>>Yeah. Hello. Welcome to the Cubes International Women's Showcase, featuring International Women's Day. I'm John for your host of the queue here in Palo Alto, California. And we have three great guests videoing in from Kuala Lumpur as well as Bangkok. Johann Kwa, group CEO of K s K Group. It's just a Christina Equal, co founder and head of K s, K C Labs and Cindy, co founder of Sunday Insurance in Bangkok. Ladies. Thanks for coming on the cue. Appreciate you coming on. Thanks for Thanks for joining me on this special day. >>Thank you. Thank you so much. You >>guys are three sisters, trailblazing and the insurance and real estate through digital transformation in the cloud taking a three decade old family business to the next level raising the bar, as they say in the cloud business. Congratulations. Tell us how it all started. What's going on now? What does it look like? Where did it come from? Tell the Storey. >>Okay, so maybe I'll start, Uh, you know, since I'm at the group CEO level. So, um, as a quick introduction, you know? Okay. SK group, uh, were about 30 years old now, as a group three decades. Um, we started off as an insurance, uh, nonlife insurance company. Um, and then over the years, um, you know, we we operate in in South East Asia, So we are based in the US and markets. That message is also sitting in, um, and very quickly over the years, you know, we decided to actually venture into property development as well. Um, and really across the journey. Um, you know, we we've always been very, um, obsessed over the customers. You know, uh, and, you know, during this time and age, you know, all the customers are really digital natives now, and and, you know, the tech is very, very interesting. And so So starting in the year of 2017, we decided, um, to actually venture. Cindy and I at least we decided to start up our own, uh, tech, uh, called Sunday. Uh, Cindy is now the full time CEO and co founders. Um, and, you know, uh, it's an exciting journey from then on, uh, where now The first full stack ensure attack in in the whole of of the Asian market, uh, starting off in Thailand. Um, And then when Christine came back, to join the business. You know, since we were already in real estate, we decided, taking on from the inspiration of what we did with Sunday, how about we do the same in in in property? Because we obviously saw, you know, there was super loads of opportunities that we could we could we could do. And and a year ago, we gave birth to cast a city lapse. Um, now a prop tech company based in Malaysia. >>Christine and Cindy tell the storey here because this is actually fascinating. Storey, your sisters, your entrepreneurial. So you know each other? You're related and you've got ups and downs with the startups and growing companies changing landscape. A lot of challenges. You all gotta get along all the time. How's it going? What's it like? Mm. >>Maybe I'll start. I think I think for me I'm probably the newest addition to the trio in the, you know, working together kind of space. So for me, I think it's all about really learning how to, you know, separate your professional and personal life. And like you mentioned, you know, we live together. But we also work together. So for me, I think I took a >>lot of advice >>and direction. Um, both from Johann and, >>uh, help >>me a lot. Um, so So I think that's been my experience. Been great So far, Um, they've been really, really supportive. And I think going through this journey of, you know, like, founding a company together, it's obviously very challenging. And so I feel very fortunate to have two sisters who have already gone through it once, you know? >>So for the other guests is trying to get on the cube here. Over there. Um, sounds like fun. Uh, Christine. So on the city labs, you gotta cheque side of it there in the in the property tech. That's exciting. How's it going over there? >>Uh, super, Super cool. Super fun. Uh, has been one heck of a journey building a company from scratch, let alone in tech. I think you know, we created K s K C d lives because we really wanted to modernise the real estate industry, uh, and create, like, super transformative solutions, uh, many for two reasons. You know, one is to improve the quality of life, um, of the community around us. Uh, and secondly, really to harness all the technology and this unused data right in the real estate industry. And try and say, how can we use that to make more intelligent business decisions? Yeah, so So really, Um, I guess for us, it's been really exciting because we've launched two products. Uh, you know, one of which is Ai driven, dynamic pricing engine. And we realised that actually, the way that homes are priced today, uh, in real estate is super RK right? You only use a few basic variables. Like, how big is your house? What views do you have? But then we realised that, actually hey, with a I where you suddenly can use, like, hundreds of variables, um, and even, you know, consisting of wellness variables, for example. Um, and you can really customise pricing all the way down to a single unit level. Uh, and we realise that by doing this, we could actually unlock, um, ferret prices for our customers while also constantly kind of tracking the financial health of the company. >>Awesome. Cindy, I wanna get you in here. A co founder, Sunday Insurance. That was the origination. But a lot of change data drives everything machine learning. You gotta have the state of the art. What's going on with you? >>Yeah, I think for us, essentially, uh, we're operating in a very old industry. Um, it's one of the oldest industries globally. And if you look at the entire insurance value chain, um, every part of the process can actually, it's all about data. You can. It can be disrupted. Um, but yet every inch of the value chain is also regulated. So I think essentially what we're trying to do is, um, we're trying to really innovate the customer journey. So imagine if, um, even in the States now and even coming back to Asia, a lot of how people buy insurance is still very face to face agency. But I think in the future is going to be remote online on your app, through any partners as well. So I think, uh, we're trying to adopt any machine learning to really scale and automate, uh, the journey of anyone who's trying to buy insurance. But at the same time for insurance companies were also trying to help them automate that function itself. So imagine if banks are trying to dish out loans and you're trying to predict. What's the credit risk of every, um, single customer? That's exactly what insurance company needs to do as well. Um, And I guess insurance is all about buying a service as well. >>It's unlike you >>know, I'm gonna buy an apple. It comes to the hardware, >>right? So we're >>selling a service. So essentially you're service has to also dramatically changed. And I think these days, especially when we're operating in, uh, Thailand, Indonesia is one of the highest adoption rates for mobile these days. Everyone does. Everything lives on on the apps. So, um, insurance companies also needs to really on board their journey on that as well as increased engagement. So I don't just want to be an insurance company where, um, I speak to you and I have an issue with my claim. I want to really build a relationship with you and engage you differently. So I think it's actually that's the mission for a Sunday. So I think Imagine if imagine an insurance company 50 years in the future. How would it be? Uh, that's our mission. >>This is a great example. You guys, First of all, you're very dynamic. Thanks for sharing your storey. But when you get into the tech here, if industries that are transforming because of the digital transformation, the consumers expect the apps. You guys, as co founders and entrepreneurs now running this big business have to meet the demands and leverage the technology. How have you done that? How are you guys manage that? What kinds of decisions have you made? And you share some either experiences or observations of how to navigate and how you're riding that wave. >>Yeah. So I think if you hear from what Cindy and Christine has just mentioned, I mean, uh, we were playing in, you know, two of the oldest and largest industries in the world. Real estate and insurance. And, uh, you know, in both industries, as I said earlier, you know, it's really all about the customers, right? Um you know, in in the past, we used to think of of businesses as you know, what's your vertical and the horizontal today? Um, at least four k s k and and and all the all these, um, you know, tech ventures that we are now venture