I completely agree with, is the ability to take this terrible event, with all of the issues, and again thank our healthcare workers for what they've been able to do to help so many people, and deal with the world the way it is. As my parents who are doctors taught me to do, not the way we wish it was. And then get your facts, prepare for the changes, and get ready for the future. The key would be how many companies do this. On the area Umesh has responsibility for, customer experience, I think you're going to see almost all companies focus on that. So it can be an example of perhaps how large companies learn to use the new technology, not just video capability, but AI, assistance for the agents, and then once they get the feel for it, just like we got the feel for these meetings, change their rhythm entirely. It was a dinner in New York, virtually, when we stopped, six weeks ago, traveling, that was supposed to be a bunch of board meetings, customer meetings, that was easy. But we were supposed to have a dinner with Shake Shack's CEO, and we were supposed to have him come out and show how he does cool innovation. We had a bunch of enterprise companies, and a bunch of media, and subject matter expertise, we ended up canceling it, and then we said why not do it virtually? And to your point, we did it in 24 different locations. Half the people, remember six weeks ago, had never even used Zoom. We had milk shakes, and hamburgers, and french fries delivered to their home. And it was one of the best two hour meetings I've seen. The future is this now. It's going to change dramatically, and Umesh, I think, is going to be at the front edge of how enterprise companies understand how their relationship with their customers is going to completely transform, using AI, conversational AI capability, speech recognition, et cetera. >> Yeah, I mean, Umesh, we haven't even really got into Uniphore, or what you guys are all about. But, you know, you're supporting call centers, you're using natural language technology, both on the inbound and all that, give us the overview, but you're playing on so many kind of innovation spaces, you know, the main interaction now with customers, and a brand, is either through the mobile phone, or through a call center, right. And that's becoming more, and increasingly, digitized. The ability to have a voice interaction, with a machine. Fascinating, and really, I think, revolutionary, and kind of taking, you know, getting us away from these stupid qwerty keyboards, which are supposed to slow us down on purpose. It's still the funniest thing ever, that we're still using these qwerty keyboards. So I wonder if you can share with us a little bit about, you know, kind of your vision of natural language, and how that changes the interaction with people, and machines. I think your TED Talk was really powerful, and I couldn't help but think of, you know, kind of mobile versus land lines, in terms of transformation. Transforming telecommunications in rural, and hard to serve areas, and then actually then adding the AI piece, to not only make it better for the front end person, but actually make it for the person servicing the account. >> Absolutely Jeff, so Uniphore, the company that I founded in 2008. We were talking about it's such a coincidence that I founded the company in 2008, the year of the Great Recession, and here we are again, talking in midst of the impact that we all have because of COVID. Uniphore does artificial intelligence and automation products, for the customer service industry. Call centers, as we know it, have fundamentally, for the last 20, 30 years, not have had a major technology disruption. We've seen a couple of ways of business model disruption, where call centers, you know, started to become offshore, in locations in Asia, India, and Mexico. Where our calls started to get routed around the world internationally, but fundamentally, the core technology in call centers, up until very recently, hadn't seen a major shift. With artificial intelligence, with natural language processings, speech recognition, available in over 100 languages. And, you know, in the last year or so, automation, and RPA, sort of adding to that mix, there's a whole new opportunity to re-think what customer service will mean to us, more in the future. As I think about the next five to seven years, with 5G happening, with 15 billion connected devices, you know, my five year old daughter, she the first thing she does when she enters the house from a playground, she goes to talk to her friend called Alexa. She speaks to Alexa. So, you know, these next generation of users, and technology users will grow up with AI, and voice, and NLP, all around us. And so their expectation of customer service and customer experience is going to be quantum times higher than some of us have, from our brands. I mean, today when a microwave or a TV doesn't work in our homes, our instinct could be to either go to the website of the brand, and try to do a chat with the agent, or do an 800 number phone call, and get them to visit the house to fix the TV. With, like I said with 5G, with TV, and microwave, and refrigerator becoming intelligent devices, you know, I could totally see my daughter telling the microwave "Why aren't you working?" And, you know, that question might still get routed to a remote contact center. Now the whole concept of contact center, the word has