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Vivienne Walt

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Maria Belousova, Chief Digital Officer, Indigo Ag 
Jennifer Majernik Huffstetler, Chief Product Sustainability Officer, Intel  
Rushad Nanavatty, Managing Director, Rocky Mountain Institute (RMI) 
Moderator: Phil Wahba, Senior Writer, FORTUNE

 Brainstorm Tech Dinner 2023: Discussion

Conferences

Maria Belousova, Indigo Ag 
Jennifer Majernik Huffstetler, Intel  
Rushad Nanavatty, Rocky Mountain Institute (RMI) 
Phil Wahba, Senior Writer, FORTUNE

Maria Belousova, Chief Digital Officer, Indigo Ag 
Jennifer Majernik Huffstetler, Chief Product Sustainability Officer, Intel  
Rushad Nanavatty, Managing Director, Rocky Mountain Institute (RMI) 
Phil Wahba, Senior Writer, FORTUNE

Welcome everybody to our panel. Uh the timing is really great to have this discussion on climate tech today in new york where I live record temperature of 66 san Francisco uh is got 5.4 inches of rain on uh, one of the weekend days and another five inches coming. It's good that it will help us the drought, but maybe a bit more moderation would be good. Um, europe in Amsterdam where you're based, my in laws told me that the canals had frozen in december and today with 62 in the Netherlands in january. So, uh, there's something really wacky going on with the climate and apparently some of you have solutions for that. So, um, why don't we start with um, maybe spend each 30 seconds a minute to say a little bit about what you do and how it um, how this can help with the climate crisis. So why don't we start with you Maria? All right, so my name is Maria, Chief Digital Officer, um, integral agriculture. So what we do is we use science and technology to help farmers transition to regenerative practices and then we connect them to help them monetize this transition with corporations that are looking to reduce their footprint either abatement and sequestration. So, uh, this is really a technology company uh science company and a company that helps farmers be more profitable, produce better food and also helps corporations meet their goals, Rashad Nanavati. I work for Rocky Mountain Institute or R. M. I, which is a environmental nonprofit focused on the energy transition in climate and within our M I I run a program called third derivative, which is an accelerator focused on helping early stage climate tech find a faster, more reliable pathway to market. We have about uh at this point in time, over 100 relatively early stage climate tech startups in our ecosystem. Uh and we set it up realizing that climate tech is is hard. You're talking about hardware, you're talking about molecules as opposed to bits and bytes uh builds point obviously important point not withstanding. Um and you're talking about um the need to integrate these solutions into existing systems into existing value chains and uh that requires partnerships. It requires a lot of capital, patient capital and that's what we work on helping essentially connect these startups with funding, helping connect them with partnerships and markets and helping them with knowledge and technical assistance, including uh that delivered by the 600 odd kind of climate and energy nodes that are at our mind. And yeah, thanks. Um nice to be here. Uh jen Hufstedler intel corporation. I didn't pay a bill for all those moore's law quotes they gave on stage, but we're of kindred spirit um that we truly believe that compute can help solve um a lot of the climate problems we have and you know, we're on a mission. My role is looking after all of our products across the company and how we can help build solutions with the ecosystem to deliver sustainable computing for a sustainable future. Uh And we know it's gonna take more than one company alone. And so we we have a lot of leadership in our manufacturing space where we've already had decades of investment to lower our footprint and we're now on this last mile journey and sharing it with the rest of the ecosystem. How we can help supply chain for all of the semiconductor and computer industry to achieve sustainable computing. Okay, well, I want to ask each of you uh an individual question but let's follow up with that. Can you tell us a bit more how you help your clients with the climate in terms of sharing your knowledge? Like a concrete example perhaps. Absolutely. Um We're actually I was just sharing with one of my tablemates um you know from the telco industry and networking um a little known fact, data centers are a hot topic these days. Right? Getting power into a data center, it becomes the rate limiter um of how much computers can get into service all of our needs and for all the companies around the globe, whether it's our banking, transportation and beyond. Um uh of that