AI Changes Everything

Oracle Corporation

AI Changes Everything is an Oracle podcast that explores how business leaders, innovators, and practitioners are putting artificial intelligence to work across their organizations. Through conversations with experts shaping the future of business and technology, the series examines real-world AI strategies, emerging trends, and the opportunities and challenges organizations face as AI adoption accelerates. Each episode offers practical insights into how AI is changing the way people work, make decisions, serve customers, and drive innovation. Whether you're a business leader, technology professional, or AI enthusiast, you'll hear perspectives that help connect today's advancements to what's next. Explore the series at http://www.oracle.com/aichangeseverything

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  1. قبل ٤ أيام

    AI Changes Everything: Protecting IP with Mel Morris

    Explore how AI can accelerate research, uncover hidden connections across massive data sources, and help organizations protect intellectual property earlier in the innovation cycle. In this episode of AI Changes Everything see how increasing innovation velocity can help leaders move from ideas to commercial value faster at https://social.ora.cl/6008BDMPGC  In this episode of AI Changes Everything, Mel Morris CBE, serial entrepreneur, investor, and CEO at Corpora.ai, discusses how AI is changing the way organizations approach research, innovation, and intellectual property protection. Drawing on more than a decade of work building technology designed to analyze vast amounts of unstructured information, Morris explains how researchers can move beyond traditional search methods to discover connections across disciplines that might otherwise remain hidden. The conversation explores how AI can help teams define problems more effectively, identify unexpected sources of insight, and accelerate the path from research to innovation. Morris shares how Corpora.ai enables researchers to search across a broader universe of knowledge, uncovering relevant information from domains they may never think to explore. He explains why innovation velocity is becoming a critical competitive advantage and how earlier problem definition and solution generation can help organizations protect intellectual property before competitors reach the same conclusions. The discussion also examines sovereign AI, the United Kingdom's opportunity to strengthen innovation through AI-enabled research, and the challenges organizations face when turning ideas into commercial outcomes. Morris offers lessons from building and scaling successful businesses, including his experience helping grow King during the Candy Crush era, and shares why leaders often underestimate the importance of scale when pursuing growth. The episode concludes with a practical prompting technique, perspectives on responsible AI adoption, and a thoughtful discussion about areas where AI requires careful oversight, including cybersecurity, healthcare, medical innovation, and warfare. For business leaders, researchers, innovators, and technology decision-makers, this conversation provides valuable insight into how AI can help accelerate discovery, protect innovation, and create new opportunities for growth. ------------------------------------------- Episode Transcript: 00:00:00:00 - 00:00:15:04 Unknown The views expressed in this podcast are those of the individual speakers, and do not necessarily reflect the views or policies of their organizations or or its affiliates. 00:00:15:06 - 00:00:38:23 Unknown What if I could surface critical insights from sources you'd never think to explore? What if you could compress months of research into days? What if it could protect your IP from the outset? And what if it could unlock a diverse universe of knowledge for researchers? Imagine the impact that could have. 00:00:39:01 - 00:01:01:01 Unknown Hello everyone, and welcome to the AI Changes Everything video series. I'm joined today by Mel Morris, serial entrepreneur. Hope you don't mind me calling you back. Mel, investor and now CEO of corporate AI. Okay. Mel, if you don't mind, just to kick off. Just describe the journey that's led you to computer AI and what it is and what it stands for. 00:01:01:01 - 00:01:27:08 Unknown If you could please the journey. Well, it's been a long journey. Go commit to a decade. In September. So we began looking at how you can manage a massive corpus of information. At the same time, people are looking at developing AI, so it's quite an interesting journey. But our view was we wanted to be able to look at really huge sums of unstructured data. 00:01:27:10 - 00:01:50:21 Unknown Of course, the starting point. What is huge? We had designs on hundreds of petabytes and looking around to see what technologies existed, particularly in terms of database technologies, thinking about graphs and vector. No one had really ventured into anything like that scale. So, Neil, I think the key thing with this is that essentially it was all about scale. 