center in it, which means, in the past, we used to have these physical, massive locations, where people used to come in and put on their headsets to receive calls. Like John said, more than ever, we will see these centers become dispersed, and virtual. The channels with which these queries will come in would no more be just a phone, it would be the microwave, the car, the fridge. And the receivers of these calls would be anywhere in the world, sitting in their home, or sitting on a holiday in the Himalayas, and answering these situations to us. You know, I was reading, just for everyone to realize how drastic this shift has been, for the customer service industry. There are over 14 million workers, who work in contact centers around the world. Like I said, the word center means something here. All of them, right now, are working remote. This industry was never designed to work remote. Enterprises who fundamentally didn't plan for this. To your point Jeff, who thought digitization or automation, was a project they could have picked next year, or they were sitting on the fence, will now know more have a choice to make this adjustment. There's a report by a top analyst firm that said by 2023, up to 30% of customer service representatives would be remote. Well guess what, we just way blew past that number right away. And most of the CEOs that I talked to recently tell me that now that this shift has happened, about 40% of their workers will probably never return back to the office. They will always remain a permanent virtual workforce. Now when the workforce is remote, you need all the tools and technology, and AI, that A, if on any given day, 7-10% of your workforce calls in sick, you need bots, like the Amazon's Alexa, taking over a full conversation. Uniphore has a product called Akira, which does that in call centers. Most often, when these call center workers are talking, we have the experience of being put on hold, because call center workers have to type in something on their keyboard, and take notes. Well guess what, today AI and automation can assist them in doing that, making the call shorter, allowing the call center workers to take a lot more calls in the same time frame. And I don't know your experience, but, you know, a couple of weekends ago, the modem in my house wasn't working. I had a seven hour wait time to my service provider. Seven hour. I started calling at 8:30, it was somewhere around 3-4:00, finally, after call backs, wait, call back, wait, that it finally got resolved. It was just a small thing, I just couldn't get to the representative. So the enterprises are truly struggling, technology can help. They weren't designed to go remote, think about it, some of the unique challenges that I've heard now, from my customers, is that how do I know that my call center representative, who I've trained over years to be so nice, and empathetic, when they take a pee break, or a bio break, they don't get their 10 year old son to attend a call. How do I know that? Because now I can no more physically check in on them. How do I know that if I'm a bank, there's compliance? There's nothing being said that isn't being, is, you know, supposed to be said, because in a center, in an office, a supervisor can listen in. When everyone's remote, you can't do that. So AI, automation, monitoring, supporting, aiding human beings to take calls much better, and drive automation, as well as AI take over parts of a complete call, by the way of being a bot like Alexa, are sort of the things that Uniphore does, and I just feel that this is a permanent shift that we are seeing. While it's happening because of a terrible reason, the virus, that's affecting human beings, but the shift in business and behavior, is going to be permanent in this industry. >> Yeah, I think so, you know it's funny, I had Marten Mickos on, or excuse me, yeah, Marten Mickos, as part of this series. And I asked him, he's been doing distributed companies since he was doing MySQL, before Sun bought them. And he's, he was funny, it's like actually easier to fake it in an office, than when you're at home, because at home all you have to show is your deliverables. You can't look busy, you can't be going to meetings, you can't be doing things at your computer. All you have to show is your output. He said it's actually much more efficient, and it drives people, you know, to manage to the output, manage to what you want. But I want to shift gears a little bit, before we let you go, and really talk a little bit about the role of government. And John, I know you've been very involved with the Indian government, and the French government, trying to help them, in their kind of entrepreneurial pursuits, and Uniphore, I think, was founded in India, right, before you moved over here. You know we've got this huge stimulus package coming from the U.S. government, to try to help, as people, you know, can't pay their mortgage, a lot of people aren't so fortunate to be in digital businesses. It's two trillion dollars, so as kind of a thought experiment, I'm like well how much is two trillion dollars? And I did the cash balance of the FAANG companies. Facebook, Apple, Amazon, Netflix, and Alphabet, just looking at Yahoo Finance, the latest one that was there. It's 333 billion, compared to two trillion. Even when you add Microsoft's 133 billion on top, it's still shy, it's still shy of 500 billion. You