(energetic string music) >> Hi, I'm Peter Burris and welcome to another Cube Conversation from our beautiful studios in Palo Alto. As we do with every Cube Conversation, we want to find a great topic and a smart person to talk about it, and that's what we've got today. What's the topic? We're going to be talking about new classes of AI, that are capable of addressing some of the more complex white-collar worker work that gets done. And to have that conversation, we've got Amy Guarino, who's the COO of Kyndi, here on the Cube with us today. Amy, welcome to the Cube. >> Thank you very much Peter. >> So, tell us a little bit about yourself first. >> Sure, so I grew up at IBM in sales and sales management, and then started doin' startups. Most recently, I spent eight years at Marketo, and then just after the Vista acquisition, I joined Kyndi. So that was two years ago. It was a nine person science and research kind of an organization and we've done a few things to get the group in order and we now have 31 folks and really focus on explainable AI. >> Okay, so explainable AI, what is that? >> So what's really interesting is that AI has had a lot of success, specifically around deep learning, neural nets. And one of the challenges with that approach is that it is a black box. You can't understand what the outcome was, or is. And what's really interesting, I was with a customer yesterday, and they were telling me that they were using deep learning around water treatment plants. But they got a lot of feedback that if I'm going to be drinking water, you need to explain to me what it is that you're doing to it and why. And they were like, well holy cow, we can't. And they said, that's a problem. And that's why they came to us, cause they wanted to learn about how you could do explainable type of AI. And the approach that we take really focuses on language. And how do analyze that language, but doin' it in a way where you're able to trace back to the actual raw data source to make sure that it really is correct. So we think about it as more augmenting humans versus replacing humans. >> Well let me see if I can break that down, cause I think of AI, at least things that are pertinent to AI, in a couple of different ways, kind of a mix. To what degree is something programatic, and therefore you can discover patterns in how the program operates so that you can improve it. But there's also social elements to any system that has to happen. >> Yes. >> And it's, and the black box is good for very programatic, relatively structured, where the problem space is relatively well defined, relatively well articulated and has a very specific role in a broader context of things. But when we start talking about activities that have a significant social component, where human beings are a major participant or a major source of value in the activity set that's being performed, you can't count on a black box because humans won't adopt it. So is it, when you say discoverable AI, was that it? >> Explainable AI. >> Explainable AI, is it really AI for those use cases where human beings are and essential part of the value, creation value chain? >> I think that's a great way to think about it. We initially thought it was going to be most applicable in regulated industries, where you had a requirement to explain it. But what we found is it absolutely works there, but it also is very relevant for any kind of decisions where humans are allocating resources or doing something and they have to explain why. >> So the explainable AI means that the AI can be more easily adopted by human centered activities. >> Absolutely. >> Okay, so how, so we think about AI, we think about deep learning, we think about machine learning, I mean, text automatically introduces natural language processing. What of, what elements are you combining to make the explainable AI of Kyndi work? >> So what we do is we actually ingest documents, PDF's, word documents, any kind of text, we then apply natural language processing to that to be able to parse out the entities, the terms, all of the concepts. We apply machine learning so that we can extract what we call proto-ontology, or structure, from that. So you don't have to do a lot of work upfront building out a taxonomy, and therefore we have benefit of being able to go from one domain to another very quickly and then we take all-- >> Which, by the way, blackbox AI does not do well. >> That's correct, that's absolutely correct. We addressed that deficiency as well. And then we take that output and we put it in what we call cognitive memory, which is a knowledge graph. It's a proprietary knowledge graph that allows us then to be able to search the information on there from a context perspective, so a cognitive type of search. We can also apply certain preset, sort of a filters, for different applications. So, one of the areas where we focus on is around pharmaceutical, and they're very interested in understanding and analyzing a lot of the texts associated with reports around drug discovery. And to be able to understand where there's data integrity and where's there's not-- >> And whether the process had been followed right, you got to believe. >> Yes, absolutely. And to be able to apply those preset filters against that across a really large data set and be able to highlight and get to a smaller subset that the scientists can dig into and really understand where there are potential issues and figure out how to mitigate those issues is critical. >> So let me see if I can generalize. A explainable AI being applied in a domain, like pharmaceutical-- >> Yes. >> that has a common set of audit features to it, in terms of the methods used-- >> Yes. >> for drug discovery, drug authorization, and okay. And utilizing that with the drug discovery people who are responsible for actually validating that the process is being followed appropriately to limit the amount of manual work that goes into the audit process, have I got that right? >> Yes, absolutely, by a huge factor. >> How huge? >> It's like 100 times. >> Oh, okay, well that works. >> Yes, it does work. >> So we're talking about being able to, you said 100 times, to reduce the number of people or to increase the volume of possible candidates for drug commercialization. >> Absolutely right, absolutely right. >> So