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DataRobot Company Profile, Stock Price, News, Rankings

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DataRobot | Fortune

DataRobot Company Profile, Stock Price, News, Rankings | Fortune

Vivek LUTHRA, Senior Managing Director, Growth Markets and ANZ Data and AI Lead, Accenture  Debanjan SAHA, CEO, DataRobot Moderator: Ellie AUSTIN, FORTUNE

Brainstorm AI Singapore 2024: Unlocking AI To Boost The Bottom Line

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You both work with companies across a range of sectors. Can you each highlight one or two industries that you think are really leading the way in terms of turning A I integration into revenue? But let's start with you. Sure, thanks. Good evening everyone. My name is Z. I'm based in Melbourne, Australia and I run our accenture's regional business for the A I I think the key sectors where we seeing uh you know, business leaders that are what we call, uh you know, strategic bets include datas like banking, insurance, health, those three would be the top three sectors. We see a lot of action. OK. You're kind of similar. There are four industries where we see a lot of traction. One is financial services including banking and insurance, health care, retail and manufacturing. There is a lot of actually work in manufacturing where they're using both generative and predict act. And are there any industries that are encountering specific challenges? Do you think? Uh look, I think uh surprisingly, the industries which are more regulated are actually using it a lot. You know, contrary to what uh I think uh you would feel uh actually, if you see whether it's health, it's regulated, financial services regulated. I think it's mostly uh manufacturing industries which are, uh if you think about the spectrum of A I, you know, classical A I, which is in terms of, you know, let's say predictive prescriptive modeling has been around for a while. I think as we kind of start to join generative A I to it, we see for example, manufacturing companies taking a little bit different approach, taking time to be able to, you know, get it. But we are already seeing some initial leaders in that sector. And when you say taking a different approach, what are they doing? That's different? Sure, you know, the kind uh the context is a little bit different. Uh asset heavy industries have different types of business uh conditions in which they need to work. Uh If you think about to get maximum value out of A I, you got to get your data sets ready. Uh And for asset intensive industries, the data sets could be very specific to the type of instrumentation they use, which is not the same as let's say financial services, for example. So I think those would be the kind of thought process which some of the clients we work with are going through. So bon if you're looking at kind of your portfolio of of clients, can you outline some of the specific obstacles that you think exist to unlocking the bottom line as, as the session is called, well, I mean, you know, I actually see uh three gaps which I think uh our customers are kind of uh dealing with one they call the value gap right there are, you know, a lot of excitement about A I but translating that into business outcome is not easy for various different reasons. Sometimes there are not enough uh tools and platforms that people can use. Sometimes they don't have the data, sometimes mapping the business problem into an A I problem is not easy for people. The second thing that I see, I call it confidence gap. There are a lot of people who are kicking the tires, they are doing prototyping, but they are not confident enough to take those applications A I applications or models of production because they are not sure about the accuracy of it. They are not sure about the safety and other risks that associated with those models. And third of course, is uh the expertise gap, right? I mean, this is kind of a huge area of focus for a lot of people. Uh There are not too many people who actually know how to build, for example, foundational models, right? But luckily you don't need that many people to build foundational model, what you really need people who can use these models to actually solve business problems. Even that I think requires lot of expertise and training. Other thing that I think is also going to happen, people are worried about is that do all of this, that A I is going to disrupt the skill mix in an enterprise. And people are worried about how, what impact it's going to have. For example, you know, various different jobs that people do today, what is going to happen to those jobs. And there are new jobs which are going to be created by A I which requires also skilling up the workforce. So what would be your advice to someone, a young person who is thinking about what to study at university? They want to thrive in this new A I landscape. What should they be doing? What, you know, I have one of those at home. My son just graduated high school and he's a computer science major in um University of California and he's kind of a little bit worried what is going to happen to the coding jobs, right? Because, you know, there are a lot of coding jobs which I think this A I copilots are going to do, but I do think it's also going to create new types of jobs and where I think it's going to be probably most lucrative and productive in terms of skilling up is that when you can marry the A I skill with, for example, a specific domain specific skill, right? For example, if you are an expert in biology, how to apply A I to do new medicines or personal medicine would be a huge thing. I think even, you know, some of the other areas, for example, education, I do think personalized education will be a big thing. So how do you use A I to create those new industries, new use cases, new businesses. That's where I think going to be the most action and that's what people need to learn. So that's what people should be thinking about as they, as they map out their careers. I'm interested in the confidence gap that you the expertise gap, Rebecca. That's something that you, that you've witnessed as well. And if so, are there any demographic specifics to that? Is it, is the, is the confidence gap more prevalent in some parts of the world than others? Is it women rather than men? Are there any specifics around, around those factors? Go ahead. Do you have, I think, look, the we are going through a large pivotal moment. Uh I think not having the right talent in the right places is a challenge which every business is facing right now. We just can't find enough uh you know, uh talent to go about doing what we call reinvention, thinking end to end. How do you really not apply this