know, and really, the federal government is really the only people in a position to make kind of sweeping, these types of investments. But should we be scared? Should we be worried about, you know, kind of this big shift in control? And should, do you think these companies with these big balance sheets, as you said John, priorities change a little bit. Should it be, keep that money to pay the people, so that they can stay employed and pay their mortgage, and go buy groceries, and maybe get take out from their favorite restaurant, versus, you know, kind of what we've seen in the past, where there's a lot more, you know, stock buy backs, and kind of other uses of these cash. As you said, if it's a crisis, and you got to cut to survive, you got to do that. But clearly some of these other companies are not in that position. >> So you, let me break it into two pieces, Jeff, if I may. The first is for the first time in my lifetime I have seen the federal government and federal agencies move very rapidly. And if you would have told me government could move with the speed we've seen over the last three months, I would have said probably not. The fed was ahead of both the initial interest rate cuts, and the fed was ahead in terms of the slowing down, i.e. your 2 trillion discussion, by central banks here, and around the world. But right behind it was the Treasury, which put on 4 trillion on top of that. And only governments can move in this way, but the coordination with government and businesses, and the citizens, has been remarkable. And the citizens being willing to shelter in place. To your question about India, Prime Minister Modi spent the last five years digitizing his country. And he put in place the most bandwidth of any country in the world, and literally did transformation of the currency to a virtual currency, so that people could get paid online, et cetera, within it. He then looked at start-ups and job creation, and he positioned this when an opportunity or problem came along, to be able to perhaps navigate through it in a way that other countries might struggle. I would argue President Macron in France is doing a remarkable job with his innovation economy, but also saying how do you preserve jobs. So you suddenly see government doing something that no business can do, with the scale, and the speed, and a equal approach. But at the same time, may of these companies, and being very candid, that some people might have associated with tech for good, or with tech for challenges, have been unbelievably generous in giving both from the CEOs pockets perspective, and number two and three founders perspective, as well as a company giving to the CDC, and giving to people to help create jobs. So I actually like this opportunity for tech to regain its image of being good for everybody in the world, and leadership within the world. And I think it's a unique opportunity. For my start-ups, I've been so proud, Jeff. I didn't have to tell them to go do the right thing with their employees, I didn't have to tell them that you got to treat people, human lives first, the economy second, but we can do both in parallel. And you saw companies like Sprinklr suddenly say how can I help the World Health Organization anticipate through social media, where the next spread of the virus is going to be? A company, like Bloom Energy, with what KR did there, rebuilding all of the ventilators that were broken here in California, of which about 40% were, out of the stock that they got, because it had been in storage for so long, and doing it for all of California in their manufacturing plant, at cost. A company like Aspire Foods, a cricket company down in Texas, who does 3D capabilities, taking part of their production in 3D, and saying how many thousand masks can I generate, per week, using 3D printers. You watch what Umesh has done, and how he literally is changing peoples lives, and making that experience, instead of being a negative from working at home, perhaps to a positive, and increasing the customer loyalty in the process, as opposed to when you got a seven hour wait time on a line. Not only are you probably not going to order anything else from that company, you're probably going to change it. So what is fascinating to me is I believe companies owe an obligation to be successful, to their employees, and to their shareholders, but also to give back to society. And it's one of the things I'm most proud about the portfolio companies that I'm a part of, and why I'm so proud of what Umesh is doing, in both a economically successful environment, but really giving back and making a difference. >> Yeah, I mean, there's again, there's all the doctor stuff, and the medical stuff, which I'm not qualified to really talk about. Thankfully we have good professionals that have the data, and the knowledge, and know what to do, and got out ahead of the social distancing, et cetera, but on the backside, it really looks like a big data problem in so many ways, right. And now we have massive amounts of compute at places like Amazon, and Google, and we have all types of machine learning and AI to figure out, you know, there's kind of resource allocation, whether that be hospital beds, or ventilators, or doctors, or nurses, and trying to figure out how to sort that all out. But then all of the, you know, genome work, and you know, kind of all that big heavy lifting data crunching, you know, CPU