00:01:50:23 - 00:02:12:15 Unknown So you spoken about the scale. This is someone out there who doesn't operate in the field of research. What's the problem you're solving and who are you solving it for typically. Research is about finding information, and it's also about connecting the dots. So the number of dots that you can connect is entirely depend on the size of the corpus you're looking at. 00:02:12:17 - 00:02:34:22 Unknown So we allow researchers to connect more dots across for more information than probably any other tool. Right. So today these researchers we have access to limited amounts of information, shall we say, in research. And suddenly you just open that up for them. What are the other sort of benefits that it has for them? If you think about typical research, think about universities higher education. 00:02:35:00 - 00:02:58:00 Unknown As people develop their skill set, they tend to narrow their focus. As you get further and further up the scale, that focus reduces, the depth, increases, but the focus reduces. So university projects typically have, if you want to draw a sort of a picture of a Venn diagram or vice of people there, and you can say, well, the this group of people may have different skill sets. 00:02:58:01 - 00:03:19:02 Unknown There's some overlap. There's lots of diversification. The same is true with data. So if you're looking at a corpus of information that's quite narrow, then you're going to find certain things out. But there might be things in relate to fields of science or even unrelated fields of science that could answer things that you're looking at. You could be looking at electrical process. 00:03:19:04 - 00:03:40:14 Unknown There might be something inside of fish that actually might be relevant to what you're doing, but you might not think to look there. With corpora, we connecting more of the dots across more of the domains easily. And will corpora understand the context of what is being asked and what's being researched. And go and find almost those adjacent pieces of information automatically on behalf of the researcher? 00:03:40:17 - 00:04:06:00 Unknown Yes. And he points to the interesting point, because, all the new tools, particularly if you look at, say, the transformer technologies that became ChatGPT, these sorts of toolsets lend themselves now to the fact we become a bit lazy. We say we're looking for this, and then we go a step further. We talk about where we think they're likely to find information that narrows down the scope. 00:04:06:02 - 00:04:23:18 Unknown So we have a saying that when using corpora. Brevity is king, right? Do not try and tell us where to look. Give us the challenge. Tell us the problem. Let us decide where all the information that might be relevant that exists. And then you can choose to decide which elements of that you want to follow. You want to use. 00:04:23:18 - 00:04:42:22 Unknown You want to ignore. This is a fundamental shift, right. Because I think what you're saying is where I was in the past, I had to understand my problem and know where to look and then go look for it. But now you're just saying, tell me the problem. Copy will figure out where to look. And there may be places where you haven't even considered and bring that information back to you. 00:04:42:22 - 00:05:00:10 Unknown Is that. That's exactly right. And I'll give you a simple example. So let's say I'm looking at a medical problem. So I might want to look at a new method of treating this form of cancer. Now I probably would look in PubMed. I would look at most of the medical journals. I probably wouldn't look inside SEC filings. 00:05:00:13 - 00:05:26:06 Unknown No. But the SEC filings might include really important information on an 8-K or some other document in a filing that relate to exactly the problem you're looking at. But if you didn't think to look there or you didn't know to look at that, you'd never see it. So you might waste time going to file a patent for your new medical invention, and then realize that the 8-K, the SEC filing, became a piece of prior art that would invalidate your patent. 00:05:26:07 - 00:05:54:08 Unknown Interesting. Wow. Okay. And what what is the process look like? You know, there is sort of an understanding the problem, but how does it talk me through? Sort of like the process of how this works. Well, we decided that part of the challenge was that people particular with tools like ChatGPT, the transformer technologies, tend to want to provide this almost so copious definition of what the problem is. 00:05:54:10 - 00:06:20:08 Unknown And in so doing, they're actually proffering part of the solution. At the same time. And that effectively now is going to narrow down again what you're trying to do. So with corpora we would advise starting with a simple statement. All right. The example the same would be something like the challenge the let's call it as well let's say the the harmful biological, physiological and systemic impacts of human dialysis. 00:06:20:10 - 00:06:40:01 Unknown That would be a perfect statement to start with corpora. You would then ask it to produce a problem definition, which would actually probably probably be with 20, 30 pages of all of the different things that might constitute those sorts of challenges. You would then get that problem definition. You can review that, you can add to it, you can change it. 00:06:40:03 - 00:06:57:01 Unknown Then you can take the problem definition and you can then say, okay, so that's that's a problem. But what's the solution? And you can literally say now produce a solution. And it will idea to solution that goes against that problem definition and gives you a chapter and verse about how it can be solved by look

