Industries in the Intelligent Age / DAVOS 2025

Summary

This panel discussion at the World Economic Forum focused on the current state and future potential of artificial intelligence (AI) across various industries. Leaders from technology, energy, consumer goods, healthcare, and consulting sectors shared insights on how AI is being implemented and its impact on their businesses.

The panelists emphasized that AI is no longer a future concept but a present reality, with many companies already using it at scale. They discussed various use cases, including improving supply chain efficiency, enhancing drug discovery processes, optimizing energy production, and personalizing customer experiences. The importance of having the right infrastructure and talent to fully leverage AI was highlighted, with several panelists mentioning their efforts in upskilling their workforce.

Collaboration and partnerships were identified as crucial elements in advancing AI capabilities. The panelists also addressed the need for responsible AI implementation, emphasizing the importance of building trust and establishing proper governance frameworks. The discussion touched on the potential of AI to solve broader societal issues, such as improving government services and addressing global challenges like energy security and hunger.

The panelists agreed that AI is not replacing jobs wholesale but rather changing the nature of work. They stressed the importance of adapting HR practices to this new reality, including moving towards skills-based hiring and continuous learning. The rapid pace of AI development was noted, with predictions of more advanced AI systems capable of complex reasoning and problem-solving in the near future.

In conclusion, the panelists called for actions such as ensuring equitable access to AI technology globally, building trust through responsible AI practices, empowering frontline workers, and leveraging company-specific data to drive value. The overall tone was optimistic about AI’s potential to transform industries and solve complex problems.

Keypoints

Major discussion points:

– Current state and adoption of AI across different industries

– Challenges in scaling AI and transforming organizations

– Importance of data, infrastructure, and talent in leveraging AI

– Potential of AI to solve societal problems and transform industries

– Need for responsible AI development and building trust

Overall purpose/goal:

The purpose of this panel discussion was to explore how AI is currently being used across different industries, discuss challenges and opportunities in scaling AI adoption, and consider the broader impacts and future potential of AI technology.

Tone:

The overall tone was optimistic and forward-looking. Panelists were enthusiastic about AI’s potential while also acknowledging challenges. The tone became more urgent towards the end when discussing calls to action, emphasizing the need to act quickly to responsibly leverage AI’s benefits.

Speakers

– Sara Eisen – Host at CNBC

– Paul Hudson – CEO of Sanofi

– Matt Garman – Head of AWS

– Amin Nasser – CEO of Aramco

– Ramon Laguarta – CEO of PepsiCo

– Julie Sweet – CEO of Accenture

Additional speakers:

– None identified

AI Adoption and Transformation Across Industries: Insights from Global Leaders

This comprehensive panel discussion at the World Economic Forum brought together leaders from diverse sectors to explore the current state and future potential of generative artificial intelligence (AI) across various industries. The panellists, representing technology, energy, consumer goods, healthcare, and consulting sectors, shared valuable insights on AI implementation and its impact on their businesses.

Current State and Adoption of AI

A key theme that emerged from the discussion was that AI is no longer a future concept but a present reality, with many companies already using it at scale. Julie Sweet, CEO of Accenture, emphasised this point, stating that AI is being actively deployed by leading companies. The panellists agreed that proper infrastructure and data management are crucial for successful AI implementation. Matt Garman, Head of AWS, stressed the importance of cloud migration and organised data for capturing AI value. This sentiment was echoed by Amin Nasser, CEO of Aramco, who revealed that his company receives nearly 10 billion data points daily, underscoring the scale of data involved in AI operations.

AI Applications and Use Cases

The discussion showcased a wide range of AI applications across different industries:

1. Consumer Goods: Ramon Laguarta, CEO of PepsiCo, explained how AI enables hyper-personalisation and real-time decision-making for customer experiences. He highlighted AI’s role in improving agricultural yields, optimizing transportation routes, and personalizing nutrition recommendations.

2. Healthcare: Paul Hudson, CEO of Sanofi, shared that AI is transforming drug discovery and development, with approximately one-third of discovery efforts now being validated by AI, significantly increasing the probability of success. He also mentioned the integration of AI into their drug development committee, changing decision-making processes.

3. Energy: Amin Nasser described how AI is revolutionising the energy sector through data analysis and predictive capabilities, helping to manage energy grids and predict equipment failures in the oil and gas industry. He highlighted Aramco’s development of the Aramco MetaBrain and a trillion parameter large language model.

4. Consulting: Julie Sweet emphasised AI’s role in enabling hyper-personalisation and real-time decision-making for customer experiences across various sectors.

5. Technology: Matt Garman shared an example of using AI to upgrade Java versions, which saved 4,000 person-years of effort at AWS.

Workforce and Talent Implications

The panellists agreed that AI is changing the nature of work rather than replacing jobs wholesale. They stressed the importance of adapting HR practices to this new reality, including moving towards skills-based hiring and continuous learning. Key points included:

1. Upskilling: Amin Nasser emphasised the need for companies to upskill their workforce and create AI-enabled subject matter experts.

2. HR Transformation: Julie Sweet argued that HR departments need to be reinvented for skills-based hiring and continuous change management.

3. Task Replacement: Sweet also noted that AI is replacing tasks rather than entire jobs, necessitating employee reskilling.

4. Frontline Empowerment: Ramon Laguarta suggested that frontline workers can be empowered through AI to create more value. He also highlighted the need for new job families and the challenge of avoiding a divide between digital and analog workers.

Partnerships and Collaboration

The discussion highlighted the crucial role of partnerships and collaboration in advancing AI capabilities:

1. Tech Partnerships: Paul Hudson stressed the importance of partnerships with technology companies for AI innovation and implementation.

2. Cross-Industry Collaboration: Julie Sweet called for collaboration across industries and with governments to address AI challenges.

3. Cloud Provider Partnerships: Matt Garman noted that working with cloud providers helps companies organise data and adopt AI effectively.

Future Developments and Challenges

Looking ahead, the panellists discussed several key developments and challenges:

1. AI Evolution: Matt Garman predicted that AI models are evolving to use reasoning loops and parallel processing for more complex tasks.

2. Trust Building: Julie Sweet emphasised the critical need to build trust through responsible AI practices for scaling adoption.

3. Technology Equity: Amin Nasser raised concerns about technology equity, warning of potential widening wealth gaps between nations due to unequal access to AI technology.

4. Leadership Role: Paul Hudson argued that CEOs should lead AI initiatives rather than over-delegating to avoid adoption issues.