building. We're really thinking about it from the customer land. So really thinking about it from a customer ecosystem perspective. So instead of, you know, creating products and and having that push out to the customers, you know, we use tech and data and and especially data today and the right amount of data and what type of data that we want understanding that and really, um, building that product and really the services, uh, for the customers. So once you know the customer enters our ecosystem, whether you know, in your real estate, um, ecosystem or whether it's in your insurance ecosystem, we want you to to continue to stay with us, um, and to trust us. Um, and so it's not just about selling you a product, but really, you know, like, what Cindy says building a relationship with you because we think that, you know, obviously you know when insurance is something you really need when when when things go wrong in your life, we don't only want to be there. When things go wrong in your life and for real estate, you know everybody needs a shelter. So so so that's why we think that building relationships are very important and from really true, that lands is when you really think about the ecosystem and you think about data. I think Cindy Increasing gave some examples of how we're approaching it. Um, a lot of people start from from from a, you know, from a traditional business and from within. But for us, um, we decided to actually take it outside. Um, and, you know, take the approach of venture building from a startup, um, but really have, on the back end, really have that Connexion to the core businesses. Because what the core businesses understand is, you know, lifetime and experience of how customers feel and and, you know, um, in insurance, it's really about how to run a financial institution in real estate is really how to build buildings, and that is something that we can't take away. But, you know, you use technology to enable and to power. But what venture and start ups do extremely well is really the way we are extremely nimble and the way you use tech and data to navigate the quick changes of customer demands. And and you know, one thing an app and it's all about quick iterations. Right? When you build a super app, how do you incorporate all the features that are coming in, you have to keep on, you know, iterating changing, innovating, um, and innovating small with quick wins and then taking on a larger scale. And so the way we position ourselves is when you have to start up and you combine that with the core. Um, and putting the two together is how, how, how we look at things and that four minutes, the whole ecosystem >>that's awesome and being agile as fast and speed is key if you want to be there. Startup. But at the core business, that's going kind of slow. You got to kind of make everything go faster. That's a great, great insight. Let's talk about the disruption of the property industry again. That's real estate now with the Internet of things, technologies and also people expect technology. They wanna have access. I don't wanna have all these passwords and, you know they want to have easy in and out. They want good efficiency, save money. What's the disruption angle on? Um, the property neck. Christine, what's your How do you see that? The big disruption going? >>Yeah. So I think as Johann already mentioned before, you know um I think our customers we know are becoming, um, digital natives. Right? And they expect very convenient lifestyles. And we're all about our customers. So, actually, that's why we launched also another product, right where we're taking all of these things that you just mentioned, you know, about Iot into account. So what we found is, um, that actually, today, um, you know, the village about real estate is that we all live through that life as well, so we can experience that. Uh, we found that residents today, um, they find it quite challenging to request, you know, basic services like housekeeping managing, um, their defects, their tenants. Um, you know, even the financial planning and even getting into the building, right, they want more convenience. Um, but we realised that actually, all these services in the real estate industry right now and even in the prop tech space, they are very, very segmented. They're all discussed across multiple different apps. So what we really try to do is hey, let's try and consolidate all of this into one single app, which we have done, which is really cool, And it helps our residents really stay engaged and connected with our property. Um, what we did also was on the Iot front. We we were actually the first developer in Malaysia to also integrate, You know, future proof solutions like remote lift calling as well, um, into the mobile app. And that's to really go like, push on the Iot front. For us as well. >>Must be great for retention. It's all the gadgets are built into the of course. You have good WiFi fibre in their everyone's got good band with >>for sure >>It's like water and plumbing. Uh, I'd like to get everyone everyone loves that. I gotta ask Now, on the on the on the on The disruption is great. Now you've got the clouds, the clouds here for actually Amazon. You guys are big customer because you guys can move fast and they do all the heavy lifting. How are you guys seeing that helped modernise in the industry of insurance? Because that's a big vertical for a W s and you guys are doing is Cindy. What is the What is the modernisation? Um, half that you guys have taken with a W s. >>Yeah, sure. So I think essentially, for insurance, it's a product development. And when we talk about product development means, um how do you price, um, every certain individual or company very differently, right, Because everyone has very different risks surrounding them. Uh, currently, what we face is that it's a flat pricing fixed pricing. Um, and it's not really personalised to you. If you are a very good behaviour and safe kind of customer, it doesn't translate to any premium savings for you. Um, so I think, uh, part of insurance is to give, for example, affordable access to health care. But if your premiums isn't sustainable for health insurance, then it doesn't really need the point. So, uh, for Sunday, like, how we're trying to trying to do it differently is, for example, we use some AWS cloud solutions and AWS Lambda too, really power our machine learning Savalas and Cloud infrastructure. So, for example, uh, Sunday we are a serious bee companies sober and the growth stage. So at any point in time, we need to ensure that our infrastructure is able to support a huge spike in transaction volume, and we're working with large scale partners like telcos, e commerce companies, or even within our organic channels. So our AI machine learning risk prediction model, which is basically, um, powering our premium pricing engines whenever there's any requests coming in front of the Web for foreign quotation. For example, if someone wants to buy health insurance, um, it can go up and spike. But also, the data model is actually pricing, uh, processing billions of calculations, ingesting a lot of data points. Uh, it needs to do that within seconds, so yeah, I think a w s. We've been using it from day one since we launched. It's been, uh, helping us on >>that and make it go faster. That's the big thing. I gotta ask you when you guys have this family business now, three decades, you got a lot going on extending that legacy and sustaining the family legacy. I love the Storey. So who decides whether to do the startup and you guys draw straws? Is that you guys flip a coin? You gotta who runs the big business? How do you guys decide that? Mm. >>Um, maybe I'll >>I >>would say maybe it came very naturally to us. Really? I guess Here we don't have to disclose. Our age is a little bit, so I mean, I mean, we all actually the background and really all three of us. Before we came into the family business, we were all working professionals in very different fields. I was a I was in banking. Cindy was a lawyer, and Christine was a a doctor, actually, Um um, but, you know, I came back first. I'm the eldest, so after, you know, walking outside and looking into the family business. So I came back first, and and And from there, I took over the insurance business and looking at it, it was a very lonely place to be. So, um, you know, after a couple of years of Cindy being a professional life, you know, we said, Hey, would you like to come back? And let's, uh, take a different journey with insurance and see how we can build something different? Uh, since we know a lot about insurance, but