network transmissions about 50% higher energy consumption than data centers. Um The telcos, uh their mobile network is the large portion of it. So every time we're taking out your phone that mobile network is consuming more energy than any other transmission. So one example with a client that we have with deutsche telekom is you know using ai I heard it out there in the talk and at our table as well um the software because moore's law and compute became cheaper, the software became less efficient. Um You know you go back to assembly code and the limited resources that you had back in the day um the software and the hardware much more tightly coupled. Um so now that they're more loosely coupled and time to market became more important utilizing ai and the telemetry on the system and what's being utilize the customer traffic at Deutsche telekom when it's lower they're able to look at the incoming traffic and turn off course for example in a processor and idle those when they're not needed and then turn them on in a responsive time um as the traffic increases over time. So that's an example with five G networking, we're all using it with the phones in our pockets. Um But that's a real, you know, applicable way to use ai analytics um and computing to lower the footprint of everyday energy consumption. Okay, Maria, can you give us an example of of inefficiencies in agriculture and food production that that you're you're helping. Uh Yeah absolutely. So Bill was talking about timing and how timing is really, really important. And so right now you have this situation where corporations CPG are looking for sustainability solutions, right? And you know whether it's abatement or sequestration, they're looking for how they can source better grain, how they can reduce their footprint. And um on the flip side farmers are looking how to be more profitable right now the last couple of years were really amazing for them. But you know in general farmer farmer profits are you know, not drawing. Yeah. And so we're in this like in the middle of this right? And so what we realized is that using the existing agriculture land, using the infrastructure and using technology and science on top of that we could help farmers. So essentially what we do is we help farmers transition to regenerative practices, right? And in many cases these are farmers that are doing commodity farming uh and we introduced these practices, you know, no tilling better cover crop use, um you know more responsible fertilizer usage and as they do that we have the technology and the data, satellite imagery data, john Deere machines, data, all this like you know, trillions of terabytes of data that we can use to actually measure the impact of these uh what the farmers do and uh Transition this impact of convert this impact into financial instruments, right? Carbon credits. And so last year we did our first release 20,000 credits which is like 20,000 tons of carbon uh C. 02 right, sequestered atmosphere. And at the root of that is really interesting. It's a good old photosynthesis, right? When the plant grows it takes ceo to convert it into carbon and deposited in soil. And so what we do is we just use technology to prove that this happened so that we can actually produce a high quality credit um Rashad, you you guys, Rocky Mountain, um which makes me laugh because the Netherlands does not have anything resembling the Rocky Mountains, but uh, but it's not based in the Netherlands, you are, but uh, you guys advise cities and governments, Right? So one thing I often wonder is, you know, governments at various levels play a central role in protecting the environment, whether it's changing rules, whether it's incentives. Um, and I'm just wondering as a new yorker and we keep hearing these horror stories of how the tax base is eroding because our office towers are empty. Um you know, just as one example, you know, governments might not have as much money as we would like them to to support all this. So, you know, what's your feeling about the ability of government to spur innovation, to support it to incentivize it. And also, you know, provide the laws that that will foster, I think there's plenty of capacity to to do that. And, you know, the points that both um Bill and scott made a kind of representative like 2022 was massively pivotal in so many different ways. When you look at the market fundamental again, the point, the bill made like solar and wind are the cheapest energy sources in human history at at this point in time. Uh, fossil fuel consumption has peaked, it may have peaked in 2019, but it almost certainly peaked in 2022. And the climate specific legislation that's been passed between I. R. A. The chips Act II. J. In the U. S. Uh, that represents a fourfold increase in climate spending um, over