5. Change Management: Julie Sweet stressed the importance of change management in successful AI adoption.

Calls to Action

The panel concluded with several calls to action:

1. Ensuring equitable access to AI technology globally (Amin Nasser)

2. Building trust through responsible AI practices (Julie Sweet)

3. Empowering frontline workers through AI adoption (Ramon Laguarta)

4. Leveraging company-specific data to drive value (Matt Garman)

5. Prioritising the organisation of data in secure environments to leverage AI effectively (Paul Hudson)

6. Improving communication about AI to address misinformation and build trust (Julie Sweet)

The overall tone of the discussion was optimistic about AI’s potential to transform industries and solve complex problems. However, the panellists also acknowledged the significant challenges in scaling AI adoption and the need for responsible implementation practices.

In conclusion, this panel discussion provided a comprehensive overview of generative AI’s current state and future potential across various industries. It highlighted the transformative impact of AI, the importance of workforce development, and the need for collaboration and responsible practices in AI implementation. The discussion also raised important questions about global technology equity and the role of leadership in driving AI adoption, setting the stage for further exploration of these critical issues.

Sara Eisen: Sarah Eisen, host at CNBC. I’m thrilled to be back here at Davos talking about the topic that everybody’s talking about, which is AI. This is the AI panel, and it’s interesting. I’ve done a few AI panels for the World Economic Forum in previous years, and we talked about the promises of AI and the perils of AI and the opportunities for AI and this sort of magical future, but this year is different because today we’re talking about what’s actually happening, which is it’s here, and it’s being used by all the people on the stage, and I think we’re going to have a great conversation about how it’s being used, what are the challenges, what are the opportunities, and how are we all learning about it. And it’s a perfect time to do this. The World Economic Forum today, I just want to say it right, they released a white paper. They have this AI transformation of industries initiative. as part of the broader AI Governance Alliance, which all these companies are part of. And they released this white paper today. And I just want to share one of the findings, because I thought it was interesting and apropos of this conversation. 74% of companies struggle to scale AI. And only 16% are prepared for AI-enabled reinvention. So let’s talk about it. I just want to introduce our esteemed panel. Matt Garman is the head of AWS. Ramon LaGuarta is the CEO of PepsiCo. Julie Sweet is the CEO of Accenture. We’ve got here Amin Nasser, the CEO of Aramco. And then of course, Paul Hudson, who is the CEO of Sanofi. So we’ll go into the drug sector, big food, energy, and then across services and technology. But Matt, I’ll just kick it off with you. You’re the technology guy. I feel like you guys were just starting to get all your enterprise customers onto the cloud. Where does Gen AI fit in? And where are we in that process? Well, it’s an extension of that.

Matt Garman: I think we talked to, a lot of customers get super excited about what they can do with Gen AI. And then, and actually there’s kind of two groups of people that actually start to get value from that. There are the group of people that have a bunch of their data already moved to the cloud and organized and in a data lake so that they can actually access it and drive enterprise value. And then there’s folks that haven’t done that yet. And they find that they go and they do proof of concepts for AI and get things up and running quickly. And then they try to integrate into their enterprise data and they can’t do that. And so that is one of the big pushes that we see early from a lot of customers is that when they’ve already done much of their cloud migration or they’ve really on that path, it gives them a real leg up to actually driving value from any of the AI projects. And I think that’s one of the things that we’ve seen early on as companies start to shift. I think if you, because when you started early on, everybody did proof of concepts. Every single company out there did 100 proof of concepts to test and see what AI was capable of. But then if you didn’t really have your enterprise data, organized and labeled and set up in a cloud environment, it was actually, it’s actually really hard to get value out of those AI projects. And that’s where we see a lot of the companies sitting right now.

Sara Eisen: And who’s your biggest competitor on that front? Is it Microsoft?

Matt Garman: Well, from getting data in the cloud, I think that a lot of it is on premise still. And so many of the customers are struggling with legacy. I think as you think about AI, it’s the cloud providers are us and Microsoft and Google are largely the players in the space. But the biggest struggle right now for the customers that slow them down is just being in legacy on premise, by far, like that is the biggest thing that we work with customers against as opposed to any other competitors out there. I mean, I was fascinated talking to you about how much you’re doing with AI as an energy company.

Sara Eisen: I mean, you’ve got what? Tell us a little bit about what’s going on in Saudi Arabia when it comes to you utilizing AI to improve your business.

Amin Nasser: You know, AI is a transformative force for not only our industry, for all industries. The energy industry in particular receive a lot of data daily from seismic, from modeling, from all type of processing that we are running in our facilities. And we receive almost 10 billion data points daily. But you mentioned something about 74% could not scale it up because you need first, you need the infrastructure. You need to make sure that you have the talents that know how to scale it up and utilize all of that data. And if you don’t have the quality of data, which we do over 90 years, today, seismic, for example, it used to take us months to run seismic. Today, we moved to two weeks, two days, and you are going to hours in terms of running seismic that took a long time. That allows you to discover more and find more oil and gas. Productivity of wells used to run logs and all of these things, but in areas where we cannot run all of these logs to see the subsurface in terms of permeability, porosity, now you can use AI to predict machine learning in terms of predicting failures and all of it. We can predict now when, because there is a lot of data that comes, AI is helping us to transform the way we are managing all of these equipments and reducing the downtime, increasing our efficiency, reducing our carbon footprint. And this is transformative, as I said, for our industry. And as we go along in terms of using it to manage, for example, we’re looking at the grid. You need to stabilize the grid. We are investing heavily in renewable in the kingdom, but you need at a certain point to manage the grid because there is an intermittent production. You have energy coming for 12 hours. You will stop it. Gas will be pushed back. Today, we use mechanical ways to manage the load. And you’re talking about loops in the pipelines, and these are in billions of dollars. With AI, you will be able to manage the load and end up reducing the cost that you require mechanically today to deliver the energy in a stable manner. Reliability. Reliability is a big important item for us. You’re talking about, as an industry, we spend almost one trillion dollars. If we are able to reduce carbon, today we use pressure, temperature, and vibration to look at equipments and predict when it will fail. We are looking now at using process data, and using this process data, combine it with pressure, temperature, and predict much better in failures of these equipments. Similarly, in corrosion and other things, there is a lot of things that you can do, but you need to build the use cases. And this is where you need to scale up, and you need the talent, the people that are AI-enabled, and we have 6,000 of them in Saudi Aramco today. These people build the use cases. Today, I have 430 use cases. Each use case is a project on its own, and it end up with huge benefits. You cannot use the AI expert alone. You need to use the AI expert combined with the subject matter expert that are AI-enabled. We’re gonna come back to the labor issue and how you upscale your workforce, which I know you have done,

Sara Eisen: because clearly 6,000 people. But just on the use cases, a lot of what you just talked about was cost savings and productivity, which, Ramon, I know you talk a lot about. Every earnings quarter, we talk about how much productivity savings you are seeing. So to what extent is that coming from Gen AI, and how you use it in the supply chain? To what Matt was saying, I think we’ve been at transforming our infrastructure for now four years.