  2. ٣٠ يوليو

    AI Changes Everything: Inside Oracle Red Bull Racing's AI Edge

    See how Oracle Red Bull Racing uses AI, simulation, software engineering, and data science to improve performance in one of the world's most competitive environments. In this episode of AI Changes Everything, see how technology, race strategy, and engineering innovation help teams make faster decisions and build competitive advantage at http://www.oracle.com/aichangeseverything In this episode of AI Changes Everything, Martin Galpin, head of technology at Oracle Red Bull Racing, explores how AI, simulation, software engineering, and data science contribute to performance in Formula One. Galpin explains how Oracle Red Bull Racing develops the specialized software it cannot buy elsewhere, creating tools that help engineers, strategists, and performance teams gain an advantage in a sport where every fraction of a second matters. The discussion examines how simulation and modeling have become essential to modern car development, especially as Formula One teams operate within strict constraints on testing, wind tunnel usage, and computational resources. The conversation explores how AI and machine learning support a wide range of activities across the organization, from information retrieval and engineering productivity to sensor modeling and vehicle performance analysis. Galpin shares how generative AI is changing software engineering, why the cost of creating code is rapidly declining, and why understanding, maintaining, and governing software remains a critical responsibility for engineering teams. The episode also looks at the role of AI in race strategy, where explainability, trust, and human judgment remain essential. Galpin discusses why high-pressure decision environments require engineers and strategists to understand the recommendations AI systems provide rather than simply accepting them at face value. He explains how Oracle Red Bull Racing evaluates new technologies, balances physical and data-driven modeling approaches, and focuses on measurable outcomes when adopting AI. Beyond technology, Galpin shares his perspective on the future of software engineering, the importance of domain expertise, and how AI is reshaping the skills organizations need to succeed. The discussion concludes with practical advice on working with AI, thoughts on the future of Formula One technology, and a reminder that AI is most effective when it acts as a force multiplier for talented people rather than a replacement for them. ----------------------------------------- Episode Transcript: 00:00:00:00 - 00:00:15:06 Unknown The views expressed in this podcast are those of the individual speakers, and do not necessarily reflect the views or policies of their organizations or iCal or its affiliates. 00:00:15:08 - 00:00:33:15 Unknown In Formula One, every decision and every second can define a championship, but when the stakes are at their highest and there's no margin for error, is technology alone enough? Or does human judgment remain the ultimate competitive advantage? 00:00:33:17 - 00:00:53:17 Unknown Hello, everyone. I'm here today with Martin Gallatin, head of technology at, Oracle Red Bull Racing. And we're gonna have a fantastic session, I'm sure. Probably one of the most exciting video series we've done. Let's get right into it. So, Martin, can you just talk about your role at Red bull? What is it that you and your team do? 00:00:53:19 - 00:01:27:11 Unknown Thanks for having me. So, yeah, I'm Martin, head of technology. Oracle Red Bull racing. We, a group of software engineers and data science, that work on, projects for, the entire campus. We work on aerodynamics, vehicle performance, race strategy, the, the simulators, it in the mix. And then we have, some machine learning data science projects that that look across all of those different functions. 00:01:27:13 - 00:01:50:19 Unknown The, the overall goal of what we do in the department is to build the software we can't buy from, from others. So software the differentiates ourselves from our competitors. So I mentioned and I realize there's a lot of things you can't go into because of, you know, company confidentiality. But I mentioned earlier what your team does is that sort of that secret sauce, that extra thing that allows you to go a little bit further. 00:01:51:10 - 00:02:15:19 Unknown Can you talk a little bit about what the role of technology is in things like, developing brake strategy or modeling the car or just getting that little extra oomph? I think formula one is a a unique environment compared to most in as far as I think, simulation modeling data has been part of how we operate for decades. 