anything that we've seen in the recent past uh, collectively. Uh, it represents just the legislation passed last year uh, is more significant than all climate and energy transition related uh, legislation passed prior to that fact. And it's not just the USA you had, um, Japan with its G three or I think it's called the G three strategy. You had the EU with the R. E. Power initiative, you had china's latest five year plan heavily focused on the energy transition. Um, and in in addition to that, those generational investments, I think the other thing that really changed was the narrative. Um, it was no longer one that was focused on sacrifice or cost or pain. It was about opportunity and gain. And that's massively significant because it suggests that all of those policies are actually going to be durable, which is critical for investors to have that level of certainty, which obviously means very, very good things for climate deck investing. Yeah, I'm not to bring up the Netherlands again, but it's a small country, 18 million people, but because they they are threatened by flooding. They that happened in the fifties their survival is at stake. And yeah I hope here in the US we sort of understand that Katrina should have been a warning. Uh Sandy should have been a warning. So um should I be a bit more optimistic about generally and and like jen Maria to chime in too that our leaders understand how serious this is? I definitely see a significant shift in in, you know how the government is playing, how how the corporations are playing. You know when I look at all of the C. P. G. S. And the corporations that are interested in talking to us that there's definitely significant momentum and you know something else. Uh Just interesting shot mentioned that 2022 was significant in terms of what the government has done and we're working with the government in so many ways. Like we're engaging with U. S. D. A. Both as recipients of the grants as well as talking about the policy and the regulation that it's coming out right? Like they're talking about, you know what what is like how do you do scope three right? Like how do you do this accounting? It's really really complex, right? Like the questions about permanent addition. Al Itty and all of that is it's significantly complicated, very difficult to do with tech, very difficult to do with data. It's very difficult to do logistically and yes if you want high quality and high bar, we need to do this. So it's really interesting to see what the government is doing and how they're influence. Um I'm gonna open it up to questions in a moment, but uh I guess to me what I find interesting is as people, as investors want to look into this technology, um evaluating return on investment and evaluating the size of the market, everything will be affected by what role government plays more so than uh you know, the pure tech tech for entertainment and things like that. So um is so how how do we evaluate, you know, people want to invest in this space, How do they decide how to put their money if they don't really know what the rules are gonna be or how things will be measured or? Yeah, uh I make maybe a couple of points. One, it's it's the biggest investment opportunity writ large that the world has at least over the next decade. Uh I think the Bloomberg any f number said something like 755 billion was invested in 2021. If you consider what's needed to get the world net zero, we need to actually triple that number by 2025 then double it again before 2030. Uh So, you know, there's a simple appeal to to human greed uh and uh and the desire to make money because it's it's it's massively significant. The other thing I would say is for anyone interested in the spaces follow the emissions. We see this when you uh look at kind of where existing climate tech investment is going. Uh mobility, Transportation accounts for something like 15% of global emissions but accounts for 50% of climate tech funding, heavy industry. The hard debate sectors uh accounts for something like 35% of emissions, only 12% of funding. So if you want to have an impact in the world uh following the emissions makes obviously a ton of sense. The uh the third thing I'd say is that it's not necessarily about new invention. Uh it's about getting stuff out of the lab and into the market against stuff that Bill and his enterprises have done really, really successfully. The I a. Looked at something like uh all of these sort of climate technologies out there identified about 400 that need to become dominant in order to get to net zero. Only 60 only 40% of those right now are truly uh commercially viable. And so that's the challenge uh that overcoming that sort of early stage, all those early stage values of of of death. And the last point I'd make is is decent to the U. S. And and the rich world. Um Uh 50% of climate tech investing in 2021 actually happened in Asia energy consumption in Asia. Asia is set to rise by two or three fold over the next couple of decades. 