Ramon Laguarta: So we’re ready to capture the value of AI across the end-to-end of the company. Now, if you think about PepsiCo, you think about PepsiCo, but maybe three verticals, agriculture, manufacturing, logistics, selling, and then consumer-facing, we can create value in all of those elements. And there is a couple of areas we’re going after. First is making our people more capable. We have 330,000 people across the world. We can make our front line much more capable, much more intelligent, which makes the company much more agile, making decisions much more locally relevant. It’s a huge transformation. The second one is we’re connecting the company end-to-end. This is a big gap that we have across the world. We were not sharing information in an agile way. We’re not giving our people access to the person, somebody in Turkey, that know what Peru was doing, or what Vietnam was doing. Now we have shared platforms where our associates can become much more productive. And the third element for us is simulation. We can simulate the future in a way that we could not in the past. That is impacting our R&D developers, how they can create new products much faster. So end-to-end, our agronomist, our front line salesman, throughout the company, we’re capturing value. It’s not only cost, Sarah. I think there’s a lot of growth. There’s a lot of understanding consumers better. There’s a lot of serving consumer needs much better. There is making the life of our associates much easier. And it’s making them intelligent solution makers versus just kind of action oriented. So I think there’s a massive transformation in terms of intelligence of the company, connectivity of the company, growth opportunities. It’s just massive, the opportunity. The work is hard. It’s infrastructure, it’s people, it’s different financial metrics. There’s a lot of changes you have to make at scale to make this happen. So can Gen AI tell us right now what’ll be the next big snacking hit? Gen AI can help us understand consumers better. And Gen AI can help our R&D developers and our marketeers come up with alternative simulations to say, okay, this is gonna be probably

Sara Eisen: a preferred convenient food for that particular occasion or a much more functional beverage for that particular opportunity. Paul, one of the most exciting applications, I think, and opportunities is in healthcare and drug discovery. But we haven’t really seen that happen yet, have we?

Paul Hudson: Well, it takes about 12 to 15 years to discover and develop and bring forward a new drug. And large language models are what, two, three years at least in practical application. We spend about two to 3 billion to develop and discover and develop that medicine over the same time. Roughly a third of the discovery effort now, we’re talking the drugs that will launch in 10 years, are being validated. validated by AI, so our probability of success is so much higher. People forget that there’s so many diseases that are undruggable or considered undruggable, and so many diseases that are unmet in terms of need and treatment that we’re using AI to genetically validate targets, to make sure we’re more likely to bring forward a medicine a decade later, to validate the mechanistic approach, particularly with small molecules. We need to make sure that every dollar is well spent in terms of the investment and increase our probability of success, and you won’t see that in real terms. I think we’re still waiting for the first medicine that’s done the entire journey. You know, at Sanofi, it’s been really fascinating for us because, yes, we genetically validate everything and target selection is more than 90% accurate now, but we spent an awful lot of time putting together our data, making sure that we have end-to-end, we’ve completed that. We’re running validated agents now at scale with more than 20,000 people using them on a daily basis. It could be from as simple as inventory management, where the agent is correlating all the data real-time and letting us know on our phone, almost like Instagram notifications where we’re at. We’re doing very much the same in terms of forecasting accuracy. We’re more than 99% accurate in terms of our use cases, and while most big companies are still piloting, dabbling, most CEOs that are a little bit apprehensive, think more cyber than opportunity, are running lots of proof-of-concepts because it’s safer, right, just to do that for a few years before retirement. It’s such a big opportunity that the point we’re at at the moment for us, we’ve decided that everybody will be in the same place in the end in healthcare, so being first and the broadest, which is where we believe we are right now, gives us a huge opportunity to beat others. You know, you have to imagine, and I think it was said earlier, the jobs that are at risk are the jobs where the human isn’t interested in AI. doesn’t beat human plus AI. It takes the role of somebody who’s not interested in AI. And we’ve managed to get the vast majority of our people now being enabled, much like using Waze, for example, should I turn left, should I turn right, which is the fastest, on a daily basis. Great news for patients, ultimately, incredible news for us. And maybe lastly, we used to, when I joined the company, spend a week doing a budget process. Now we can spend about three hours, ultimately, because AI lets us know where we’re heading, we use it as a base case, we adjust resources from there, and we’re redeploying up to a billion dollars real time, enabled by an agent, to allow us to do that. So, exciting times in healthcare. I think in healthcare in general, people are a bit slow. I think in the race of turtles, we’re the lead turtle.