incrementally? But actually invent examples, you know, we worked uh uh maybe a little bit sidetracked but we worked with the food and beverages company uh in uh in uh you know, globally and there by using an A I driven solution for marketing content generation, we could actually build one year equal worth of content in about eight days. And that is phenomenal in terms of productivity enhancement. But at the center of it is how do you actually transform your workforce to be able to really scale this? One needs to think beyond use cases to business capability. You know, how do you think about a product launch versus a photoshoot in which you can kind of apply something and so on and so forth? So I think in terms of the question you asked, I think talent is difficult to find. Uh companies have to think about what does it mean from a workforce transformation perspective at accenture, we specifically have worked a lot on what we call what the future of work is going to be starting from there to be able to kind of think through. So if that is the future of work, what is the type of workforce you need and then be able to define which type of workers with what skill sets are needed and have structured curriculums on? How do you actually transform the work that kind of becomes quite key. And I can show you today, I mean, we are trying to get talent in every market we operate in talent is limited. So it's a combination of getting the right set of basic capabilities and training them internally in your firm. Actually moving people, I mean, conventional data versus modern data is a big area. For example, because if you don't get your data, right, how do you kind of really unlock value from A I and so on and so forth? So hopefully that gives ideas. So in terms of confidence gap, I think it depends on the risk level of the use cases that you are addressing, right? I think, you know, the EU A I act actually has done a pretty good job in defining those risk level. There are use cases where you probably should never use A I, right? For example, social justice and things like that. And there are use cases which are high risk, there are use cases, there are moderate risk, there are use cases which are low risk and it depends on the regulation that people have. And you know, one of the things I tell people that um the CIO S when you talk to them, they are concerned about three things that can A I create top line growth. Can A I save cost? And how do I stay out of jail and stay out of jail is where the confidence come into play. And if you talk to the cio s of regulated industries, that's where they are most worried about because it can not only create business risk, it can create reputational risk. Sometimes it can create many other type of risk. They can be on the wrong side of the law, for example. So what's your advice? To the leaders in this room on bridging that confidence gap, getting to the other side of it. Yeah, I think, you know, the first thing to do is to define the risk profile of the use cases that you're working on. And depending on the risk profile, there are different steps that you take. For example, what kind of testing you need to do, what kind of disclosures you need to have, what kind of moderation you need to apply to the results produced by A I? Right? So if you think through that in a systematic way, I think you can take care of the risk and you know, figure out where you can take it to production and what actions you need to take. If things go wrong, I'm gonna open it up to the floor for questions in a minute. Um I've just got a couple more for you both. Um So Banton, you've been a data robot, a CEO for about 18 months now, I I believe is that about it? Um And when you started the company was, it was in a bit of a rough spot. Think there just been quite a few layoffs, but you've really executed a turnaround. There's a new platform 9.0 you're rolling out various integrations. Can you talk to us a bit about the role that A I has played in that turnaround internally? Well, I mean, you know, when I joined the robot, um gene TB, I kind of was not the thing, right? So Gener TB, I happened, I remember I was actually in a Morgan Stanley conference in San Francisco. And everybody asked me about Gener TB I and the way back, the Uber driver squeezed me for half an hour on Gener TB, I, I knew that Gene TB I has arrived. And so Gener TB, I has played really a big cut, you know, part in turning around the company because there is so much of opportunity that I see with Generative Bi and what we have also done, we have been in A I business for a long time. We have been a P A company for about, you know, 12 years. But some of the things that we developed for predict bi applies quite well to generative Bi, especially when it comes to governance and observable and things like that. And what I have seen is that is the combination of predictive and generative bi the intersection of that you find the most value added use cases for businesses, right? And I think of this in terms of the lib brain and the right brain, the lib brain is your analytic part, the predictive A I and the right brain is your communicative part. And when you put the two together, that's when you get the iphone moment of A I which is what has happened, right? Go ahead. The, that's a question for you. So accenture has committed I think $3 billion to A I um how are you going to ensure that that investment changes the way that you interact with your client in a positive way, maybe accelerate the process. But you also maintain that high touch human element that accenture is so well known for. What's the balance that? Sure. And thanks for going there, I think it's a very committed investment from accenture. Our CEO could have announced that in 2023. Uh I think the key parts to that was how do we kind of really work with the industry and shape the industry's future part of that was how do we build industry specific solutions, functional specific solutions, uh build a network of what we call uh you know, uh Ja I studios across the globe, be able to really help our clients uh see what's the latest uh across the globe when they come to such a center. Uh The other part was uh you know, uh training our own workforce trying to kind of do that. So largely how do we keep humans at the at the center of this? If you look at most of the research accenture has published quite a bit, about half of the benefits come from automation to the bung point as you go from classical A I, which was all about data to generative A I which is so much about language when you put it together, what you can do in terms of automation, like, you know, autonomous processes that scale is amazing. Uh However, you know, as you kind of go towards, you know, putting this