consuming work, that hopefully is accelerating the vaccine. Because I don't know how we get all the way out of this until, it just seems like kind of race to the vaccine, or massive testing, so we know that it's not going to spike up. So it seems like there is a real opportunity, it's not necessarily Kaiser building ships, or Ford building planes, but there is a role for tech to play in trying to combat this thing, and bring it under control. Umesh, I wonder if you could just kind of contrast being from India, and now being in the States for a couple years. Anything kind of jump out to you, in terms of the differences in what you're hearing back home, in the way this has been handled? >> You know, it's been very interesting, Jeff, I'm sure everyone is concerned that India, for many reasons, so far hasn't become a big hot spot yet. And, you know, we can hope and pray that that remains to be the case. There are many things that the government back home has done, I think India took lessons from what they saw in Europe, and the U.S, and China. They went into a countrywide lockdown pretty early, you know, pretty much when they were lower than a two hundred positive tested cases, the country went into lockdown. And remember this is a 1.5 billion people all together going into lockdown. What I've seen in the U.S. is that, you know, California thankfully reacted fast. We've all been sheltered in place, there's cabin fever for all of us, but you know, I'm sure at the end of the day, we're going to be thankful for the steps that are taken. Both by the administration at the state level, at the federal level, and the medical doctors, who are doing everything they can. But India, on the other hand, has taken the more aggressive stance, in terms of doing a country lockdown. We just last evening went live at a University in the city of Chennai, where Uniphore was born. The government came out with the request, much like the U.S., where they're government departments were getting a surge of traffic about information about COVID, the hospitals that are serving, what beds are available, where is the testing? We stood up a voice bot with AI, in less than a week, in three languages. Which even before the government started to advertise, we started to get thousands of calls. And this is AI answering these questions for the citizens, in doing so. So it goes back to your point of there's a real opportunity of using all the technology that the world has today, to be put to good use. And at the same time, it's really partnering meaningfully with government, in India, in Singapore, in Vietnam, and here in the U.S., to make sure that happens on, you know, John's coaching and nudging, I became a part of the U.S.-India Strategic Partnership Forum, which is truly a premier trade and commerce body between U.S. and India. And I, today, co-chaired the start-up program with, you know, the top start-ups between U.S. and India, being part of that program. And I think we got, again, tremendously fortunate, and lucky with the timeline. We started working on this start-up program between U.S. and India, and getting the start-ups together, two quarters ago, and as this new regulation with the government support, and the news about the two trillion dollar packages coming out, and the support for small businesses, we could quickly get some of the questions answered for the start-ups. Had we not created this body, which had the ability to poll the Treasury Department, and say here are questions, can start-ups do A, B, and C? What do you have by way of regulation? And I think as a response to one of our letters, on Monday the Treasury put out an FAQ on their website, which makes it super clear for start-ups and small businesses, to figure out whether they qualify or they don't qualify. So I think there's ton that both from a individual company, and the technology that each one of us have, but also as a community, how do we, all of us, meaningfully get together, as a community, and just drive benefit, both for our people, for the economy, and for our countries. Wherever we have the businesses, like I said in the U.S., or in India, or parts of Asia. >> Yeah, it's interesting. So, this is a great conversation, I could talk to you guys all night long, but I probably would hear about it later, so we'll wrap it, but I just want to kind of close on the following thought, which is really, as you've talked about before John, and as Umesh as you're now living, you know, when we go through these disruptions, things do get changed, and as you said a lot of people, and companies don't get through it. On the other hand many companies are birthed from it, right, people that are kind of on the new trend, and are in a good position to take advantage, and it's not that you're laughing over the people that didn't make it, but it does stir up the pot, and it sounds like, Umesh, you're in a really good position to take advantage of this new kind of virtual world, this new digital transformation, that's just now waiting anymore. I love your stat, they were going to move X% out of the call center over some period of time, and then it's basically snap your fingers, everybody out, without much planning. So just give you the final word, you know, kind of advice for people, as they're looking forward, and Umesh, we'll get you on another time, because I want to go deep diving in natural language, I think that's