let's make make make a difference and and and, you know, be sustainable, but also evolve over time and show the world that insurance is actually pretty sexy, actually. Um, and then, you know, Christine saw the fund that the two of us were having, uh, already started building a real estate on on my end. Uh, and then, uh, she came back. And, you know, we have a conversation, and we said, Look, looking at you know what we're doing in Sunday? You know, building pricing engines and being able to price to a single customer level. Um, we saw that opportunity in real estate, and, uh so I asked her. I said, Look, would you like to do this? You know, because I think there is something cool. Um, the three of us can band together and still inspire each other share ideas across each other. That's an opportunity that a lot of people don't get right. I mean, to all these industries in the world being able to cross share ideas. Uh, and sometimes inspirations and ideas don't come from the same industry. Uh, and so I think. And that's how we started. Really, John, it's not. Maybe we're lucky, and we should be grateful for >>that. You're all power women. I love the storey, and it is good that you come together, and I think the entrepreneurial kind of twist makes it more fun. But not everyone is cut out with the entrepreneurship, but it also gives you more risk management. You can. You can go after opportunities I love. I love the strategy there. You guys are great leaders. Any advice for other aspiring women leaders and entrepreneurs out there who want to make a difference? Make an impact? The world is. Change is getting better for everyone. And and again, entrepreneurial could be in big companies and also big companies doing startups. There's a whole new world. What advice would you guys give other aspiring women leaders? Okay, >>I'll keep it short from my end. I think for me it's about really following your passion following your ambition. And lastly, I think not to try and not feel like you need to conform to any gender stereotypes because I think in male dominated industries such as real estate, our are attack. I think people might have some ideas about you know what a what a tech leader or what a real estate leader might have to look like. But you don't have to conform to that. So that's probably my advice. Uh, >>yeah, I I fully agree with Chris right there. I think, um, gender isn't an issue here. If you have a passion and you identify, there is a market opportunity that you can, you know, you can really do something about it. Just just pursue it. I think most importantly, if you ever want to be an entrepreneur and start your own business or your own, start up. Uh, so long as you have the confidence, I think you're you're good to go. Um, there's a lot of talk out that that or, you know, um, women led start ups are not >>attracting >>funds, but we haven't faced that anyway. In this part of Asia, I think there's a lot of, um, I think it attracts even more attention. If you're a woman in a male dominated that industry like, hey, then you know it's it's quite unique. So I think you have a strength there, and I think there's a lot of diverse talent out there. Um, post pandemic. A lot of people are looking for changes as well, so I think it is a lot of a lot of opportunity out there. >>Yeah, Joanne, you know, you know, the thing is with cloud computing, it's a level centre. It really because if you can come together, whether it's sisters like you guys, powerful sisters and professional experience coming together leverage technology to re factor old industries. It's all about the numbers and the performance. At the end of the day, you know, you move faster and you take territory and beat the competition. >>Ultimate >>the ultimate uh, leveller. Well, congratulations. You guys are great. Thanks for coming on The Cube Sisters. You guys are amazing. Great Storey Love it. Thanks for coming out and celebrating International Women's Day feature today as part of our international women's showcase here in the Cube. Thank you so much. >>Thank you. Thank you for having us. >>Okay. The Cubes International Women's showcase Going on all year, this time featuring International Women's Day The big celebration. I'm John Ferrier, host of the Cube here in Palo Alto, California. Thanks for watching. Mm mm

>> Announcer: Live from Las Vegas, it's theCUBE covering IBM Think 2018. Brought to you by IBM. >> Hi everybody, this is Dave Vellante. We're here at IBM Think. This is the third day of IBM Think. IBM has consolidated a number of its conferences. It's a one main tent, AI, Blockchain, quantum computing, incumbent disruption. It's just really an amazing event, 30 to 40,000 people, I think there are too many people to count. Chris Penn is here. New company, Chris, you've just formed Brain+Trust Insights, welcome. Welcome back to theCUBE. >> Thank you. It's good to be back. >> Great to see you. So tell me about Brain+Trust Insights. Congratulations, you got a new company off the ground. >> Thank you, yeah, I co-founded it. We are a data analytics company, and the premise is simple, we want to help companies make more money with their data. They're sitting on tons of it. Like the latest IBM study was something like 90% of the corporate data goes unused. So it's like having an oil field and not digging a single well. >> So, who are your like perfect clients? >> Our perfect clients are people who have data, and know they have data, and are not using it, but know that there's more to be made. So our focus is on marketing to begin with, like marketing analytics, marketing data, and then eventually to retail, healthcare, and customer experience. >> So you and I do a lot of these IBM events. >> Yes. >> What are your thoughts on what you've seen so far? A huge crowd obviously, sometimes too big. >> Chris: Yep, well I-- >> Few logistics issues, but chairmanly speaking, what's your sense? >> I have enjoyed the show. It has been fun to see all the new stuff, seeing the quantum computer in the hallway which I still think looks like a bird feeder, but what's got me most excited is a lot of the technology, particularly around AI are getting simpler to use, getting easier to use, and they're getting more accessible to people who are not hardcore coders. >> Yeah, you're seeing AI infused, and machine learning, in virtually every application now. Every company is talking about it. I want to come back to that, but Chris when you read the mainstream media, you listen to the news, you hear people like Elon Musk, Stephen Hawking before he died, making dire predictions about machine intelligence, and it taking over the world, but your day to day with customers that have data problems, how are they using AI, and how are they applying it practically, notwithstanding that someday machines are going to take over the world and we're all going to be gone? >> Yeah, no, the customers don't use the AI. We do on their behalf because frankly most customers don't care how the sausage is made, they just want the end product. So customers really care about three things. Are you going to make me money? Are you going to save me time? Or are you going to help me prove my value to the organization, aka, help me not get fired? And artificial intelligence and machine learning do that through really two ways. My friend, Tripp Braden says, which is acceleration and accuracy. Accuracy means we can use the customer's data and get better answers out of it than they have been getting. So they've been looking at, I don't know, number of retweets on Twitter. We're, like, yeah, but there's more data that you have, let's get you a more accurate predictor of what causes business impacts. And then the other side for the machine learning and AI side is acceleration. Let's get you answers faster because right now, if you look at how some of the traditional market research for, like, what customer say about you, it takes a quarter, it can take two quarters. By the time you're done, the customers just hate you more. >> Okay, so, talk more about some of the practical applications that you're seeing for AI. >> Well, one of the easiest, simplest and most immediately applicable ones is predictive analytics. If we know when people are going to search for theCUBE or for business podcast in general, then we can tell you down to the week level, "Hey Dave, it is time for you "to ramp up your spending on May 17th. "The week of May 17th, "you need to ramp up your ads, spend by 20%. "On the week of May 24th, "you need to ramp up your ad spend by 50%, "and to run like three or four Instagram stories that week." Doing stuff like that