what other domains do you expect Kyndi to be applied to? >> It's a very broad capability. It's any kind of work where you're reading lots of text. Today we focus in terms of the pharma opportunities. We have a lot of manufacturing folks that are looking at ways to be able to look at and review, sort of tribal knowledge that exists within a manufacturing environment. As people retire, there's a lot of information that doesn't quite get passed down and they're trying to figure out ways to get that information and also make it more easily searchable. >> Can you look at COBOL code? >> Uh, we've talked about it, we've talked about it. We do that and also in the government, we do a lot of work. >> Alright, so, you know it's interesting that you started talking about pharmaceutical. Most firms like yours work their way up to pharmaceutical. >> Yes. >> Because pharmaceutical is, you know the FDA is governed by rules where liabilities actually are associated with software. >> Yes. >> Most domains doesn't have to worry about that. So you guys are starting with the hardest problems with the greatest potential commercial risk and you're working your way into others. >> Well I think it's because it's explainable. I think that's the advantage that we have. And so we are able, then, to go back and provide that provenance to be able to support how we got there. And so it makes a big difference. >> Okay, so what's going to happen with Kyndi in 2019? >> We're going to continue to grow and really expand, particularly on the commercial side of the business, and go beyond pharmaceutical into manufacturing, maybe even a little for the financial services. But really make our customers successful, show how successful we can be. And that's going to be our marketing capability, to be able to help share this with the rest of the world. >> Yeah, if you're around COBOL, you can help my CIO guys. >> Okay. (laughter) >> There's a lot of people, like me, retiring. Alright, Amy Guarino, COO of Kyndi, talking about explainable AI and the need for new classes of tools that can augment human activity, make 'em more productive. Amy, thanks very much for being on The Cube. >> Thanks Peter, it's been great. >> Once again, I'm Peter Burris, thanks very much for watching this Cube Conversation. Until next time. (energetic string music)

(light orchestral music) >> Hi, I'm Peter Burris and welcome to another Cube Conversation from our wonderful studios here in Palo Alto, California. One of the most challenging things that any business has to navigate, especially B2B business, is that crucial relationship between sales and marketing and customer engagement. How to make customer engagement as high quality, high value, to a customer but also as productive to the business as possible. And to have that conversation, we've got Amy Guarino, who's a COO of Kyndi here on theCUBE with us today. Now Amy, welcome to theCUBE. >> Thank you very much Peter. >> So we're going to start with something that recently happened. You recently attended a Women in Sales conference, tell us a little bit about yourself and then we can talk about that conference. >> Sure, I'm the COO of Kyndi which is an explainable AI company, and that means I have responsibility for everything customer-facing. So from sales, marketing, services, support, and anything else to help make sure that we run the business in a good way. I recently came from Marketo, I had eight years there, where sales and marketing was really definitely a critical piece, and hopefully we helped change a little bit in terms of the way people think about sales and marketing. >> Well that's a small job that you have so, but nonetheless you had time to go to this Women in Sales conference, tell us a little bit about it. >> Sure yeah, so it's a group that started out in New York city, and then they've been having some events across the country, but this was the first West Coast event. So myself and there were two other women that have sales leadership roles out here and we participated and there were about 120 mostly women, I think there were three fellas there that joined. So what I couldn't figure out is why more fellas didn't come, it seems like a great place to meet a bunch of pretty interesting women. So, it really was a fun event and a lot of the questions focused on women in sales careers and how best to develop a sales career. >> Well certainly I'm sure it would have been an opportunity for some men to discover something about how women envision the role that sales plays, the role of engagement. There have been a number of studies over the years that women actually seem to demonstrate an even stronger affinity for making some of those connections necessary to traverse a very highly complex, high-value sales relationship. What were some of the highlights that you took away from the conference? >> Well I think some of things that were pretty interesting was understanding how do women look at sales differently and really what are some of the unique aspects of how women approach things, and a lot of it focused on listening skills and a woman's ability to, and it's not to mean that fellas aren't good listeners. >> What, what, what, what? I'm sorry, what? >> But I think it is something that women do have a natural affinity to be able to listen and to really pull out when someone is speaking, whether it's a prospect or a customer, what really is important to them? >> So listening is one, any others that just pop to mind? >> I think the other was in terms of sales management is really interesting is the ability for women sales managers and leaders to be able to understand what are the strengths and weaknesses of folks on the team, how to be able to coach them, and then how to pull together a team that really takes advantage of all of the different skills across the whole sales team. >> So here's one of the questions that I have about looking at women in sales as a thing. You have to on the one hand be very careful about generalizing, but on the other hand you really do want to discover what attributes of a person tend to create value for business, create value for the customers, et cetera. Was there any conversation about how far we should take some of these generalizations like I once had someone tell me, "Well men are very transactional, "women