Julie Sweet: That’s a good place to be, better be in the lead. Julie, you work with probably a lot of these folks, all sorts of different industries, as you provide IT services around the world, including how these companies are upgrading, right, to gen AI, what do you see as the best, most exciting use cases? Well, first of all, I want to underscore what you said when you opened, that it is here now, and I do work with each of the companies here, and partner with Matt, and I will tell you that for companies with CEOs who are like the ones in this panel, both visionary and deep in the execution, AI is being used at scale now, and you heard these examples, and you need both, right, because you have to embrace the vision, but also, I mean, this was a master class just now, in terms of all the things that companies are facing and how to scale. I’d say there’s three big trends that we can also think about, kind of if we lift it up for a moment. One is on growth, which Ramon accurately said, the business case for AI is always better if you have growth and efficiencies, right? So hyper-personalization. which is taking old traditional things where you personalized it using customer segmentation and prior data and instead being able to real-time personalize much more to actually preferences. So for example, Radisson Hotels now when someone goes on and says they want to go ski, they want to go to a Swiss Chalet, one of their hotels in the Alps, they can actually show different pictures because they know if someone goes and skis or if someone goes and goes to the spa. And so in real time when they’re showing that hotel they can now put different pictures to do that. That is true across industries now and we’re seeing it in banking, we’re seeing it in retail, we’re seeing it increasingly in health. So that is an important area to focus on. The second big one is physical AI and that was what Amin was touching on and that of course is growth and efficiency right? Downtime affects your your revenue but we’re seeing beyond the energy category, we’re seeing this in manufacturing or the global tire manufacturer. We’re used to literally if there was a defect on the line it would take weeks to investigate the cause and it’s now come down to hours which makes a huge difference if that line is stopped for a week or two weeks versus a few hours. And so we just announced some work we’re doing with Kion and NVIDIA using technology including Matt’s technology around warehousing solutions and cutting manual labor which is really important because there’s a huge talent shortage for people to work in these warehouses. So physically AI is important and then the last trend which I think is also super exciting is that it’s expensive and it takes a lot of time to upgrade your technology and actually as Matt was saying earlier a lot of the challenge we have today is people are on mainframes right? They’re still on prem and Gen AI is now helping us be able to get off the mainframe faster and that’s That’s a lot of work that Matt and I are doing with many clients. So the use of Gen AI to itself accelerates the movement to the future. So those are just three things that I think are just good to keep in mind as you’re plotting, you know, how do you embrace this and move quickly. Another theme that I’m picking up from everybody is just the collaboration and partnerships. And we see this every day. Like don’t do you have how many here are partnered with NVIDIA in some way for chips? Like everyone. Everyone. We are. Yeah. Kind of have to do that. But you’re also partnered, I know, with Grok and others. Yes. Of course. We work with everybody. How, Julie, do you think that evolves the partnership landscape? Well, I think, you know, first of all, innovation only happens when through partnerships, right? Any company says that they can innovate only internally is almost by definition not innovating. And especially as you think about the things that will unlock. Like, for example, the work that we helped do for the World Economic Forum that you mentioned earlier on industries. That is bringing together all the industries, all the companies in industries to work together to talk about what’s the, you know, what is the unlock that AI does and help everybody move faster. And I think that’s important as you think about talent, for example. And you’re seeing a lot of this actually in Saudi Arabia. They’re like just a, you know, a leader in bringing together companies to how do you think about upskilling and not trying to do it only within your own company, but through partnerships both with government and the private sector. And so many of the, both the opportunities and challenges ahead are going to come through collaborations across companies in communities and with governments and educational institutions. Matt, I’m curious how you approach the jobs issue internally at Amazon and at AWS specifically and keep your team or get your team to become Gen AI experts. Sure, sure. I guess my mic wasn’t working so I got double mics now.

Matt Garman: Yeah, well, look, for us it wasn’t actually that hard. I think we were fortunate to have some of the best technology experts in the world, and so they jumped in and in many ways were driving a bunch of the Gen AI technology. So getting that set of the people to dive into the technology wasn’t really that hard. It’s like kids in a candy store for them really. They’re quite excited to dive in, and we have some of the best science experts in the world, some of the best technology experts in the world, and so that part of our business I think was quite easy. Then the rest of the business, we also know that our customers are all looking to us to be the experts, and so it’s always incumbent on us to be at the leading front of that. So whether that’s our sales folks that are at the head in front of customers, they’re often asking us, how do they go implement these technologies? So we spent a lot of time making sure that fortunately we have that expertise in-house, that we’re also training the rest of our team all of the time to be at the leading edge of technology, and so we’ve built that muscle over the last 20 years that we’ve been building AWS. Whether it’s new technologies across the board, we’ve built that mechanism to do training. But it’s also not just us as it turns out, we also our customers look to us to train them as well, and so we’ve actually built up a whole organization that’s focused on training customers, and it’s to free training. We train millions and millions of customers for free on AI, because we feel like they’re not going to get the most out of technology, they’re not going to get the most out of what we do, if they don’t, and it’s actually not how you use the technology, that’s not actually the training. The hardest bit is getting people’s heads around what’s possible, and I think not even what’s possible now, but what’s possible 24 months from now, or 48 months, or two, three, four years out, because I think people have a hard enough time realizing what’s possible today, but as you’re implementing it 12 months later, the technology is going to be much further along, and so you really have to think about what’s going to be possible and unlocking and it really is, it’s unlike a technology that we’ve had before, maybe going back to the Internet or something like it, it really is going to change every single job in every single industry in ways that I think it’s really hard for people’s heads to get around, and so it’s not just doing something. 5% better, it’s doing something 1,000% better or more. And so that’s orders of magnitude. It really takes time to spend with customers and partners like Julia and others, where we have to sit down and help them understand, how can their business actually get that much more efficient, that much more effective? How can we change customer experiences that way? And so internally, we had to get our teams to think bigger like that, too, and really push them to think what’s possible out there. But it’s exciting. That’s a pretty fun place to work. And I think as soon as your employees get their heads around that, OK, it’s not that my job’s going away, or it’s not that this is going to save us 5% of the cost, but we’re going to fundamentally be able to go deliver things that weren’t possible before. If you at least have the right sets of employees, most people get pretty excited about that opportunity. Paul, what were you going to say?

Paul Hudson: Yeah, I think it’s really an important point. I just want to connect the last piece to partnerships. So of course, we work with Oaken in drug development. We work with Formation Bio and OpenAI in trying to get us through to even submissions to the regulator. Incredible, great partnerships. And Ailey Labs on Snackable AI, how we nudge the average person in the company to make a better decision. Important thing that people- Sandbox AQ, too, right? Sandbox AQ, also. And Sandbox, and we’ll be at a session with them later today. And the reality is also the pace, and that you can really get better pacing if you partner externally. Their motivation and their incentive is different. And I think it can really pull, particularly when you’re adopting new technology. And I talked to a lot of my peers about the adoption of AI through partnerships. And people are still moving too slow, of course. But part of it is it needs to be sponsored from the top, because it’s still unknown to so many people and the scale of it. And so many business processes have to be changed. And if you’re not going to do that, it’s a sort of nice addition to the tools that you have. If you really want to change this, I’ll give you an example. We spend around 8 billion euros a year on research and development. And the key decision-making body, our drug development committee, begins its session with a validated agent recommending whether a drug should pass through a toll gate, whether it goes to phase three or goes to phase two. And we do that because it’s very sobering, because the agent doesn’t have a career at stake. The agent isn’t wedded to the project for the last 10 years. The agent is dispassionately. saying don’t go forward, or go forward faster, or go forward and remember these things. And we’re not used to having somebody without a career at stake in the room at a senior level. And we really have to change how we think about the adoption of AI and how it changes behavior, and to the points made earlier, how it helps people double down on insights rather than analysis to see the roles change for productivity gains and growth, but not getting obsessed about, am I gonna be around?

Sara Eisen: That’s really not the question. Right, I feel like we’ve moved on from the job-killing debate. I mean, how do you train your people? You mentioned thousands of them are now AI experts. Are those engineers? Who are they, and how do you do it? Most of them, I would say, majority are engineers that are trained in the field, that they are the subject matter expert.