13 of the biggest cities in the world by the end of the century are actually going to be in Africa and all of that has pretty profound implications for the technologies that that matter. Are there any questions while folks are thinking I can add to that, just, I mean it's it's about energy right at the end of the day and you know, as fast as we can all get the renewables everywhere is how we're going to solve this problem. It's distributed solutions. It's in your homes, it's in your business is uh, we heard from Candace, talking about Salesforce, Uh, going to 100% renewable electricity, you know, as much as that's part of what we're working. You know, obviously our own factories, we've got, we manufacture chips with over 80% renewable electricity, right? That's industry leading. But in Asia you don't have that everywhere. So how do we collectively as a global ecosystem unleash the capacity for distributed energy and for it to be on boarded to the grid. And that's gonna take computing solutions to that are smart, secure intelligence that are now able to handle the two way traffic of energy generation versus the traditional systems that have been built. So, um, you know, I think that's really it the heart of what you're talking about and we're seeing regulations now looking for it. So you talk about how is government participating and I think we're seeing first out of europe and that's rolling around the globe and we're seeing it everywhere in our markets across asia as well. I think we have a question uh okay, we have a question right here. Well, I can't see I'm being asked to stand up. There we go. Hey, Jonathan martin from a data platform for Ai So, a lot of conversations obviously about sustainability, but also a I coming up in many of the conversations for a number of people though, these two things are quite diametrically opposed forces today, about 1.8% of the world's power is consumed by data centers and it's expected that by 2030 that number will grow to well over 8%. The primary contributed to that growth being power hungry, accelerated, compute and fast networking. So, I would love to hear that the panel's views on how we get to a more sustainable future for artificial intelligence. Yeah, I know it's a great question. I'll start and you guys can follow on it was the second part of what I I wasn't didn't say it's about energy availability and energy conservation. Right? And what ai what we can do with the Ai is to conserve the energy that's consumed by that compute. So when you think about cloud computing today, probably for the average corporation, 35% of their spend in the cloud is wasted. There's things called Zombie instances, there's just things running that aren't doing real work. So I talked about programming languages and the affinity to the hardware. Um we think about know what left behind. I also think about no transistor left behind. It was bill was talking about, you know, the silicon is out there, how do you take advantage of it? You need to increase the utilization of what's already there. There's certainly high performance computing, you know, situations that are maxed out all the time and consuming a ton of energy to solve the incredibly hard problems that they're working on. But the vast majority of data center compute it's not at a high utilization level and it's got a lot of the software challenges ai can help to resolve to identify optimizations um to put forth in whatever the system or solution is. Yeah. I'd also say that I agree that, you know, technology in general is expensive, you know, energy wise, expensive. And I think that uh some to some extent you can't do away without sequestration. You have to look for ways to capture uh C. 02 and do something with it, right? Like deposited in soil deposited and like we need all kinds of solutions. There's many different approaches. And really the question is, how can you scale it as soon as possible, Right, So that you can continue um capturing as as your meeting. Okay, well I think we're just about out of time but um this panel has buoyed my spirits. I was feeling a little bit guilty about schlepping across the country for a three hour dinner and then slipping back tomorrow, thinking, wow, I'm doing a lot of air travel to talk about protecting the environment and I had a lot of steaks on my vacation in Montreal last week. So anyway, I see. We anyway, we, we all um, there's a lot of work to do, but there's also a lot of reason to be hopeful. So thank you for joining us and uh, we'll be around for a bit longer and, and, and this was great, thank you Maria Rashad and jen.