just a fascinating topic in the way that people are going to interact with machines and get rid of the stupid qwerty keyboard. But let me get kind of your last thoughts as we wrap this segment. Umesh we'll let you go first. >> Umesh, you want to go first? >> I'll go first. My last thoughts are first for the entrepreneurs, everyone who's sort of going through this together. I think in difficult times is when real heroes are born. I read a quote that when it's a sunny day, you can't overtake too many cars, but when it's raining you have a real opportunity. And the other one that I read was when fishermen can't go out fishing, because of the high tide, they come back, and mend their nets, and be ready for the time that they can go out. So I think there's no easy way to say, this is a difficult time for the economy, health wise, I hope that, you know, we can contain the damage that's being done through the virus, but some of us have the opportunity to really take our products and technology out there, more than usual. Uniphore, particularly, has a unique opportunity, the contact center industry just cannot keep up with the traffic that it's seeing. Around the world, across US, across Asia, across India, and the need for AI and automation would never be pronounced more than it is today. As much as it's a great business opportunity, it's more of a responsibility, as I see it. There can be scale up as fast as the demand is coming, and really come out of this with a much stronger business model. John has always told me in final words you always paint the picture of what you want to be, a year or two out. And I see Uniphore being a much stronger AI plus automation company, in the customer service space, really transforming the face of call centers, and customer service. Which have been forced to rethink their core business value in the last few weeks. And, you know, every fence sitter who would think that digitalization and automation was an option that they could think of in the future years, would be forced to make those decisions now. And I'm just making sure that my team, and my company, and I, am ready to gear to that great responsibility and opportunity that's ahead of us. >> John, give you the final word. >> Say Jeff, I don't know if you can still hear me, we went blank there, maybe for me to follow up. >> We gotcha. >> Shimon Peres taught me a lot about life, and dealing with life the way it is, not the way you wish it was. So did my parents, but he also taught me it always looks darkest just before the tide switches, and you move on to victory. I think the challenges in front of us are huge, I think our nation knows how to deal with that, I do believe the government has moved largely pretty effectively, to give us the impetus to move, and then if we continue to flatten the curve on the issues with the pandemic, if we get some therapeutic drugs that dramatically reduce the risk of death, for people that get the challenges the worst, and over time a vaccine, I think you look to the future, America will rebound, it will be rebounding around start-ups, new job creation, using technology in every business. So not only is there a light at the tunnel, at the end of the tunnel, I think we will emerge from this a stronger nation, a stronger start-up community. But it depends on how well we work together as a group, and I just want to say to Umesh, it's an honor to be your coach, and I learn from you as much as I give back. Jeff, as always, you do a great job. Thank you for your time today. >> Thank you both, and I look forward to our next catch up. Stay safe, wash your hands, and thanks for spending some time with us. >> And I just want to say I hope and pray that all of us can get together in Palo Alto real quick, and in person, and doing fist bumps, not shake hands or probably a namaste. Thank you, it's an honor. >> Thank you very much. All right, that was John and Umesh, you're watching theCUBE from our Palo Alto Studios, thanks for tuning in, stay safe, wash your hands, keep away from people that you're not that familiar with, and we'll see you next time. Thanks for watching. (calm music)

>> Live from Washington, DC, it's theCUBE, covering AWS Public Sector Summit. Brought to you by Amazon Web Services. >> Welcome back everyone to theCUBE's live coverage of the AWS Public Sector Summit, here in our nation's capital. I'm your host, Rebecca Knight, co-hosting with John Furrier. We have two guests for this segment, we have George Gagne, he is the Chief Information Officer at Defense POW/MIA Accounting Agency. Welcome, George. And we have Christopher McDermott, who is the CDO of the POW/MIA Accounting Agency. Welcome, Chris. >> Thank you. >> Thank you both so much for coming on the show. >> Thank you. >> So, I want to start with you George, why don't you tell our viewers a little bit about the POW/MIA Accounting Agency. >> Sure, so the mission has been around for decades actually. In 2015, Secretary of Defense, Hagel, looked at the accounting community as a whole and for efficiency gains made decision to consolidate some of the accounting community into a single organization. And they took the former JPAC, which was a direct reporting unit to PACOM out of Hawaii, which was the operational arm of the accounting community, responsible for research, investigation, recovery and identification. They took that organization, they looked at the policy portion of