together rest half of the benefits are what we call augmentation based. So you got to start thinking not incrementally around processes, but you need to start thinking reinvention. How do you actually start to think in a new way, how you would do customer care and it's very, very different than looking at how you're doing it today versus how do you want to do in the future for doing augmentation? You got to start with putting humans to the center thinking about in this new world. What is the role which machine plays to kind of really support humans and still get to benefits and the benefits are not just automation, productivity benefits. The real benefits are around outcomes having different types of work, which is more interesting getting to what we call a 10 X impact in clients, business and so on and so forth. So I would think that's how we are looking at. How do we put humans at the center? Quick question before I open it up. The bon you mentioned before the iphone moment for a I, I'd love to know from both of you. What would be your personal iphone moment for a I maybe it's happened but maybe it hasn't. What would that look like for you? That light bulb, massive light bulb moment? That changes everything. Well, I mean, you know, it is when A I becomes pervasive in everything that we do, right? And I don't know if I'll call it iphone moment, but this is an example use case which kind of fascinated me for a while. Uh So we have customers who use classical A I to do various different kinds of forecasting. We have a grocery store customer, they own a lot of grocery stores in the US. And they use our models to predict, for example, how many mangoes they need to order in a particular store in California, right? And they have been doing that for every product in every store. Some various different days have different models and there are thousands of those more and what they have done with Gene Bi and which is quite fascinating. Now, they figured out how many excess mangoes they are going to have in a particular store. And they are creating personalized coupons that they are sending out to people that they know they are also creating, for example, very, very localized radio ads that they are putting in various different radio stations. For example, if they know that Indians like, you know, ripe mangoes and Mexicans like green mangoes, they will put a Hindi Language ad in a local news station and they are doing all of this thing automatically. So, you know, pretty much everything that we do probably can be made more intelligent with the use of A I and it's when it becomes pervasive and invisible, that's when you know that iphone moments will happen. So, no leftover, man, guys, that's very interesting. It's interesting. Look, I would kind of want to say that maybe last year it was mostly about experimentation and we work with businesses, but this year we are seeing businesses really put their heads together to be able to scale, to be able to scale. You need to think about, you know, the competencies across having a large language model is a small part of that which you use for doing whatever. There are normally seven things we see leaders who do this well, kind of bring a lot of things together to be able to scale this up. So think about being on cloud, that could be one thing. Second is getting your data ready, which we spoke about third, think about the enterprise grade A I solution. That's not just about the model on the model, you have adaptation on top of adaptation, you have consumption. So you've got to put that thing together, which becomes important. You also want to integrate that with your, you know, digital platforms, like your enterprise, the P platforms, the marketing platforms, the sales platform. So there are a lot of these things which come together to be truly be able to unlock the value and be able to scale up. And I think that to me is where we're seeing a big difference from last year to this year. Any questions for the V and a man? And if I raise your hand, if not, I will continue with one last question, I, I wanna finish on the subject of timeline. So you've just out laid out multiple steps. So that um if a, if a client comes to you and says, hey, we're about to invest a ton of money now in A I, this is our priority going forward. What's a realistic timeline for them to see meaningful returns? Sure. And, and let me take uh examples, right? Uh I think in the press uh uh accenture and National Australia Bank recently announced a journey we've been on with them for about 1.5 years now. Uh through this process, they focused on how do we invent uh uh using uh A I generative specifically in customer service and operational efficiencies. Think about 20 odd use cases, about eight of them being enterprise grade level. So that's about 1.5 years. What I want to say is I think the key thing is it is a multiyear journey, right? If you're thinking about reinvention, my encouragement to leaders I work with is to think beyond use cases to be able to go at scale, you got to move from use cases to business capability and that takes time. Take a lot of thinking, I would highly encourage all of us not to kind of really get bogged down by incrementally changing the process investing in your own workforce is going to be very important. And then thinking through all these seven conference we spoke about, but really it is a multi year journey. You know, you got to put, be thinking, how do you not going to do incremental changes to what you're doing today to be able to and one final tip for the people in this room, if they, you know, they in companies that they want to really unlock as we at the bottom line going forward. What's the one thing they should go into their offices tomorrow and start thinking about? Well, I think I do think use cases and returning value to business is important. I mean, you know, for J we are in the honeymoon period and that's not going to last very long, right? So it is important to pick the use cases which show near term value, but also have a long term plan so that you can show the value at scale, which I think is going to happen. But showing near term value and showing some return on investment, showing some successes, think it is very, very important. One final bit of advice pretty similar, I think lead with value. That's what we do with all clients. You've got to be really knowing what you're solving for from a business perspective. This is not a tech for tech. After decades of technology life cycle, you have something where the technology is adapting to how humans work. I think it's an exciting era if approached correctly. There is so much to be done in terms of unlocking value. Thank you so much for joining us. Thank you. Thank you very much.