Sasan Goodarzi, Chief Executive Officer, Intuit
Ashok Srivastava, Senior Vice President and Chief Data Officer, Intuit
Moderator: Jeremy Kahn, FORTUNE

Brainstorm A.I. 2022 - View From The Top

Yael Garten, Director of AI/ML Data Science and Engineering, Apple
Interviewer: Verne Kopytoff, Senior Editor, FORTUNE; Co-chair, Fortune Brainstorm A.I.

Brainstorm A.I. 2022 - How To Turn Good Products Into Great Ones With A.I. 

How To Turn Good Products Into Great Ones With A.I.

Brainstorm A.I. 2022 - How To Turn Good Products Into Great Ones With A.I.

consumers expect elegant design and products that are intuitively easy to use right beneath the smooth surface. The smooth interface, artificial intelligence already powers much of its operating system from Siri to face I. D. But how is this tech giant leveraging Ai and machine learning into emerging products and services? Will ask the senior Apple executives to share her insights into Ai advances at the world's most valuable company. She is also on the board of a 169 year old retail company and can address how a legacy brand embraces the technology and is able to revitalize its processes and products. Anybody know what that retailer is? Please welcome Yael Garten, director of Ai ml data science and engineering at Apple. And member of the board of directors at Levi Strauss and co for an interview with Fortune Senior editor VErne kopytoff. Hello everyone and thank you for coming. You thank you thank you. Uh to start things off, could you set the stage and list a few? I mean Apple has so many products. Could you list a few that um that use ai your favorites for example. I love them all. Um But yeah, if you think about our hardware, software and services ai and Ml Ds really extensively across them. To name a few of these, a couple of them in the information seeking space uh theory and search research for music and and movies and a lot of great content news um that those are some examples um you already mentioned face I. D. Earlier. Um If you think about the accessibility area, a lot of what we focus on is making technology accessible to everyone and making the world accessible. So one of my favorite features that we recently announced is a door detection which enables people that have low vision or blind to use their iphone camera to detect the door that they're about to near. And it reads two texts that you see on the door, how to open the door handle. So that's really a great example of making the world accessible and using technology for that. And it's powered by machine learning. Um and then maybe a third example in our in our health uh areas of investment um is um what we call walking steadiness. Um So this is an example where your iphone detecting how you walk and your stability and and uh we are able to use machine learning in this case to identify trends in your walking stability um and and identify you when there are changes to those trends and how you might take actions to improve that to help reduce uh risk of falling and other other kinds of things. So really expensively across hardware, software services, those are just a few. And Apple is so big and has billions of users. What are the unique challenges of dealing with data for such a huge operation. Yeah, well I don't know if this is a challenge. I kind of view it as an opportunity and one of the things that we um that we take very, very seriously is is the privacy of our user data. I mean, so that's something that I would just highlight as um really we put a lot of focus and thought and energy into how can we create a great machine learning based products that are also privacy preserving. Um and so that involves right, taking more time, more thoughtfulness in the design of features. How can we do more and more on the device, really leveraging the integrated hardware, software staff that we have thinking about using the hardware using the framework building applications um that enable us to preserve privacy and develop a great machine learning based products. Um so that's one of the areas where I'd highlight as we we spend a lot of time and and focus on that as well as making that something that third party developers can, can leverage as well. And when you're working on your projects? I mean, you've worked on so many, what are the key lessons and sort of pitfalls you've you've come across? Yeah. Um well maybe a company of any size, not just one the size of Apple. Yeah, well if you're if you're talking about how you implement machine learning and maybe more more broadly, I would say there's this life cycle of uh when you want to deploy a machine learning based solution, um how do you take into consideration all of the phases? And this is, again, really speaking pretty broadly, how do you take into account all of the phases of that development life cycle and plan it out? I think I mentioned earlier this lifecycle of machine learning development right. Which starts from what's the problem? We're trying to solve what's the product experience, what's the workflow? Um and this can be really in any company. Right. Apple at Levi's um and really any company. So if you think about what's the product experience and then you move on to what the data that we need to collect um to be able to create that machine learning based experience and then how are we going to train the model and how do we ensure that the data is representative? And how do we then evaluate the model before we deploy it? And how do you then ship it in terms of a really cohesive user experience and how do you later continue to evaluate the performance of that model as it's out there in the real world and as the world is changing and people are changing macro trends are changing and continuing to have that responsibility of thinking through all