Amin Nasser: And you talked about collaboration. We need the AI expert from Vidya, Microsoft, Google, and all of these. But we need first the subject matter expert who understand AI, who will create the opportunities. Because at the end of the day, you need to see what is the size of the opportunity. I’ll just give you another example. Corrosion, which impact not only our industry, every industry around the world, use corrosion inhibitor, use intimacy craving, use coupons, whatever. But it’s $3 trillion annually. Cost. Cost. So this is the opportunity. Now, this is a use case, and to say, okay, how do you go around creating value by reducing the corrosion? How do you use your process data, other than what I mentioned, the corrosion inhibitor, the intimacy craving, in terms of identifying where the corrosion is gonna happen, and how can you reduce it? That’s a big thing. But for Aramco, we are, the way we are training our people, we are using our large language model, Aramco MetaBrain. It use about 70 billion parameters. We are also developing a trillion parameter, large language. model. This is for the use of our people, using our data trained on these things. Do other energy companies do that? I don’t know. But my point, this is important and critical. The other thing you need in order to capitalize on AI, and everybody talks about scaling, you need the infrastructure. And the infrastructure, you need to build it over so many years, because it’s not only the fiber optics, the sensors, the computer capacity, I would say the language models, it is also the talents, the people who understand all of these things. So what we are doing in order to further our collaboration with the tech company and capitalize on all of these advances in AI is taking these use cases, going to the high tech companies, the one I mentioned, and show them what is the opportunity and how we can commercialize these things together. Because others want to scale up, but they need to see the value. Where is the value? If I’m going to spend a billion dollars to upgrade my infrastructure, and this is a low figure, by the way, it’s tens of billions of dollars for the company size of Aramco, we’re talking about infrastructure. That needs to see where is the opportunity exists, and how can I capitalize on AI and move. We created Aramco Digital Company, separate from Aramco. This is important and critical, because they do work differently, they need their different base structures, they need different training and development. So we created that in 2023, and it is helping us to digitalize and move forward. But creating the value, seeing the opportunity, putting a target, and I agree, it should not be a one year or two year or a 10 year or 15 year target to say this excites people. The talent you have, if you give them an ambitious target, they will work for it. And then you will see, because I mentioned the reliability, how much we spend as an industry or reliability, a trillion dollars. Today, around going to a 99.8% reliability, so what is next? How can you improve it further, capitalizing on the advancement on AI? And the corrosion example, you mentioned it’s $3 trillion cost. So, Gen-AI allows you to save what? Today, we use, as I said, corrosion inhibitor, instruments in craving and ultra-thickness measurements. I don’t want to get too technical. But now, with AI, you can use process data. Because sometimes, with my fluid, I send a slug of water. That will have an impact on the pipe as a certain location. That process data, being processed simultaneously with the measurements that we take, it will allow us to be more precise in measuring corrosion, identifying the location, predicting what will happen in these instruments and pipes and all of these things. Because all of these things does not exist today. The AI expert cannot generate and see the value. Only the subject matter expert who are trained, who understand and put the size opportunity and work with the specialist can really commercialize all of these technologies. And this is what we are doing. I like how deep we’re going on corrosion and energy production.

Sara Eisen: What about, you know, we’ve been talking a lot about company-specific, industry-specific use cases. I wonder if maybe we can talk a little bit about societal issues and whether we can solve things like energy security or, I don’t know, cancer or Ramon, hunger.

Ramon Laguarta: Maybe I can add a couple of elements on the previous conversations. There’s two things also we need to deal as we transform the companies digitally. One is the, we’re gonna have to hire new job families that we then have in the company. Like, if you think about data scientists, data engineers, technology people, we need to avoid that the company divides between. in the digital and the analogs, and that is a big change management and culture transformation that we need to own as leaders. Obviously, there is training, there’s digital academies, we’re all doing that at scale where we started, but it’s complex from the change management point of view. And, you know, going now maybe I can join to your question. Yeah, now solve. Think about, you know, our agronomist and how our agronomist now have much more intelligence and how they can work with our farmers in improving yields in, you know, in potato or corn or oats or whatever, they can reduce the use of water, they can reduce the use of fertilizers whilst improving the livelihood of the farmers. So those are ecosystems that we can create now that create value for the farmer. The same for transportation. If you think about the way we’re doing routing today before compared to what, how we were doing in the past is totally different. So we’re reducing the number of miles, we’re reducing the consumption of fuel, we’re consumption, you know, the impact on carbon. When we talk about nutrition and future portfolio, I can see how we can personalize much more our portfolio to specific needs of the consumer. Think about hydration, for example, how we are now able to understand hydration at a much more granular level and therefore Gatorade or Propel or all our brands can give consumers many more options, not only in the form of liquid, but in the form of powders that helps with the functionality of that particular product. So as you have smart manufacturing, much more agile, much more flexible ways of delivery and much more consumer insights, we’ll be able to solve some of the consumer needs around food or specific dietary needs or functional needs. So I see agriculture transforming at speed versus before and we’re gonna enable that with partnerships. I see transportation changing. And that means a lot of resources will be freed up, much less impact on the world. And I see nutrition, I see functionality for consumers also evolving into much more personalized and much more, you know, targeted solutions, depending on our preference or our particular physiological needs. So those are big areas where AI can help us, where, you know, companies like ours, our know-how with our reach and scale, we can help transform. Julie, do you work with governments and can governments play a bigger role here in utilizing AI to solve some of our societal problems? Look, there’s some governments that are doing that already.

Julie Sweet: I mean, in the UK, we worked with one of the health and public services, health services areas of the government that was struggling to serve people because they didn’t have enough people. And so people would write in having food insecurity and needing services, and it might take six weeks to get back to them, the most vulnerable people, simply because there weren’t people to hire. Today, using Gen AI, that’s 24 hours, right? So there’s a lot of opportunity from supporting, you know, research like into drugs and solving energy challenges to, you know, food challenges. But there’s also a real change in the way that governments can serve citizens. So there’s a lot of opportunity with government. But I think one of the things that as we kind of walk away to think about what has to be different, whether you’re a government or a company, is I would say, you know, thinking about reinventing HR, because we talked about talent in that, but actually, what this requires is a completely different thinking about how people work and what skills they have. In most cases, AI isn’t replacing people, it’s replacing tasks or parts of the processes, which means that in order to upskill people, you have to understand what skills do they have to begin with. And so we at Accenture moved to skills-based HR over five years ago, I have a database of almost 800,000 people and their skills, and so we’re systematically redefining the skills needed at Accenture, who needs Gen AI, what kinds of technology, but also as we are replacing some of the things that they’re doing with Gen AI, we’re able to identify who could be up-skilled. So you have to have skills-based HR, companies and countries. The second thing is you have to have HR that now adapts to how do you have trained people to work alongside, so releasing a blog today that shares our story in marketing and talks about what it was like, so we build our agents so you can give feedback, just like you would give feedback to someone you were supervising, or feedback to a colleague. That suddenly makes the AI very real to people and they start to see how it can really help them. That’s a totally different mindset. We didn’t have a playbook that says, how do we take people who are now gonna use AI fundamentally in their job, and make this to be something that they really care about? The last piece is change management. The kinds of change management that you have to do here require you to understand the technology, understand the people. In most cases, companies use change management to train on a new system, or they use whatever partner they happen to be using and we use that. This is a skillset now that has to be at the center of your company because it’s a continuous reinvention. The amount of opportunity that AI is gonna change in countries and companies, it’s gonna take decades, and so it’s not a one and done. Building change management so it’s not viewed as being something off on the side is really important, and I share that because at the end of the day, the technology will only be effective if it’s trusted and it’s adopted, and you can then create new opportunities for your people. I think we have to think very, very differently about the incredible departments of HR and the role that they need to play and how they have to reinvent themselves.