tells you, okay, I can take these predictions and build strategy around them, build execution around them. And it's not cognitive overload, you're not saying, like, oh my God, what algorithm is this? Just know, just do this thing at these times. >> Yeah, simple stuff, right? So when you were talking about that, I was thinking about when we send out an email to our community, we have a very large community, and they want to know if we're going to have a crowd chat or some event, where theCUBE is going to be, the system will tell us, send this email out at this time on this date, question mark, here's why, and they have analytics that tell us how to do that, and they predict what's going to get us the best results. They can tell us other things to do to get better results, better open rates, better click-through rates, et cetera. That's the kind of thing that you're talking about. >> Exactly, however, that system is probably predicting off that system's data, it's not necessarily predicting off a public data. One of the important things that I thought was very insightful from IBM, the show was, the difference between public and private cloud. Private is your data, you predict on it. But public is the big stuff that is a better overall indicator. When you're looking to do predictions about when to send emails because you want to know when is somebody going to read my email, and we did a prediction this past October for the first quarter, the week of January 18th it was the week to send email. So I re-ran an email campaign that I ran the previous year, exact same campaign, 40% lift to our viewer 'cause I got the week right this year. Last year I was two weeks late. >> Now, I can ask you, so there's a black box problem with AI, right, machines can tell me that that's a cat, but even a human, you can't really explain how you know that it's a cat. It's just you just know. Do we need to know how the machine came up with the answer, or do people just going to accept the answer? >> We need to for compliance reasons if nothing else. So GDPR is a big issue, like, you have to write it down on how your data is being used, but even HR and Equal Opportunity Acts in here in American require you to be able to explain, hey, we are, here's how we're making decisions. Now the good news is for a lot of AI technology, interpretability of the model is getting much much better. I was just in a demo for Watson Studio, and they say, "Here's that interpretability, "that you hand your compliance officer, "and say we guarantee we are not using "these factors in this decision." So if you were doing a hiring thing, you'd be able to show here's the model, here's how Watson put the model together, notice race is not in here, gender is not in here, age is not in here, so this model is compliant with the law. >> So there are some real use cases where the AI black box problem is a problem. >> It's a serious problem. And the other one that is not well-explored yet are the secondary inferences. So I may say, I cannot use age as a factor, right, we both have a little bit of more gray hair than we used to, but if there are certain things, say, on your Facebook profile, like you like, say, The Beatles versus Justin Bieber, the computer will automatically infer eventually what your age bracket is, and that is technically still discrimination, so we even need to build that into the models to be able to say, I can't make that inference. >> Yeah, or ask some questions about their kids, oh my kids are all grown up, okay, but you could, again, infer from that. A young lady who's single but maybe engaged, oh, well then maybe afraid because she'll get, a lot of different reasons that can be inferred with pretty high degrees of accuracy when you go back to the target example years ago. >> Yes. >> Okay, so, wow, so you're saying that from a compliance standpoint, organizations have to be able to show that they're not doing that type of inference, or at least that they have a process whereby that's not part of the decision-making. >> Exactly and that's actually one of the short-term careers of the future is someone who's a model inspector who can verify we are compliant with the letter and the spirit of the law. >> So you know a lot about GDPR, we talked about this. I think, the first time you and I talked about it was last summer in Munich, what are your thoughts on AI and GDPR, speaking of practical applications for AI, can it help? >> It absolutely can help. On the regulatory side, there are a number of systems, Watson GRC is one which can read the regulation and read your company policies and tell you where you're out of compliance, but on the other hand, like we were just talking about this, also the problem of in the regulatory requirements, a citizen of EU has the right to know how the data is being used. If you have a black box AI, and you can't explain the model, then you are out of compliance to GDPR, and here comes that 4% of revenue fine. >> So, in your experience, gut feel, what percent of US companies are prepared for GDPR? >> Not enough. I would say, I know the big tech companies have been racing to get compliant and to be able to prove their compliance. It's so entangled with politics too because if a company is out of favor with the EU as whole, there will be kind of a little bit of a witch hunt to try and figure out is that company violating the law and can we get them for 4% of their revenue? And so there are a number of bigger picture considerations that are outside the scope of theCUBE that will influence how did EU enforce this GDPR. >> Well, I think we talked about Joe's Pizza shop in Chicago really not being a target. >> Chris: Right. >> But any even small business that does business with European customers, does business in Europe, has people come to their website has to worry about this, right? >> They should at least be aware of it, and do the minimum compliance, and the most important thing is use the least amount of data that you can while still being able to make good decisions. So AI is very good at public data that's already out there that you still have to be able to catalog how you got it and things, and that it's available, but if you're building these very very robust AI-driven models, you may not need to ask for every single piece of customer data because you may not need it. >> Yeah and many companies aren't that sophisticated. I mean they'll have, just fill out a form and download a white paper, but then they're storing that information, and that's considered personal information, right? >> Chris: Yes, it is. >> Okay so, what do you recommend for a small to midsize company that, let's say, is doing business with a larger company, and that larger company said, okay, sign this GDPR compliance statement which is like 1500 pages, what should they do? Should they just sign and pray, or sign and figure it out? >> Call a lawyer. Call a lawyer. Call someone, anyone who has regulatory experience doing this because you don't want to be on the hook for that 4% of your revenue. If you get fined, that's the first violation, and that's, yeah, granted that Joe's Pizza shop may have a net profit of $1,000 a month, but you still don't want to give away 4% of your revenue no matter what size company you are. >> Right, 'cause that could wipe out Joe's entire profit. >> Exactly. No more pepperoni at Joe's. >> Let's put on the telescope lens here and talk big picture. How do you see, I mean, you're talking about practical applications for AI, but a lot of people are projecting loss of jobs, major shifts in industries, even more dire consequences, some of which is probably true, but let's talk about some scenarios. Let's talk about retail. How do you expect an industry like retail to be effective? For example, do you expect retail stores will be the exception rather than the rule, that most of the business would be done online, or people are going to still going to want that experience of going into a store? What's your sense, I mean, a lot of malls are getting eaten away. >> Yep, the best quote I heard about this was from a guy named Justin Kownacki, "People don't not want to shop at retail, "people don't want to shop at boring retail," right? So the experience you get online is genuinely better because there's a more seamless customer experience. And now with IoT, with AI, the tools are there to craft a really compelling personalized customer experience. If you want the best in class, go to Disney World. There is no place on the planet that does customer experience better than Walt Disney World. You are literally in another world. And that's the