00:02:16:13 - 00:02:49:10 Unknown I think Oracle Red Bull Racing were a lot quicker to recognize the value of software and modeling on driving confidence and I think the rest of the grid is now catching up to that. I think I see driving a lot of that, but I think fundamentally in Formula One with the restriction that we have now for testing at the track for the wind tunnel, and the CFD, fundamentally, most of your car development relies on the tools that you have available to you. 00:02:50:00 - 00:03:11:02 Unknown So it is pretty fundamental in designing, a winning racecar these days. And what was it about the Oracle Red Bull Racing that kind of made you have that focus on software engineering, where perhaps some of your competition? Well, was that a design ethos from the start when the team was set up or was that all down to you and your team? 00:03:11:04 - 00:03:42:16 Unknown No, I mean, I've been, Oracle Red Bull Racing for over ten years now. And so, I have been a long time, but largely I think the, the cultural, the philosophy and the investment in and simulation modeling, was a lot earlier than that. I think the, the, I guess the easiest way to it to think about it is the, the leadership at Red bull from a, from a, from the technical side comes from a very, modeling, heavy background. 00:03:42:19 - 00:04:16:15 Unknown And I think that as a culture as approaching problems in a, in a computational way, the when you go back 15, 20 years was actually, was actually quite novel. Whereas now we've accumulated 20 years of experience in the problems where maybe others have five, ten, and the tools that we have in the foundations that we, we've laid over the years, I think, a lot of the continuity of success we've had, over the last decade and it's clearly showing in the results. 00:04:16:17 - 00:04:34:09 Unknown Now, Martin, you spoke just a few moments ago about some of the limitations when it comes to things like testing and simulation, which was a surprise to me because, you know, the impression I have is that you're pretty much unconstrained. But that's not the case, is it? Can you just give some color around what these limitations are? Yeah. 00:04:34:09 - 00:05:03:11 Unknown I mean, there was a time when we were unconstrained. The that was 20 years ago now we have a project to, to start with. So we have an overall capital, minimal, but we can spend in a given year. We have restrictions on track testing, so we are only allowed to run this year's car. At a certain number of events a year, most of that pre-season. 00:05:03:23 - 00:05:29:09 Unknown So there's no opportunity to run the race car outside of race events during the season other than some marketing events. And then we have restrictions on the wind tunnel. So how much time? We have to test the model of the car. Before we build components and, to develop their dynamics, as well as some regulation around the computational time to do safety. 00:05:29:09 - 00:06:00:01 Unknown So fluid dynamics, so that the only real areas at the moment that are not constrained is, is the simulation tools, with the exception of the wind tunnel and CFD, obviously. So it was a higher importance on those those tools being being good. Yeah, absolutely. So this is where I sort of tell it really comes in having people in the team that can just sort of be highly creative or squeeze the absolute and degree out of a piece of technology, either of any limitations on the technologies that you can use. 00:06:00:01 - 00:06:24:16 Unknown It's all within, formula one. There's not really limitations on the technology itself. There's just limitations on the constraints of our users. So as I say that the cost is is one aspect, but if we take, say, the CFD regulations, where obviously the, the fluid dynamics is more cost effective than building a model and running in a wooden tunnel. 00:06:24:18 - 00:06:50:18 Unknown But the the constraints of such the, the there's not an unlimited amount of compute capacity that we can use to do CFD in an attempt to bias the advantage away from teams. Who can you can find more, more compute capacity than others. So yeah, that's not really a constraint on the technology as such. But there is constraints that change which technologies you pick and how you approach problems. 00:06:50:18 - 00:07:13:23 Unknown And just talk a little bit more specifically about AI. Where are you using AI today? You know, can you give an insight into what sort of areas of the business where AI is playing a pivotal role? So I think when we talk about AI, obviously AI is lots of different modeling techniques. Lots of different applications of technology again, available. 00:07:13:23 - 00:07:49:11 Unknown I think we were quite early in that journey in this as far as we've had a machine learning group, in the department since, I think 2016. So almost ten years now. And obviously the landscape for AI is changed a lot in that time. But it really depends where, where in the spectrum of car development you look, there's aspects where like what organizations we can use, recent AI to drive efficiency and operational, efficiency. 00:07:50:03 - 00:08:20:16 Unknown In terms of information retrieval and building software systems that are, you know, better, integrated and smar