the organization, which is here in Crystal City, DPMO and then they took another part of the organization, our Life Sciences Support Equipment laboratory in Dayton, Ohio, and consolidated that to make the defense POW/MIA Accounting Agency, Under the Office of Secretary Defense for Policy. So that was step one. Our mission is the fullest possible accounting of missing U.S. personnel to their families and to our nation. That's our mission, we have approximately 82,000 Americans missing from our past conflicts, our service members from World War II, Korea War, Korea, Vietnam and the Cold War. When you look at the demographics of that, we have approximately 1,600 still missing from the Vietnam conflict. We have just over a 100 still missing from the Cold War conflict. We have approximately 7,700 still missing from the Korean War and the remainder of are from World War II. So, you know, one of the challenges when our organization was first formed, was we had three different organizations all had different reporting chains, they had their own cultures, disparate cultures, disparate systems, disparate processes, and step one of that was to get everybody on the same backbone and the same network. Step two to that, was to look at all those on-prem legacy systems that we had across our environment and look at the consolidation of that. And because our organization is so geographically dispersed, I just mentioned three, we also have a laboratory in Offutt, Nebraska. We have detachments in Southeast Asia, Thailand, Vietnam, Laos, and we have a detachment in Germany. And we're highly mobile. We conduct about, this year we're planned to do 84 missions around the world, 34 countries. And those missions last 30 to 45 day increments. So highly mobile, very globally diverse organization. So when we looked at that environment obviously we knew the first step after we got everybody on one network was to look to cloud architectures and models in order to be able to communicate, coordinate, and collaborate, so we developed a case management system that consist of a business intelligence software along with some enterprise content software coupled with some forensics software for our laboratory staff that make up what we call our case management system that cloud hosted. >> So business challenges, the consolidation, the reset or set-up for the mission, but then the data types, it's a different kind of data problem to work, to achieve the outcomes you're looking for. Christopher, talk about that dynamic because, >> Sure. >> You know, there are historical different types of data. >> That's right. And a lot of our data started as IBM punchcards or it started from, you know, paper files. When I started the work, we were still looking things up on microfiche and microfilm, so we've been working on an aggressive program to get all that kind of data digitized, but then we have to make it accessible. And we had, you know as George was saying, multiple different organizations doing similar work. So you had a lot of duplication of the same information, but kept in different structures, searchable in different pathways. So we have to bring all of that together and make and make it accessible, so that the government can all be on the same page. Because again, as George said, there's a large number of cases that we potentially can work on, but we have to be able to triage that down to the ones that have the best opportunity for us to use our current methods to solve. So rather than look for all 82,000 at once, we want to be able to navigate through that data and find the cases that have the most likelihood of success. >> So where do you even begin? What's the data that you're looking at? What have you seen has had the best indicators for success, of finding those people who are prisoners of war or missing in action? >> Well, you know, for some degrees as George was saying, our missions has been going on for decades. So, you know, a lot of the files that we're working from today were created at the time of the incidents. For the Vietnam cases, we have a lot of continuity. So we're still working on the leads that the strongest out of that set. And we still send multiple teams a year into Vietnam and Laos, Cambodia. And that's where, you know, you try to build upon the previous investigations, but that's also where if those investigations were done in the '70s or the '80s we have to then surface what's actionable out of that information, which pathways have we trod that didn't pay off. So a lot of it is, What can we reanalyze today? What new techniques can we bring? Can we bring in, you know, remote sensing data? Can we bring GIS applications to analyze where's the best scenario for resolving these cases after all this time? >> I mean, it's interesting one of the things we hear from the Amazon, we've done so many interviews with Amazon executives, we've kind of know their messaging. So here's one of them, "Eliminate the undifferentiated heavy lifting." You hear that a lot right. So there might be a lot of that here and then Teresa had a slide up today talking about COBOL and mainframe, talk about punch cards >> Absolutely. >> So you have a lot of data that's different types older data. So it's a true digitization project that you got to enable as well as other complexity. >> Absolutely, when the agency was formed in 2015 we really begin the process of an information modernization