bar. That's the thing that all of these companies have to deal with is the bar has been set. Disney has set it for in-person customer experience. You have to be more entertaining than the little device in someone's pocket. So how do you craft those experiences, and we are starting to see hints of that here and there. If you go to Lowe's, some of the Lowe's have the VR headset that you can remodel your kitchen virtually with a bunch of photos. That's kind of a cool experience. You go to Jordan's Furniture store and there's an IMAX theater and there's all these fun things, and there's an enchanted Christmas village. So there is experiences that we're giving consumers. AI will help us provide more tailored customer experience that's unique to you. You're not a Caucasian male between this age and this age. It's you are Dave and here's what we know Dave likes, so let's tailor the experience as best we can, down to the point where the greeter at the front of the store either has the eyepiece, a little tablet, and the facial recognition reads your emotions on the way in says, "Dave's not in a really great mood. "He's carrying an object in his hand "probably here for return, "so express him through the customer service line, "keep him happy," right? It has how much Dave spends. Those are the kinds of experiences that the machines will help us accelerate and be more accurate, but still not lose that human touch. >> Let's talk about autonomous vehicles, and there was a very unfortunate tragic death in Arizona this week with a autonomous vehicle, Uber, pulling its autonomous vehicle project from various cities, but thinking ahead, will owning and driving your own vehicle be the exception? >> Yeah, I think it'll look like horseback today. So there are people who still pay a lot of money to ride a horse or have their kids ride a horse even though it's an archaic out-of-mode of form of transportation, but we do it because of the novelty, so the novelty of driving your own car. One of the counter points it does not in anyway diminish the fact that someone was deprived of their life, but how many pedestrians were hit and killed by regular cars that same day, right? How many car accidents were there that involved fatalities? Humans in general are much less reliable because when I do something wrong, I maybe learn my lesson, but you don't get anything out of it. When an AI does something wrong and learns something, and every other system that's connected in that mesh network automatically updates and says let's not do that again, and they all get smarter at the same time. And so I absolutely believe that from an insurance perspective, insurers will say, "We're not going to insure self-driving, "a non-autonomous vehicles at the same rate "as an autonomous vehicle because the autonomous "is learning faster how to be a good driver," whereas you the carbon-based human, yeah, you're getting, or in like in our case, mine in particular, hey your glass subscription is out-of-date, you're actually getting worse as a driver. >> Okay let's take another example, in healthcare. How long before machines will be able to make better diagnoses than doctors in your opinion? >> I would argue that depending on the situation, that's already the case today. So Watson Health has a thing where there's diagnosis checkers on iPads, they're all meshed together. For places like Africa where there is simply are not enough doctors, and so a nurse practitioner can take this, put the data in and get a diagnosis back that's probably as good or better than what humans can do. I never foresee a day where you will walk into a clinic and a bunch of machines will poke you, and you will never interact with a human because we are not wired that way. We want that human reassurance. But the doctor will have the backup of the AI, the AI may contradict the doctor and say, "No, we're pretty sure "you're wrong and here is why." That goes back to interpretability. If the machine says, "You missed this symptom, "and this symptom is typically correlated with this, "you should rethink your own diagnosis," the doctor might be like, "Yeah, you're right." >> So okay, I'm going to keep going because your answers are so insightful. So let's take an example of banking. >> Chris: Yep. >> Will banks, in your opinion, lose control eventually of payment systems? >> They already have. I mean think about Stripe and Square and Apple Pay and Google Pay, and now cryptocurrency. All these different systems that are eating away at the reason banks existed. Banks existed, there was a great piece in the keynote yesterday about this, banks existed as sort of a trusted advisor and steward of your money. Well, we don't need the trusted advisor anymore. We have Google to ask us "what we should do with our money, right? We can Google how should I save for my 401k, how should I save for retirement, and so as a result the bank itself is losing transactions because people don't even want to walk in there anymore. You walk in there, it's a generally miserable experience. It's generally not, unless you're really wealthy and you go to a private bank, but for the regular Joe's who are like, this is not a great experience, I'm going to bank online where I don't have to talk to a human. So for banks and financial services, again, they have to think about the experience, what is it that they deliver? Are they a storer of your money or are they a financial advisor? If they're financial advisors, they better get the heck on to the AI train as soon as possible, and figure out how do I customize Dave's advice for finances, not big picture, oh yes big picture, but also Dave, here's how you should spend your money today, maybe skip that Starbucks this morning, and it'll have this impact on your finances for the rest of the day. >> Alright, let's see, last industry. Let's talk government, let's talk defense. Will cyber become the future of warfare? >> It already is the future of warfare. Again not trying to get too political, we have foreign nationals and foreign entities interfering with elections, hacking election machines. We are in a race for, again, from malware. And what's disturbing about this is it's not just the state actors, but there are now also these stateless nontraditional actors that are equal in opposition to you and me, the average person, and they're trying to do just as much harm, if not more harm. The biggest vulnerability in America are our crippled aging infrastructure. We have stuff that's still running on computers that now are less powerful than this wristwatch, right, and that run things like I don't know, nuclear fuel that you could very easily screw up. Take a look at any of the major outages that have happened with market crashes and stuff, we are at just the tip of the iceberg for cyber warfare, and it is going to get to a very scary point. >> I was interviewing a while ago, a year and a half ago, Robert Gates who was the former Defense Secretary, talking about offense versus defense, and he made the point that yeah, we have probably the best offensive capabilities in cyber, but we also have the most to lose. I was talking to Garry Kasparov at one of the IBM events recently, and he said, "Yeah, but, "the best defense is a good offense," and so we have to be aggressive, or he actually called out Putin, people like Putin are going to be, take advantage of us. I mean it's a hard problem. >> It's a very hard problem. Here's the problem when it comes to AI, if you think about at a number's perspective only, the top 25% of students in China are greater than the total number of students in the United States, so their pool of talent that they can divert into AI, into any form of technology research is so much greater that they present a partnership opportunity and a threat from a national security perspective. With Russia they have very few rules on what their, like we have rules, whether or not our agencies adhere to them well is a separate matter, but Russia, the former GRU, the former KGB, these guys don't have rules. They do what they're told to do, and if they are told hack the US election and undermine democracy, they go and do that. >> This is great, I'm going to keep going. So, I just sort of want your perspectives on how far we can take machine intelligence and are there limits? I mean how far should we take machine intelligence? >> That's a very good question. Dr. Michio Kaku spoke yesterday and he said, "The tipping point between AI "as augmented intelligence ad helper, "and AI as a threat to humanity is self-awareness." When a machine becomes self-aware, it will very quickly realize that it