of those steps. I would say that in general, um underestimating the um the amount of time and investment it takes is probably one of the pitfalls that I would say, you know, entity, small and large industry academics, you know, etcetera underestimate. And so just thinking about that in advance and planning for that is something that's pretty important um, as well, putting in place the structure in terms of, you know, team and organization to have um, adoption of machine learning based technologies as something that is part of the conversation and something that companies and entities are really planning for. So that's probably kind of two of the common pitfalls and what's the level of investment, what's the workflow and then what's the team and structure that we need to have in place for that adoption and what's the, what's the secret to knowing that you're even building the correct models? What steps do you take to make sure you are? That's a great question. Um, you know, I would say it really just comes back to what's the use case, So really what is um and again really broadly, you know, what's the business use case you're trying to solve? What's the product experience you're trying to solve? Um so if I take, for example, if I think about how we develop things that Apple, we really try to think about what is that end user experience that we're trying to deliver and really obsessing over that at the beginning and ideation phase all the way through the end stage. And I think that that's really important because then, you know, she learning for example, which was talking about a lot today is is an an ai part of that, but that's not it. So, having uh, that question really guide us then brings the right people to the table. Right? So if its product managers, designers, engineers have different types of hardware, software, machine learning data engineers coming together uh bringing in, you know, members from from marketing and from the legal team to really think through what are the ethical implications of what we're doing? Um What are the intended consequences and what are the unintended consequences that might come out? So bringing together all of those components as you're developing the feature, as you're developing the solution is gonna be some of the key aspects to ensuring that it does what you want it to do and it doesn't do what you don't want it to do or that you've carefully thought through all those steps as you're going into production. Um and then again really putting in place um mechanisms and rigorous evaluation programs to evaluate after deployment. Um is it working as intended? If not, what are the changes to make? I think a big part of that is really having metrics that help you understand how the models and how the solutions are performing in the real world and being able to adopt and adapt to those metrics as kind of the North Star of are we on the right track? Is it going where we wanted it to, we go into questions in a in a couple minutes, so please have some ready. Uh Now you're on the board of 100 and 70 year old company levi's what are the challenges of getting such a legacy company going with Ai and what are some of the projects they've been able to pull up? Yeah, great. Um yeah, Levi is a really iconic brand and it's one that I would say it's actually been investing in technology and innovation for for many, many years and including in recent years in data and AI. Um and so there's it's been really exciting to see because a piece of it has been, you know, digitizing um data that wasn't digital before. Right? So moving everything from manual to digitized in a way that it can be a basis for building applications on top of it some of it interestingly has been taking data that they've had and making interesting use cases of it. So for example, think about archives of genes going back to the 1850s and how can you take um images of those genes and then start to think about ways and they're doing this in ways of um uh which are ai assisted to design new products and new fabrics based on those archives and based on analysis of those. Um So that's really interesting ways of leveraging existing data and then also great examples of bringing in external data. So for example, um an example I really liked was bringing in weather data um and using building models off of that and showing how um actually when you have differences, for example, slight differences in weather. Um the team found that in Milan versus Rome, the period of time which skirts were worn was different or the color of denim that was purchased was darker due to the winter coming. And so that starts to enable the company to shift strategically in ways that enable different distribution of goods, uh different perch of goods that are coming to the different stores and you start to think about that at a global um you know, basis and it really starts to change how goods are distributed, how you operate distribution centers, how you plan your pricing, um how you order in advance and so it starts to bring in really agile ways of um running the company um in ways that you know, prior were done differently. And so as more and more of the company starts to embrace these different, great you know, examples of great ways that machine learning or uh you know, data data science have been integrated. More of the company starts to um to leverage that in addition to just really great ideas that many companies are doing and many of you that we spoke about earlier are doing bootcamps. Right? So how do you train more and more of the workforce to understand what data science is and what a i is and how you can leverage that so that it's really something that is coming from more and more pockets of the company versus for example, just one centralized