Sara Eisen: It brings up a question, which is, what is the next technological leap in AI, Matt? I mean, first of all, there’s been such rapid sort of progress and development. There are questions whether Gen AI innovation is hitting a wall, or if we’re moving into AGI, Artificial General Intelligence, where do you think it’s going?

Matt Garman: Yeah, and just, I just one thing to add to what Julie said, is just, it is super interesting when you think about the skills that you’re teaching your teams to do. And it is, when you can find a particular task that you can find that is, that AI is particularly good at doing, it can have massive impacts. And that’s, and actually, I think that’s one of the things to highlight for everyone, which is if you get one of those use cases, really leaning into a particular use case that takes work and toil off of the rest of your team, that unblocks them. One example we had of this is, we built a product that automatically upgraded Java versions. And you talk to software development teams, and that is a task that not one person is super excited about doing. No one’s like, you know what I really love is just upgrading Java. It’s boring, but it makes sure you have all the latest security, it gives you performance benefits, et cetera. We built an AI system called Q-Transform that just does that for you. And so we decided to eat our own dog food. And we said, okay, we’re gonna do that across Amazon. And we had a team of five people that the team’s internally estimated that to upgrade all of our internal systems was gonna take somewhere around 4,000 plus man years to upgrade. We had a team of five people that did it in a couple of months across. And so we freed up 4,000 person years of effort to go now build features and capabilities for all of our customers by taking away work that no one wanted to do in the first place. And that is like, it’s a very good thing where you just lean in. You just say this is a capability that we can go and roll out. And it’s a good, but it’s a highlighting when you find that thing and it has a massive impact on. in your company. Now ask about what’s next from that. You’re right that the technology is moving at an incredible rate. I don’t know that we’ve seen a technology progress as fast as it has. And I think one of the challenges of that is it’s hard for everyone to keep up. You know, I think everybody externally loves to point that like, oh, maybe AI is hitting a wall. No, it’s not. And it’s just because if you look at any of how these technologies evolve over time, oftentimes you’ll make a lot of progress on one path and then that path will run out of steam. But then you just find a different path and you continue to go on from there. And so the one path that probably is out of steam is it’s unlikely that we just throw more data, like more tokens at the exact same compute cluster and for the models to get smarter. That, you know, they’ve already trained on the whole corpus of data on the internet. There’s still some specific areas where I think we can actually train more on particularly like IoT data and world data and other things like that. But for the most part, that path has gone. However, I think if you look at all of the latest models, they’re doing things like reasoning loops and other things like that. And you find out that actually just asking the models to do the same, it’s not quite right, but it’s roughly it’s, if you ask the models to just actually think about the answer a couple of times, like get the answer, try again, try again, try again, they actually come up with better answers and they can actually do more. And so if you build a reasoning loop where they actually can then spread off and it turns out that they’re very good at doing lots of things in parallel. And then they take their own outputs and use those as inputs to actually go and solve the problem again, they can actually get much better. And so that’s where a lot of the models today are actually doing this kind of internal reasoning where, and I think we’ll see some of these models go, which is not, you know, where a lot of us have gotten used to this kind of ask it a question, it gives you an answer back. I think you’ll increasingly see, and there’s still some technology pieces that have to happen for this to really work well. But it’s more like you’re gonna ask these models to go do something and they’ll come back a day later or a week later. later. And it’s more like, if you have a real employee, this is what happens. You don’t ask your employees to go do work and then say, what’s the answer? That’s not how it works. They go and think for a while, and they iterate, and then they talk to some other people. And actually, that is how some of the models are evolving, where they’ll actually talk to each other. They’ll get inputs from other specific expert models. They’ll bring them back. They iterate for a while. And so I think that that’s where you’ll start to see these much more capable models be able to do interesting and very novel work items that aren’t possible today, where we expect the answer back in five seconds.

Sara Eisen: Paul, I know you wanted to say something.

Paul Hudson: Then we’re going to get to the call to the action. Yeah, I know. And maybe I’ll do a little bit of both, if that’s all right, to help you with that. You know, the radical transparency of data that you get with AI end to end, and that we have in Sanofi, you know, in big corporations, there’s a lot of managers and senior managers who’ve made careers out of polishing slide decks to make sure that they can communicate upwards what we should think about a situation. And with the radical transparency, we’ve sort of blown that away a little bit. People can turn bad news into good news with the right PowerPoint presentation. And we’re trying to really avoid that. The biggest mistake that people, and we can unleash resources and discover new medicines. The biggest mistake CEOs make, I believe, and you may have an opinion, is we’re a different generation. And we delegate AI to CDOs. And it’s the biggest mistake you can make. And I have a great CDO, but the nature of the change and the courage needed to change business process at scale to better optimize resources and to go chase miracles for us means that if you delegate to a CDO, you have automatically lost. You have great AI that nobody uses. And it’s a real watch out, I think, for most of the CDOs and my peers, most CEOs, because everybody thinks that’s where it should go for safety and responsible AI. We’re in there. But sadly, at the intersection, say, between the business and manufacturing, the CDO is not the winner. And when you’re trying to change business process, that’s where the real win is. And that’s why I have to lead it myself. And it’s not just about me, it’s just I’m the arbitrator to make sure we get the adoption that we need. It’s the over-delegated AI. And if I have a call to action, don’t over-delegate, know the value, radical transparency, go end to end, and the insights you get and the resource reallocation opportunity is so huge that it can absolutely change an organization.