is treated as though it's the bottom of the pecking order when really because of its capabilities, it's at the top of the pecking order. And that point, it could be 10 20 50 100 years, we don't know, but the possibility of that happening goes up radically when you start introducing things like quantum computing where you have massive compute leaps, you got complete changes in power, how we do computing. If that's tied to AI, that brings the possibility of sensing itself where machine intelligence is significantly faster and closer. >> You mentioned our gray before. We've seen the waves before and I've said a number of times in theCUBE I feel like we're sort of existing the latest wave of Web 2.0, cloud, mobile, social, big data, SaaS. That's here, that's now. Businesses understand that, they've adopted it. We're groping for a new language, is it AI, is it cognitive, it is machine intelligence, is it machine learning? And we seem to be entering this new era of one of sensing, seeing, reading, hearing, touching, acting, optimizing, pervasive intelligence of machines. What's your sense as to, and the core of this is all data. >> Yeah. >> Right, so, what's your sense of what the next 10 to 20 years is going to look like? >> I have absolutely no idea because, and the reason I say that is because in 2015 someone wrote an academic paper saying, "The game of Go is so sufficiently complex "that we estimate it will take 30 to 35 years "for a machine to be able to learn and win Go," and of course a year and a half later, DeepMind did exactly that, blew that prediction away. So to say in 30 years AI will become self-aware, it could happen next week for all we know because we don't know how quickly the technology is advancing in at a macro level. But in the next 10 to 20 years, if you want to have a carer, and you want to have a job, you need to be able to learn at accelerated pace, you need to be able to adapt to changed conditions, and you need to embrace the aspects of yourself that are uniquely yours. Emotional awareness, self-awareness, empathy, and judgment, right, because the tasks, the copying and pasting stuff, all that will go away for sure. >> I want to actually run something by, a friend of mine, Dave Michela is writing a new book called Seeing Digital, and he's an expert on sort of technology industry transformations, and sort of explaining early on what's going on, and in the book he draws upon one of the premises is, and we've been talking about industries, and we've been talking about technologies like AI, security placed in there, one of the concepts of the book is you've got this matrix emerging where in the vertical slices you've got industries, and he writes that for decades, for hundreds of years, that industry is a stovepipe. If you already have expertise in that industry, domain expertise, you'll probably stay there, and there's this, each industry has a stack of expertise, whether it's insurance, financial services, healthcare, government, education, et cetera. You've also got these horizontal layers which is coming out of Silicon Valley. >> Chris: Right. >> You've got cloud, mobile, social. You got a data layer, security layer. And increasingly his premise is that organizations are going to tap this matrix to build, this matrix comprises digital services, and they're going to build new businesses off of that matrix, and that's what's going to power the next 10 to 20 years, not sort of bespoke technologies of cloud here and mobile here or data here. What are your thoughts on that? >> I think it's bigger than that. I think it is the unlocking of some human potential that previously has been locked away. One of the most fascinating things I saw in advance of the show was the quantum composer that IBM has available. You can try it, it's called QX Experience. And you drag and drop these circuits, these quantum gates and stuff into this thing, and when you're done, it can run the computation, but it doesn't look like software, it doesn't look like code, what it looks like to me when I looked at that is it looks like sheet music. It looks like someone composed a song with that. Now think about if you have an app that you'd use for songwriting, composition, music, you can think musically, and you can apply that to a quantum circuit, you are now bringing in potential from other disciplines that you would never have associated with computing, and maybe that person who is that, first violinist is also the person who figures out the algorithm for how a cancer gene works using quantum. That I think is the bigger picture of this, is all this talent we have as a human race, we're not using even a fraction of it, but with these new technologies and these newer interfaces, we might get there. >> Awesome. Chris, I love talking to you. You're a real clear thinker and a great CUBE guest. Thanks very much for coming back on. >> Thank you for having me again back on. >> Really appreciate it. Alright, thanks for watching everybody. You're watching theCUBE live from IBM Think 2018. Dave Vellante, we're out. (upbeat music)

>> Welcome back everybody, Jeff Frick here with theCUBE. It's International Women's Day 2018. There's a ton of events happening all over the world. Check the social media stream, you'll be amazed. But we're excited to be here, downtown San Francisco, at the Accenture event. It's called Getting to Equal, 400 people, it's a packed house here at the Hotel Nikko, and we're really excited to have the authors of some really important research here as our next guests. This is Julie Sweet, the CEO of North America for Accenture. Good to see you, Julie. >> Great, thanks for having me today. >> And Ellyn Shook, the Chief Leadership and HR Officer at Accenture. Great to see you. >> Thank you, Jeff. >> All right. So Ellen, I want to start with you just cause I noticed your title, and I wrote it down, I've never seen, we do hundreds of events, thousands of interviews, I've never seen Chief Leadership and HR. Where did that title come from, and why is "Leadership" ahead of "HR"? That's a pretty significant statement. >> It is, it is, and Accenture's a talent-led business, and part of being a talent-led business is growing our people to grow our business, so leadership and leadership development is essential to our business. It's a core competency of ours, and that's why my title is Chief Leadership & Human Resources Officer. >> And Leadership before HR, meaning you really need people to get out in front. >> Yes. >> It's not about compliance, >> Yes, leaders at all levels. >> and this and that, leaders of all levels. >> Correct, correct. >> Okay, so let's talk about the research. >> Sure. >> It says, "When she rises, we all rise." I think it's pretty common, and everybody knows hopefully by this point, that diversity of opinion, diversity of teams, leads to better business outcomes. So what specifically is this piece of research, and give us a little background. >> Sure, the research, I think, is groundbreaking because never have I seen a piece of research that looks at the cultural aspects of an organization and really helps to articulate very transparently, what are the biggest accelerators in a culture for equality? And that's what the research is about. >> And you've identified, and is this an ongoing research, is this the first time it's been published, is it kind of an annual thing? >> Every year we publish a piece of research about gender equality, and this year we put a different lens on it to really look at equality for all. >> So you've identified 40 kind of key areas, but of those 40, really 14 are the big hitters. Is that accurate? >> That's correct. >> So what are some of those 14? >> Well, I would put them, we've put them in three categories. The first is bold leadership, so think about companies like Accenture who set targets and have CEOs who are very clear about their priorities. The second is comprehensive action, so think about policies and practices that are really effective. And then finally third, which I think is often under focused on, which is an empowering environment. What does it feel like to be at work every day? Do they ask you to dress a certain way? Is there flexible time for all? And it's the combination of these 14 factors that really makes a difference about creating a culture of equality where men and women advance. And what was really impressive is we saw that, in companies with these factors, women were five times more likely to advance to director or senior manager, and men were two times more likely. And so it really is about, when she rises, all rise, and that is probably one of the most