  3. ١٦ يوليو

    AI Changes Everything: Using AI to Help Prevent Human Trafficking

    Discover how AI, data sharing, and cross-sector collaboration help organizations identify trafficking patterns, improve prevention efforts, and disrupt criminal networks. See how technology can support measurable action against human trafficking by exploring more at www.oracle.com/aichangeseverything/video-series   How can artificial intelligence help address one of the world's most complex humanitarian challenges? In this episode of AI Changes Everything, Neil Sholay, senior vice president of AI and Data at Oracle, speaks with Ruth Dearnley OBE, president and founder at Stop the Traffik, about how AI, trusted data sharing, and collaboration across industries can help prevent human trafficking.   Rather than focusing only on technology, the conversation explores how AI becomes more valuable when applied to real-world outcomes. Ruth explains how Stop the Traffik developed the Traffik Analysis Hub to combine data from financial institutions, businesses, law enforcement, governments, technology platforms, and communities to reveal patterns that would otherwise remain hidden. Together, they discuss how shared intelligence can help expose trafficking networks, improve prevention, and support faster, more informed decision-making.   The discussion also explores the role of predictive analytics, AI agents, data quality, responsible innovation, and collaboration across organizations. Ruth shares examples of how insights are being applied to major global events, including preparations surrounding the FIFA World Cup, and explains how measuring prevention has become a meaningful way to demonstrate impact. Throughout the conversation, both speakers emphasize that technology is most effective when it complements human expertise, helping people make better decisions while preserving the human connection at the center of every solution.   STOP THE TRAFFIK is a global organisation dedicated to preventing human trafficking. We combine data, technology, and lived experience to identify patterns of exploitation, empower communities to protect themselves, and work with businesses, financial institutions and governments to drive systemic change. Our vision is to create a world where no one is ever bought and sold.   If you would like to help, or donate to Stop The Traffik, visit: https://stopthetraffik.org/donate/   00:00:00 Disclaimer 00:00:15 AI for Fighting Human Trafficking 00:01:00 Meet Ruth Stanley OBE 00:01:23 Stop the Traffic Origins 00:05:07 Building a Global Movement 00:08:35 Human Trafficking as a Business 00:12:37 Creating the Traffic Analysis Hub 00:17:03 From Insight to Prediction 00:20:09 World Cup Trafficking Prevention 00:24:04 Ruth's Personal Journey 00:29:21 Technology, Scale, and Vision 00:33:17 Building a Network of Networks 00:36:14 Rapid-Fire AI Questions 00:39:23 AI in Everyday Work 00:40:17 Closing Thoughts