Sara Eisen: Well, I’m glad you made it into a call to action because this is the World Economic Forum and that’s what we do. So the question is, one thing that you can do to improve Gen-AI and its sustainability and its transformation. And Amin, maybe this gets into the challenge as well. Yeah, I think everybody talks about the transition and think using AI and piloting, what can we do?

Amin Nasser: For example, we cannot use hydrogen to scale it up today because it is costly. And capsule and storage, capturing the CO2, it’s not the transporting of the CO2 injection, is also very high cost. AI, using AI to reduce the cost over the long term and make the transition at an affordable cost, that is important. But the most important thing I would need to say is about we need technology equity and we need technology, otherwise we’ll end up with technology poverty. Today, access to technology is important, important for the West and also for the global South. We need to make sure they have access to technology because the wealth gap is big today. And it will be much bigger if they don’t have access to technology. And that’s where it is important and critical. Add to that cyber security because with AI and cyber security issues, if they don’t have access to technology and capability with quantum computing, all the codes will be decoded for their strategy. strategic and important assets, utility, electricity, and that is not going to be safe and reliable for these countries to run their operations. So there has to be a technology equity, otherwise we’ll end up with technology poverty and we’ll increase the wealth gap. Yeah, it does feel like the have and have-nots is going to become a big issue. Julie, your call to action. Build trust through operationalizing responsible

Julie Sweet: AI. Our research says that only about 2% of companies worldwide have robust programs in place to put the guardrails around monitoring and policies. And so we’ve really got to move and that number hasn’t moved in the last year. And so building trust by treating AI like other programs, whether you’re protecting data or you’re protecting against anti-corruption or those things, it’s going to be absolutely critical and we’re behind in operationalizing

Sara Eisen: it. And that’s key to scaling. Ramon?

Ramon Laguarta: Yeah. I mean, on top of what everybody said, I see or we see AI as a way of empowering our frontline. So we want to, let’s make sure that we empower people, that we don’t leave them behind in this digital transformation. We educate them, we help them capture the value, what is ahead of them. I think it’s a massive opportunity to make the organization much more frontline, much more closer to consumers and elevating people’s value and capturing higher wages and much more value creation at the lower end of the organization. And that impacts society in a big way. Matt, final word.

Matt Garman: Yeah. Just remembering that your data is the differentiator for you. We’re spending billions of dollars to make custom processors that are faster and lower cost and better performing. There’s new models that are available every day that are going to be able to do incredible new things, but it’s your data applied to those that really makes the difference. for your companies, and so your data and your IP, it’s really important to get that into a secure environment where you can take advantage of it, because that’s how you actually drive value

Sara Eisen: for your organization. And I’ll add one, which is, for all of you and everybody who is working on this, to come on CNBC more and talk about it, because there’s a lot of misinformation and trust issues and they’re going to steal my jobs and move on, kind of thing, and it’s moving fast, and I think from all of you up here, we should all be pretty optimistic about it. Yes. So thank you very much, all of you, for weighing in. Thank you, guys. Thank you. Thank you. Thank you. Thank you. Thank you.

Agreement Points

AI is already being used at scale and transforming industries

speakers

– Julie Sweet
– Amin Nasser
– Ramon Laguarta
– Paul Hudson

arguments

AI is here now and being used at scale by leading companies

AI is transforming industries like energy through data analysis and predictive capabilities

AI is used to improve agricultural yields, transportation efficiency, and nutrition personalization

AI assists in drug discovery and development, increasing probability of success

summary

Multiple speakers agree that AI is not a future technology, but is already being implemented at scale across various industries, leading to significant improvements in efficiency, decision-making, and innovation.

Importance of workforce upskilling and empowerment for AI adoption

speakers

– Julie Sweet
– Amin Nasser
– Ramon Laguarta

arguments

HR departments need to be reinvented for skills-based hiring and continuous change management

Companies need to upskill workforce and create AI-enabled subject matter experts

Frontline workers can be empowered through AI to create more value

summary

Speakers emphasize the need for companies to invest in upskilling their workforce and adapting HR practices to effectively utilize AI, viewing it as a tool for workforce enhancement rather than replacement.

Similar Viewpoints

Both speakers emphasize the importance of proper infrastructure and skilled workforce for successful AI implementation and value capture.

speakers

– Matt Garman
– Amin Nasser

arguments

Cloud migration and organized data are key for capturing AI value

Companies need to upskill workforce and create AI-enabled subject matter experts

Both speakers highlight the importance of leadership and responsible practices in ensuring widespread adoption and integration of AI across organizations.

speakers

– Julie Sweet
– Paul Hudson

arguments

Building trust through responsible AI practices is critical for scaling adoption

CEOs should lead AI initiatives rather than over-delegating to avoid adoption issues

Unexpected Consensus

AI as a tool for addressing global challenges

speakers

– Amin Nasser
– Ramon Laguarta

arguments

Technology equity is needed to prevent widening wealth gaps between nations

AI is used to improve agricultural yields, transportation efficiency, and nutrition personalization

explanation

Despite representing different industries (energy and food/beverage), both speakers see AI as a tool for addressing broader societal and global challenges, such as wealth inequality and food security.

Overall Assessment

Summary

The speakers generally agree on the current transformative impact of AI across industries, the importance of workforce upskilling, and the need for proper infrastructure and leadership for successful AI implementation.

Consensus level

High level of consensus among speakers, suggesting a shared understanding of AI’s current state and future potential across different sectors. This agreement implies a likely acceleration in AI adoption and integration across industries, with a focus on workforce development and responsible implementation practices.

Different Viewpoints

Approach to AI implementation and scaling

speakers

– Matt Garman
– Paul Hudson

arguments

Cloud migration and organized data are key for capturing AI value

CEOs should lead AI initiatives rather than over-delegating to avoid adoption issues

summary

Matt Garman emphasizes the importance of cloud migration and data organization for AI implementation, while Paul Hudson argues that CEO leadership is crucial for successful AI adoption and integration.

Unexpected Differences

Focus on global AI equity

speakers

– Amin Nasser
– Other speakers

arguments

Technology equity is needed to prevent widening wealth gaps between nations

explanation

While other speakers focused on industry-specific AI applications and challenges, Amin Nasser unexpectedly raised the issue of global technology equity, highlighting potential wealth disparities between nations due to unequal access to AI technology.

Overall Assessment

summary

The main areas of disagreement centered around approaches to AI implementation, scaling, and the focus of AI initiatives within organizations.

difference_level

The level of disagreement among speakers was relatively low, with most differences being in emphasis or approach rather than fundamental disagreements. This suggests a general consensus on the importance and potential of AI, with variations in implementation strategies based on industry-specific needs and organizational structures.