effort across the organization. Because like I said, these were legacy on-prem systems that were their systems' of record that had specific ways and didn't really have the ability to share the data, collaborate, coordinate, and communicate. So, it was a heavy lift across the board getting everyone on one backbone. But then going through an agency information modernization evolution, if you will, that we're still working our way through, because we're so mobilely diversified as well, our field communications capability and reach back and into the cloud and being able to access that data from geographical locations around the world, whether it's in the Himalayas, whether it's in Vietnam, whether it's in Papua New Guinea, wherever we may be. Not just our fixed locations. >> George and Christopher, if you each could comment for our audience, I would love to get this on record as you guys are really doing a great modernization project. Talk about, if you each could talk about key learnings and it could be from scar tissue. It could be from pain and suffering to an epiphany or some breakthrough. What was some of the key learnings as you when through the modernization? Could you share some from a CIO perspective and from a CDO perspective? >> Well, I'll give you a couple takeaways of what I thought I think we did well and some areas I thought that we could have done better. And for us as we looked at building our case management system, I think step one of defining our problem statement, it was years in planning before we actually took steps to actually start building out our infrastructure in the Amazon Cloud, or our applications. But building and defining that problem statement, we took some time to really take a look at that, because of the different in cultures from the disparate organizations and our processes and so on and so forth. Defining that problem statement was critical to our success and moving forward. I'd say one of the areas that I say that we could have done better is probably associated with communication and stakeholder buy-in. Because we are so geographically dispersed and highly mobile, getting the word out to everybody and all those geographically locations and all those time zones with our workforce that's out in the field a lot at 30 to 45 days at a time, three or four missions a year, sometimes more. It certainly made it difficult to get part of that get that messaging out with some of that stakeholder buy-in. And I think probably moving forward and we still deal regarding challenges is data hygiene. And that's for us, something else we did really well was we established this CDO role within our organization, because it's no longer about the systems that are used to process and store the data. It's really about the data. And who better to know the data but our data owners, not custodians and our chief data officer and our data governance council that was established. >> Christopher you're learnings, takeaways? >> What we're trying to build upon is, you define your problem statement, but the pathway there is you have to get results in front of the end users. You have get them to the people who are doing the work, so you can keep guiding it toward the solution actually meets all the needs, as well as build something that can innovate continuously over time. Because the technology space is changing so quickly and dynamically that the more we can surface our problem set, the more help we can to help find ways to navigate through that. >> So one of the things you said is that you're using data to look at the past. Whereas, so many of the guests we're talking today and so many of the people here at this summit are talking about using data to predict the future. Are you able to look your data sets from the past and then also sort of say, And then this is how we can prevent more POW. Are you using, are you thinking at all, are you looking at the future at all with you data? >> I mean, certainly especially from our laboratory science perspective, we have have probably the most advanced human identification capability in the world. >> Right. >> And recovery. And so all of those lessons really go a long ways to what what information needs to be accessible and actionable for us to be able to, recover individuals in those circumstances and make those identifications as quickly as possible. At the same time the cases that we're working on are the hardest ones. >> Right. >> The ones that are still left. But each success that we have teaches us something that can then be applied going forward. >> What is the human side of your job? Because here you are, these two wonky data number crunchers and yet, you are these are people who died fighting for their country. How do you manage those two, really two important parts of your job and how do you think about that? >> Yeah, I will say that it does amp up the emotional quotient of our agency and everybody really feels passionately about all the work that they do. About 10 times a year our agency meets with family members of the missing at different locations around the country. And those are really powerful reminders of why we're doing this. And you do get a lot of gratitude, but at the same time each case that's waiting still that's the one that matters to them. And you see that in the passion our agency brings to the data questions and quickly they want us to progress. It's never fast enough. There's