CEO Debanjan Saha emphasizes the significance of fostering a culture that unites people, highlighting how it contributes to driving innovation.

DataRobot aims to make AI a reliable tool for companies

Leadership

Data robot is, you know, is an A I start up. We have been in the business for about 10 years and we provide an end to end platform for our customers. We think of ourselves as a very, very innovative company but innovation that really matters to business, innovation that really impacts what our customer are trying to do with technology, not just solving business problems, but to have a long term positive impact on the society. A big part of A I is really the trust in the output that you generate. So we have a lot of instrumentation to do, for example, governance of the solution that we are putting together, that means testing, that means validation, that means compliance etc. And after you do that, you have to then deploy it in production and there you have to continuously monitor them. Everybody is excited about J but the real issue there is the confidence, right? I mean if you ask chat GPT a question and you get a funny answer, you can laugh at it. But if you are an enterprise using jai, you better be sure that answers are really correct. So that if you are not careful with that, it can undermine the trust in what you are producing and that's not good, especially in enterprise. There are three things that I believe is very, very important in leading a company. One is having clarity with respect to vision and mission because without that, you cannot really convince your team, your customers, your investors, that what exactly you are doing and why I think why is as equally important as what is right? The second thing which I think is important for any company, but a purpose driven company to build that culture, which brings people together, right? The company is really people and ideas and the fabric of that is really the culture. People think big, they are not concerned about taking risks. And the third thing which is important to me is, you know, execution part of it, which is about decision making. Of course, I make, you know, some of the difficult decisions. My goal is to make as few decisions I need to make. But the difficult decision that that's important. But every day in a company, you are making hundreds and thousands of decisions and that you need to empower the team so that they can make those decisions in a data driven way and do various different kinds of things in figuring out how they can make those decisions with a high quality judgmental way. At the same time, do it quickly.

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