are very relationship-orientated." Which always seemed to me to be a bromide. But how far should we take the notion of women specifically in sales as we think about business management? >> Well the piece I think we talked about a lot last night was not so much in terms of the generalizations, but the fact that in today's world you want to hire the best of the best, and in order to hire the best of the best, women make up 50% of the population, you want to be able to-- >> And 80% of the best. >> (laughs) Well I appreciate that, but you want to be able to put yourself in a business culture or a sales culture where that's appreciated. I think especially in tech there's so many situations where you walk into a tech sales organization, and it's 80, 90% fellas and it makes it tough for women to want to join that kind of an organization. And as a sales leader, as a sales manager, if you want to hire the best of the best, you want to make sure that you're attracting people, the best, and so therefore you want to make sure your culture really is in a position to be able to attract the best. >> Yeah, 'cause I think one of the things that our Chief Revenue Officer has to do is it has to drive sales productivity which means taking advantage of skills and improving sales enabled them. But at the same time establishing a culture that encourages each person to shine, that doesn't diminish different types of skills. And I got to believe that's one of the things you took away. How are you applying some of the lessons that you learned to your job as a COO and responsible for customer engagement at Kyndi? >> Well I think the one thing is to really be attentive to it. Sometimes your business is growing so fast you're just like, "Oh, I'm just going to hire and get things going." And one of the things, we're not quite at that stage where we're adding tons of people yet, but we know we're going to, is making sure that we're thinking about and being very deliberate in terms of the types of folks that we're recruiting. And one of the things that I've seen most effective, particularly for fast-growing tech companies, is hiring women leadership. I think sometimes, and I don't think it's where the fellas are hiring people 'cause they want to not hire women, but its more they hire people they know. And so all of a sudden you look up and you realize, oh my goodness I've got six first-line sales managers and they're all guys. And when a woman goes in to interview for a first-line role and they look at that and say well, your whole management staff is all men, how is that going to make me feel comfortable? Is this the kind of environment where I'm going to be able to be successful? And so it's really being very deliberate and intense in terms of thinking about how can I make sure that I do have some women in leadership? And I think that can change the dynamic quite a bit in terms of the company culture. >> And are you discovering that at least from a Kyndi standpoint, I mean obviously Kyndi at very, very senior levels is you. So that says something about what constitutes being important at Kyndi. Do you anticipate that having more women is going to improve your ability to engage customers? Improve your ability for customers to take action quicker? What's the expectation? >> I think that the expectation is that you've got different types of perspectives and different types of way to look at customer acquisition and customer engagement and customer support, and we can all help each other. When you have different opinions and different ways of looking at things, as a team then you really get much more productive in terms of being able to do the right things for customers and make sure they're successful. >> So a culture that encourages, or at least liberates and takes advantage of diversity. Talk a little bit about the sales enablement side of that. Because again one of the things I mentioned earlier is that as chief revenue officer, part of your job has to be to accelerate increases in productivity of your field organization as fast as possible. How does what you heard from the conference yesterday, that mission, sales enablement, et cetera, come together, collide? >> Sure, yeah I'm not sure that it's specific to women, but it's any time you bring on a new rep, you want to be able to take that gap from when you hire them to the time their productive, and productive means being able to go out and actually sell something to a customer. You want to make that as quick as possible and as efficient as possible. So it's really understanding that path and understanding what it's going to take to help make that rep successful. Doing that in a systematic approach as opposed to, hey why don't you go out and go on a few calls with somebody and then see how it goes. Because when you actually take that and make it into a process, you can understand where people are picking things up, where they're not picking things up, what you can actually do to enhance that process and make it go faster and make it easier for new to people to come on board and be productive. 'Cause sales people want to sell, they want to get engaged with customers, they're eager to get going and really make an impact, and so the better you can enhance that process I think the better and more successful they'll feel. And then from an organizational standpoint, the quicker you can make your number, because it's all about how do I have as many quota-carrying, productive reps in the territory as quickly as possible. >> Yeah, one last thought, I think other thing is that sales people tend to learn from other sales people. Having a culture that encourages that kind of sharing and that kind of respect and that kind of diversity means that you're going to get a lot more different perspectives on what works. >> Exactly, it's all about figuring out what works and what doesn't work and then sharing that information across the group. >> Alright, fantastic, Amy Guarino, COO of Kyndi, talking about women in sales and how she is COO and to space taking some of the lessons learned and applying it to make Kyndi a more inclusive, better customer serving business. >> Terrific, thanks Peter. >> Thanks Amy, and once again this is Peter Burris, thanks again for listening to this Cube Conversation. Until next time. (light orchestral music)