Partial Agreements

Both speakers agree on the importance of empowering workers with AI, but Amin Nasser focuses on creating AI-enabled subject matter experts, while Ramon Laguarta emphasizes empowering frontline workers.

speakers

– Amin Nasser
– Ramon Laguarta

arguments

Companies need to upskill workforce and create AI-enabled subject matter experts

Frontline workers can be empowered through AI to create more value

Similar Viewpoints

Both speakers emphasize the importance of proper infrastructure and skilled workforce for successful AI implementation and value capture.

speakers

– Matt Garman
– Amin Nasser

arguments

Cloud migration and organized data are key for capturing AI value

Companies need to upskill workforce and create AI-enabled subject matter experts

Both speakers highlight the importance of leadership and responsible practices in ensuring widespread adoption and integration of AI across organizations.

speakers

– Julie Sweet
– Paul Hudson

arguments

Building trust through responsible AI practices is critical for scaling adoption

CEOs should lead AI initiatives rather than over-delegating to avoid adoption issues

Key Takeaways

AI is already being used at scale by leading companies across industries

Organized data and cloud migration are crucial for capturing AI value

AI is enabling hyper-personalization, predictive capabilities, and efficiency gains across sectors

Workforce upskilling and rethinking HR practices are essential for AI adoption

Partnerships and collaboration are key for AI innovation and implementation

CEOs should lead AI initiatives rather than over-delegating to ensure adoption

Building trust through responsible AI practices is critical for scaling adoption

Resolutions and Action Items

Companies should focus on operationalizing responsible AI practices

Organizations need to empower frontline workers through AI adoption

Businesses should prioritize getting their data into secure environments to leverage AI

Leaders should communicate more about AI to address misinformation and build trust

Unresolved Issues

How to achieve technology equity and prevent widening wealth gaps between nations

Specific strategies for companies to overcome challenges in scaling AI adoption

Methods for effectively retraining and reskilling workers as AI replaces certain tasks

Long-term societal impacts of widespread AI adoption across industries

Suggested Compromises

None identified

74% of companies struggle to scale AI. And only 16% are prepared for AI-enabled reinvention.

speaker

Sara Eisen

reason

This statistic sets the stage for the entire discussion by highlighting the gap between AI potential and actual implementation at scale.

impact

It prompted the panelists to address the challenges of scaling AI and share their experiences, leading to a practical discussion on implementation rather than theoretical possibilities.

We receive almost 10 billion data points daily. But you mentioned something about 74% could not scale it up because you need first, you need the infrastructure. You need to make sure that you have the talents that know how to scale it up and utilize all of that data.

speaker

Amin Nasser

reason

This comment provides concrete insight into the scale of data involved and the key challenges of infrastructure and talent in scaling AI.

impact

It shifted the conversation to focus on the practical aspects of AI implementation, including infrastructure needs and talent development.

Gen AI can help us understand consumers better. And Gen AI can help our R&D developers and our marketeers come up with alternative simulations to say, okay, this is gonna be probably a preferred convenient food for that particular occasion or a much more functional beverage for that particular opportunity.

speaker

Ramon Laguarta

reason

This comment illustrates a specific, real-world application of AI in consumer goods, showing how it can drive innovation and product development.

impact

It broadened the discussion from technical aspects to practical business applications, encouraging other panelists to share industry-specific use cases.

Roughly a third of the discovery effort now, we’re talking the drugs that will launch in 10 years, are being validated by AI, so our probability of success is so much higher.

speaker

Paul Hudson

reason

This comment provides a concrete example of how AI is transforming a critical industry, highlighting its long-term impact on drug discovery.

impact

It introduced the concept of AI’s role in long-term processes and innovation, prompting discussion on AI’s transformative potential beyond immediate efficiencies.

The biggest mistake CEOs make, I believe, and you may have an opinion, is we’re a different generation. And we delegate AI to CDOs. And it’s the biggest mistake you can make.

speaker

Paul Hudson

reason

This comment challenges conventional wisdom about AI implementation in organizations, emphasizing the need for top-level engagement.

impact

It shifted the conversation towards organizational strategy and leadership in AI adoption, prompting reflection on how companies approach AI integration.

We need technology equity and we need technology, otherwise we’ll end up with technology poverty. Today, access to technology is important, important for the West and also for the global South.

speaker

Amin Nasser

reason

This comment broadens the discussion to global implications of AI, highlighting potential inequalities in technology access.

impact

It introduced a new dimension to the conversation, shifting focus from corporate implementation to societal and global impacts of AI adoption.

Overall Assessment

These key comments shaped the discussion by moving it from theoretical possibilities of AI to practical implementation challenges, industry-specific applications, and broader societal implications. The conversation evolved from technical aspects of scaling AI to strategic considerations for business leaders, and finally to global concerns about equitable access to AI technology. This progression provided a comprehensive view of AI’s current state and future potential across various sectors and scales.

How can AI be used to reduce costs and make energy transition more affordable?

speaker

Amin Nasser

explanation

This is important for scaling up technologies like hydrogen and carbon capture, which are currently too expensive for widespread adoption.

How can we ensure technology equity and prevent technology poverty, especially for the global South?

speaker

Amin Nasser

explanation

This is crucial to prevent widening wealth gaps and ensure all countries have access to AI capabilities and cybersecurity protections.

How can companies operationalize responsible AI and build trust?

speaker

Julie Sweet

explanation

Only 2% of companies have robust programs for AI governance, which is critical for scaling AI adoption safely and ethically.

How can AI be used to empower frontline workers and create value at lower levels of organizations?

speaker

Ramon Laguarta

explanation

This could have significant impacts on organizational effectiveness and societal benefits through higher wages and value creation.

How can companies best leverage their proprietary data to differentiate their AI applications?

speaker

Matt Garman

explanation

Proprietary data is a key differentiator for companies in applying AI, but needs to be properly secured and utilized.

What are the next technological leaps in AI beyond current large language models?

speaker

Sara Eisen

explanation

Understanding future developments in AI is crucial for companies to prepare and adapt their strategies.

How can companies effectively manage the cultural transformation required for AI adoption?

speaker

Julie Sweet

explanation

This involves rethinking HR practices, change management, and how employees work alongside AI.

What is the optimal leadership approach for AI initiatives within organizations?

speaker

Paul Hudson

explanation

There’s a debate about whether AI should be led by CEOs directly or delegated to Chief Digital Officers, which impacts adoption and effectiveness.