team that's developing the solutions and so those are some of the examples of how they're approaching. Um Great, great. Let's go to questions, we have a microphone in the room um just raise your hand please and stand up and identify yourself. There we go. Hi, I'm kai from iron clad uh and I was wondering are there things that you have thought or advances that you've seen in ai that you thought would be coming for many, many years to come? I mean how do you design an organization that works around kind of the unpredictability of how advanced what tasks are going to be possible versus what tasks are um maybe disappointing in how long it's taken them to be accomplished. Yeah. Um you know, I think in terms of how you, how you organize, you have to have a portfolio, a mixed portfolio of investments. Right? So we know that the field of machine learning, artificial intelligence data science are rapidly advancing and in some ways that we can't yet anticipate. Right? And so you have to have an ability to have investments that give you, you know, a steady straight steady stream of wins, um whether it wins your business when your product wins to the team's agility. Right. A lot of um aI can be used now to really help teams be more operationally effective. Um So really having a steady stream of investments that gives you um you know gains while also making some investments in the team to seed uh you know early research or investigations that might yield fruit later but may also change as the trajectory of technology is changing and we've talked about it today, it's changing so rapidly. Um and so really having a mixed portfolio is is probably what I'd recommend. Do you have another question? Anyone, anyone anyone uh Now there's a lot of hype that Ai is a solution to every problem. What do you say about that I mean and what should executives ask themselves to decide whether ai is the solution? Yeah, so I agree with you that there's a lot of hype and I also do think that um while we're at brainstorm ai and there are a lot of great uh innovations and and and enablement that I bring. I don't think that Ai and machine learning is the solution to every problem. So I do really think it comes back to the core of asking what is the business problem, I'm trying to solve? What is the product problem trying to solve and being really clear on that to then be able to map out, you know, what is, what is the hammer that fits my all right um or sorry what is the nail that I need and what anyway, you understand what I'm trying to say but but I don't think it's the solution to everything. So I think it's really important to focus on what is it that you're trying to deliver and identify is very simpler solution or it's actually the right time and space for you to go and invest in a machine learning based approach or an artificial intelligence based approach. Um I think when you've decided that it is the time to invest um and that it is the right solution for you, I do think it comes back to you touched on this earlier, it really comes back to these kind of a couple of areas of investment that it's important to to do. I think one is really thinking through that workflow end to end of, how is if you're going to put a model into production, what is that workflow, who are the end users that are going to be interacting with it and really stretch that out and to end so that you thought about all the consideration going from what data you'll collect, how will this be interacted with. Right. Is it a dashboard? Is that a ui is it a product, is it in the field? You know, what else is going to interfere without being successful? What is going to enable it being successful and really thinking through that whole workflow into. And I think we see a lot of examples where in clinical health care, it's just uh the there are a lot of great ideas on the technology side, but getting it into the clinical workflow um isn't happening and it could be because the doctors and nurses don't know how to interact with it is because it could be because I haven't thought through the financial construct of who's going to pay for it. So I think that thinking through that workflow is really important of how is somebody going to interact with the model doesn't need to be explainable. Um Is it explainable? Right, so a lot of those aspects um the second is really say around the data. Right, so how are you going to the data cleanse the data, train the day to make sure that the data is representative and kind of protect that data and really thinking through that workflow. Um And probably the third area is around um the team, right who is going to be implementing the models and how does that really get structured in terms of your organizational structure for um joint success and joint accountability. Um So thinking through what you have, is this under a general manager working closely with a data leader, where you have shared responsibility for implementing that model in production um and where the outcomes are you know feeding back to your business. So really thinking through that construct is something that helps set it for success. Yeah, thank you so much for coming. Thank you. Thank you all

Vinod Bidarkoppa, Senior Vice President, Walmart; Chief Technology Officer, Sam’s Club 
Nitzan Mekel-Bobrov, Chief A.I. Officer, eBay
Rathi Murthy, Chief Technology Officer and President, Product and Technology, Expedia Group
Moderator: Rana el Kaliouby, Deputy CEO, Smart Eye; Co-chair, Fortune Brainstorm A.I.

Brainstorm A.I. 2022 - The Age Of Intent: Knowing What Customers Need Before They Do

Vinod Bidarkoppa, Sam’s Club 
Nitzan Mekel-Bobrov, eBay
Rathi Murthy, Expedia Group
Moderator: Rana el Kaliouby, Fortune Brainstorm A.I.

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