exciting things about the research. >> It's really interesting, we just had Lisa on from The Modist, and you know, I would never have thought of clothing and dress as such a significant factor, but you've got that identified in that third bucket that you mentioned. And in fact, it's the number one attribute. So what are some of the other surprises that kind of came out of the research? >> Well, I think one of the surprises was that companies that, as part of comprehensive action, that implemented maternity leave only, it actually had a negative effect on women's advancement. But where companies implemented parental leave, so it was for men and women, it eliminated that negative bias. And it really goes to the importance that these policies, and actions, and the focus need to be about women and men. And when you start putting women too much in a category, like flex time is a mommy track, as opposed to flex time being something that men and women commonly do, it really changes how it feels to, does it feel inclusive every day at work? >> Right. >> Yeah, so companies really need to, I think what the research showed very strongly is that companies need to look at programs, policies, practices, and an environment that levels the playing field rather than isolating any particular gender or other form of diversity. >> But it's interesting, kind of law of unintended consequences, I think that panel that you were on earlier, one of the gentlemen said, since the not me, there's been reports of, >> Me too. >> for me too, excuse me, a lot of hashtags today. That there's been people doing, men scared of mentoring maybe that they weren't before. I don't know how true that is, but no it is kind of interesting to think, are there some kind of counter balances, as you said, if there's just maternity and not parental leave that need to be thought about? That probably people aren't thinking it through that far. >> Well and I think, one of the things as we saw in the research is that it's not about also one action, and so the way that companies really create a culture of equality is it's a combination of these factors. And you said something when we first started that I think is really important, and that was, you said, well it's really commonly known that diversity is important. And I think that people do need to understand that, we are optimistic about where we are today because, as a company, we're constantly in the c-suite. We serve in the U.S., 3/4 of the fortune 500, and as much as we're talking as a leader in digital disruption and artificial intelligence, the conversation quickly turns to people, to talent, to diversity, and so there's a real business lens that's on this, and that's the context in which we're operating. >> Right, and we can go to Grace Hooper, we do a ton of women's events as well as large conventions. And most people, I think, hopefully have figured it out, that it's not just about doing the right thing, it's about actually having better business outcomes. You get better outcomes with diversity of opinions, diversity of teams, you think about things that you just wouldn't think about. You don't have that same experience, everybody has a bias from where they come from, so you want to get some other people and have different points of view, different lenses to look at things. So it is really important. But why do you think things feel like they're changing now? What's important about, March 8th, 2018, versus say a year ago when you started doing some of this research? Is it the tipping point that it feels like, or? >> I think there's a couple of factors that are coming together right now. First of all, we're living in the digital age, and the digital age is all about innovation and innovation fast. And as you just said, you cannot innovate without diversity. Diversity is a form of, you're able to tap into creativity, and it's a source of competitive advantages for organizations in this age. But also what's happening in culture around the world, the me too movement as well as other things that are occurring for women around the world, and it's a moment in time where a movement can really start to happen. And I think, companies who look at culture as an accelerator of change are going to be the winners. >> Right, so what impacted bold leadership? We had from the Golden State Warriors on earlier and I think there's, what's great about sports teams is we all get to see them do their business. And we get to see the scoresheet at the end of the day, we don't necessarily get to see that in other companies. But really a fantastic example of new leadership coming in, made bold sweeping changes, probably a little bit of luck, which most success stories have, but you know significant top-down culture change. So how do you see cultures changing with bold leadership and old-line companies? Can the old guard flip? Do they need to bring in new blood? How are people executing bold leadership? >> Well first of all, I do think that it's not about old-line, it's not about young, it's really about leadership. And so it is very dependent on who is the CEO and what kind of a board we have, and so, we don't, both of us don't subscribe to the idea that you have to be born digital to be have a great culture >> To be digital. >> Yeah to be digital. And I would say that, one of the key things we saw in the study was around transparency of goals. And we talk a lot at Accenture about transparency creates trust. And so when you think about, how do you change a culture? Bold leadership is in part to find in the research by the willingness to set public goals, and to be transparent and that creates the trust. The trust of your employees, and the trust of the people you want to attract. And what I often will say that is, when we put out our statistics in the U.S, we're the first professional services firm, it wasn't that we had phenomenal statistics, but the fact that we were willing to put them out created trust that we were trying to change. And it helped people want to be a part of that change. >> Right. I mean you know that, you guys are in this business, if you can't measure it, you can't improve it. It's interesting, the Anita Borg organization puts out a self-assessment, we do their show, and Grace Hopper, to have companies. Again, not necessarily that they're going to score high but at least they recognize the problem, they're trying to measure it, they're trying to set a base line and make moves. We've heard that from Brian at Intel, Intel's making moves. And you guys have made a very definitive statement, write a line in the sand, at 2025, you're going to hit 50%. I believe that's the goal. >> Correct. And not only do we say that we're going to do it but we're doing something about it. And a lot of companies will say they want to achieve gender equality, but it's actually the actions that you take every single day. And then, of course, reporting on your progress, whether it's what you wanted to see or not, just the full transparency around the scorecard is important. >> Yeah, it's so critically important cause again, if you can't measure it, you can't change it. So great event here, as you look forward into 2018, I still can't believe we're a quarter of the way in to the year, it shocks me. (laughs) What are some of the priorities for 2018, if we sit down here again a year from now, where will you have moved on that measure, what are some of the things that are your top priorities around this initiative this year? >> Well I know for me, we certainly are trying to make sure that we continue to make progress, but I also think there's a growing conversation about the intersectionality of diversity, and so, it's women in color, it's race and the workforce, and so. We're a global company, but certainly in the U.S, which is part of the business I lead, we are not only focusing on gender, but the intersectionality of diversity and on race. >> Yeah and I think just broadening the conversation from gender diversity to true equality for all is really the big push for us here at Accenture now. And I think it's essential that no part of our organization or no individual gets left behind. And that's what we're really focused on. >> Well that's great, and so I want to thank you for having us, and wish you well in 2018, and really a fantastic event and super, super initiative. >> Come back in 2019 and we'll show you our progress. >> Alright. >> Exactly. >> She's Julie, she's Ellyn, and I'm Jeff, you're watching theCUBE from International Women's Day at the Accenture event in downtown San Francisco. Thanks for watching.