always another case to pursue. So that definitely adds a lot to it, but it is very meaningful when we can help tell that story. And even for a case where we may never have the answers, being able to say, "This is what the government knows about your case and these are efforts that have been undertaken to this point." >> The fact there's an effort going on is really a wonderful thing for everybody involved. Good outcomes coming out from that. But interesting angle as a techy, IT, former IT techy back in the day in the '80s, '90s, I can't help but marvel at your perspective on your project because you're historians in a way too. You've got type punch cards, you know you got, I never used punch cards. >> Put them in a museum. >> I was the first generation post punch cards, but you have a historical view of IT state of the art at the time of the data you're working with. You have to make that data actionable in an outcome scenario workload work-stream for today. >> Yeah, another example we have is we're reclaiming chest X-rays that they did for induction when guys were which would screen for tuberculosis when they came into service. We're able to use those X-rays now for comparison with the remains that are recovered from the field. >> So you guys are really digging into history of IT. >> Yeah. >> So I'd love to get your perspective. To me, I marvel and I've always been critical of Washington's slowness with respect to cloud, but seeing you catch up now with the tailwinds here with cloud and Amazon and now Microsoft coming in with AI. You kind of see the visibility that leads to value. As you look back at the industry of federal, state, and local governments in public over the years, what's your view of the current state of union of modernization, because it seems to be a renaissance? >> Yeah, I would say the analogy I would give you it's same as that of the industrial revolutions went through in the early 20th century, but it's more about the technology revolution that we're going through now. That's how I'd probably characterize it. If I were to look back and tell my children's children about, hey, the advent of technology and that progression of where we're at. Cloud architecture certainly take down geographical barriers that before were problems for us. Now we're able to overcome those. We can't overcome the timezone barriers, but certainly the geographical barriers of separation of an organization with cloud computing has certainly changed. >> Do you see your peers within the government sector, other agencies, kind of catching wind of this going, Wow, I could really change the game. And will it be a step function into your kind of mind as you kind of have to project kind of forward where we are. Is it going to a small improvement, a step function? What do you guys see? What's the sentiment around town? >> I'm from Hawaii, so Chris probably has a better perspective of that with some of our sister organizations here in town. But, I would say there's more and more organizations that are adopting cloud architectures. It's my understanding very few organizations now are co-located in one facility and one location, right. Take a look at telework today, cost of doing business, remote accessibility regardless of where you're at. So, I'd say it's a force multiplier by far for any line of business, whether it's public sector, federal government or whatever. It's certainly enhanced our capabilities and it's a force multiplier for us. >> And I think that's where the expectation increasingly is that the data should be available and I should be able to act on it wherever I am whenever the the opportunity arises. And that's where the more we can democratize our ability to get that data out to our partners to our teams in the field, the faster those answers can come through. And the faster we can make decisions based upon the information we have, not just the process that we follow. >> And it feeds the creativity and the work product of the actors involved. Getting the data out there versus hoarding it, wall guarding it, asylumming it. >> Right, yeah. You know, becoming the lone expert on this sack of paper in the filing cabinet, doesn't have as much power as getting that data accessible to a much broader squad and everyone can contribute. >> We're doing our part. >> That's right, it's open sourcing it right here. >> To your point, death by PowerPoint. I'm sure you've heard that before. Well business intelligence software now by the click of a button reduces the level of effort for man-power and resources to put together slide decks. Where in business intelligence software can reach out to those structured data platforms and pull out the data that you want at the click of a button and build those presentations for you on the fly. Think about, I mean, if that's our force multiplier in advances in technology of. I think the biggest thing is we understand as humans how to exploit and leverage the technologies and the capabilities. Because I still don't think we fully grasp the potential of technology and how it can be leveraged to empower us. >> That's great insight and really respect what you guys do. Love your mission. Thanks for sharing. >> Yeah, thanks so much for coming on the show. >> Thank you for having us. >> I'm Rebecca Knight for John Ferrer. We will have much more coming up tomorrow on the AWS Public Sector Summit here in Washington, DC. (upbeat music)