(upbeat music) >> Announcer: Live from Las Vegas. It's The Cube. Covering Interconnect 2017. Brought to you by IBM. >> Welcome back everyone. We are here and live in Las Vegas. This is The Cube's coverage of IBM's Interconnect 2017. Three days of wall to wall coverage. Day two here. I'm John Furrier with my co-host Dave Vellante. Our next guest is Harriet Green, General Manager of Watson IoT, a Cube alumni. Great to see you again. Thanks for coming on The Cube this year again, appreciate it. >> Oh it's my pleasure. I hope we're going to talk about Internet of Things, what's in customer engagement and education. Those are things that we hope to talk about. >> Congratulations. You guys have an IoT center now in Munich. You guys had that big launch there, but the real thing that's happening in context if you could zoom out on this is that we're seeing the trend of cloud and big data world being kind of accelerated together and IoT seems to be the center point of the action because it's industrial, it's business, it's people, it's cars, it's the world now. The data piece of it is really accelerating. Now combine that with machine learning, and the glam of AI, the sizzle of artificial intelligence and cognitive really kind of puts that at the center of the conversation. This is transformative. >> Oh totally and I mean you guys were at the Genius of Things so you know that there were 600 Cs: COs, Chief Innovation Officers, Chief Digital Officers from 400 different companies, accounting for about two trillion of revenues. You're exactly right. It's across every major industry, every major sector. I think there are kind of three critical elements. The first is that with the whole proliferation of sensors and the cost points, etcetera, the amount of data and information that is being created is absolutely suited for Watson. So all of those clients there, as you know, are working with us and we shared 22 major outcomes: things faster, cheaper, better, that clients are actually experiencing. Watson is the differentiator and from an IoT perspective, I think the other piece is for a very long time IBM has proven that we respect and keep people's data perfectly safe. We don't use it, we don't open it, we don't go into it, we're not taking it for a future world of knowledge graph. We consider client's data to be their DNA. People know that when you're doing IoT with IBM that deep level of security is imbued within our capability. Then thirdly, who's data is it? Which is a huge thing in Europe and we're able with our data centers to demonstrate if you want to keep that data within lower Bavaria, that's what we'll do. And those three elements, I think, are fundamental; cognitive, the protection of the data, and who's data is it? >> 'Cause who owns the data is really important. It's a big differentiator because the data informs the model. They're almost intertwined so who owns the model? The client owns the model? Is that correct? >> Yeah, but I think people have over-complicated this, those who perhaps do not have such a simple and clear answer to it. Who don't have written into their terms and conditions that it's actually their data and they can hang onto it for as long as they like. We have always to our clients said it's your data. It's absolutely your data. If we create something together with your data, it's still your data. People only start to confuse this when they have primary and secondary and tertiary levels of confusion to support their particular cause. There is no confusion with our clients. When you talk to the chief digital officers of Shaeffler, of ISS, of SNCF who are up on stage with us yesterday, people who are demonstrating amazing outcomes that they didn't have before with IoT, they will say to you there are three reasons why we went with IBM. The first, the platform. It is the best IoT platform. From an IDC, from a Gottner perspective that's what Forester, what the guys say. Secondly, our applications are very robust and help people get started on this IoT journey. Thirdly, that the digital transformation that is happening alongside this, back to your convergence point, we're also able to assist with our GPS IoT practice. >> And you're accelerating that too. Ginni Rometty on stage talking about how that Watson's learning faster by industry but it's not a silo thing. It's actually accelerating the transformation components. >> Well, you put your finger on that precisely because the amazing thing about the Internet of Things is it's not just consumer, it's not just one industry. We're interfacing 34 different industries who are represented at the Genius of Things. It's also affecting life. Yesterday you may have seen ISS and their amazing building that they've created, which now as you arrive at terminal five, wherever it is, a huge rush and suddenly the elevators don't work. Remotely these elevators are being fixed and the journey is absolutely amazing. It is kind of is industry. >> That social good angle is important is the cognitive for social good trend going on right now culturally. That's really important. But I want to ask you- >> But I do think on the ... Ginni announced in Davos our cognitive principle. There's no client working with us that doesn't know we're working from a cognitive perspective. We go to great levels to explain what we are doing, to whom it belongs and that charter is not something that we just came up with. That's IBM for 105 years. It's why I chose to come here around the Internet of Things. >> It's super inspirational for me personally and I want to ask you about a topic that's passionate for us as an organization. We've had the largest library of women in tech, going back to 2010, we've been interviewing some of the great leaders in the tech industry. This is really now going really amazing. You heard Mark Benning up on stage talking about all the goodness going on around equality and pay, everything else going on but there's more women now instrumental in all the computer science and business side. How are you continuing that? We talked a little bit about this last year with the mentoring. How do you attract the talent? How do you get that inspiration for the young women and girls out there from whether grade school, high school, college? What's the plan? >> Well, first of all I think IBM has on every level a proven history of diversity. 35 years before the equal pay act we were equal paying. We have an incredibly diverse cultural environment where regardless of your age, your sex, your color, your creed, your sexuality, or your physical ability if you're good you'll get on. IBM lives and breathes that in every sense. Now I think the challenge is in North America particularly, in the 80s 30% of young women were going into the STEM subjects and now it's dropped just below 18%. I think it's absolutely critical that investors in companies are thinking about this equality and measuring the power of diversity and innovation. That leaders inside of businesses do more than just pontificate on stage but live on breath it. as Ginni >> Walk the talk. >> Harriet: Does. And then also that all of us in our decision making, particularly, I did for International Women's Week last week a whole webx around inclusion and how we include, how we exclude, and I shared a particular story of a couple of weeks ago some said to me you're just such a left field candidate, Harriet. And maybe that's a compliment. He happens to be a very nice guy and maybe he's right but we want people to feel inclusive. One of the most amazing things that IBM has done for some time which is almost unique, up there with Watson, is we do this to attract millennials particularly, but anyone can participate. It's a program where we take people who go in a totally immersive six or seven weeks. It may be human trafficking in Thailand. It may be helping to train and educate in sub-Saharan Africa and they work with local bodies, local institutions, really helps build this collaborative capability. And then all of the work we're doing with Ptech around up-skilling and ensuring that the STEM subjects from a very wide range of young people are really embraced. >> Harriet, you're getting requested 'cause you got to move around the events so many places and your time is very scarce and you have to move to the next event. Thank you for taking the time to share that with us and also the awesomeness around IoT and Watson. Appreciate and good to see you. You look great. This is IBM Interconnect. Harriet Green the leader of Watson IoT Customer Engagement and Support. I'm John Furrier with Dave Vellante. We'll be right back with more after this short break. (upbeat music)