  4. ٢٤ يونيو

    AI Changes Everything: What Leaders Must Get Right About AI

    Discover how leaders can move beyond AI experimentation to build trust, scale adoption, and create meaningful outcomes across defense, government, and enterprise organizations. In this episode of AI Changes Everything, see why leadership, mindset, and responsible AI adoption matter more than any individual technology. Explore more AI topics at http://www.oracle.com/aichangeseverything In this episode of AI Changes Everything, Tony Reeves, partner for AI in Defense at Deloitte, shares his perspective on one of the most important challenges facing organizations today: how leaders can successfully adopt AI in complex, high-stakes environments. Drawing on experience across defense, government, technology, and consulting, Reeves explores how AI is being applied across command, operational, and administrative functions. While public attention often focuses on autonomous systems and battlefield applications, he explains that many of the most immediate benefits are emerging through decision support, productivity, logistics, planning, and knowledge work. The conversation highlights how organizations can create value by focusing on real user problems rather than becoming distracted by technology for its own sake. A central theme throughout the discussion is leadership. Reeves argues that mindset often matters more than toolset and explains why organizations must move beyond traditional approaches to innovation if they want AI initiatives to succeed. He discusses the importance of multidisciplinary teams, user-centered design, trust, assurance, and creating environments where experimentation and learning can happen safely. The conversation also explores why many AI pilots fail to scale and what leaders can do differently to improve adoption and long-term impact. The episode examines some of the most important topics shaping the future of AI, including sovereign AI, resilient infrastructure, data strategy, energy requirements, assurance, governance, and the balance between human oversight and automation. Reeves shares his views on the future of competition, the changing nature of national security, and why organizations must prepare for a world where AI becomes deeply embedded in decision-making and operations. The discussion concludes with practical leadership advice, insights into preparing the next generation of leaders, and a thoughtful exploration of why human ingenuity, curiosity, and judgment will remain essential as AI capabilities continue to advance. For leaders navigating AI adoption, this episode offers valuable guidance on how to build trust, create value, and prepare for a rapidly changing future. Tony's Prompt of the Week: Please search my emails this week for any outstanding action items that need my attention, do not just include emails where I am directly @ mentioned but include emails that ask or imply that I need to take action. For instance, "What do you think?" or "Can you..." Estimate how much time it would take to complete the task based on these criteria: ○ Do now – 2 mins ○ Focus – 5 mins to 30 mins Do last - more than 30 mins Next, organise them into a table labelled as 1. 'Do Now,' 2. 'Focus,' or 3. 'Do last,' the date requested, a description of the task, a link to the message, and your estimation of the time required to complete each task based on the time estimation criteria provided, and present the table sorted by the label category. ------------------------------------------------ Episode Transcript: 00:00:00:00 - 00:00:15:06 Unknown The views expressed in this podcast are those of the individual speakers, and do not necessarily reflect the views or policies of their organizations or call or its affiliates. 00:00:15:08 - 00:00:32:13 Unknown What role does I play in national defense? How does leadership need to evolve with the use of AI in that scenario? Where should I be applied and not in the context of command and control? 00:00:32:15 - 00:00:51:19 Unknown Hi everyone. I'm here with Tony Reeves, partner for AI in Defense at Deloitte. We're going to be talking today about the use of AI in a defense context. Okay, Tony, great to have you on here. I don't even know this. We obviously met previously. You are the first person I had on the list to interview for this. AI Changes Everything video series. 00:00:51:22 - 00:01:09:05 Unknown And I'm not just saying that. And it's because I had. I thought you had some really unique views about how I could be used. So no pressure then at that point. No, no, we're going to keep this very, very light. So tell us a little bit about your role at Deloitte. What you do, if you could please give us some, some background. 00:01:09:07 - 00:01:34:20 Unknown The journey. Sure. So I've been doing it six years. We are traditionally seen as a tax and audit company, a professional services firm. And six years ago, we deliberately started thinking, what's the future going to look like, especially in defense around technology and this idea that we have to be continuing what we've always done, helping clients fix difficult problems and challenges, but also be relevant in that future space. 00:01:34:20 - 00:01:56:07 Unknown And that means actually understanding the technology, how it's applied, what it does. And so I spent last six years in Deloitte trying to help us work out who is defense, security and justice. What does I mean for that sector? How do we start to adopt AI responsibly, safely and effectively? We need to be making sure that it does and lives up to its promises. 00:01:56:09 - 00:02:16:00 Unknown Prior to that, I've spent time in Microsoft, as a digital advisor. I've worked in the government. I've led major programs. And I spent 13 years in the Army. And so I have this weird, background of being an infantry officer, some who used to hide in hedges and carry heavy kit, as well as a techie geek. 00:02:16:00 - 00:02:32:06 Unknown And I was always a geek at heart. Coding in the early 1980s when I was a kid, and continue to do so. Excellent. But I think that that's what makes you quite unique. You know, you've sort of you've been on both sides of the fence, as it were. Okay. So why don't we start with just talking about where does it make sense? 00:02:32:06 - 00:02:55:05 Unknown Where is it appropriate to use AI in a command and control context within defense? And then perhaps where does it not? Yeah. I think that's, you know, that's a heart of I think so to the audience, I mean, most people saying what's defense doing with AI? And naturally there's a concern is a worry that like, you know, whenever something is not visible or not clear, people fear the worst. 00:02:55:07 - 00:03:17:01 Unknown And I think that, you know, often and sadly on regular as you know as well, panels will always be what about the Terminator? Robots are going to come and kill us, of course. And it's like, well, yes, there is a risk. But yet actually defense is taking that extremely seriously as an organization. And so where we're increasing is seeing the use cases really falling into two three parts. 00:03:17:01 - 00:03:38:00 Unknown Is this the yes the warfighting the battle space piece where of course in you know, Ukraine we see this all the time where where drones where they're controlled or automated. On both sides are causing damage and destruction. But that's a fairly small use case, although it's of course, going to grab the headlines, in the command space. 00:03:38:02 - 00:03:59:03 Unknown There's many activities which we as a civilians will just take for granted the idea that plotting a route from A to B, going from London to Birmingham, we'd immediately go to an AI and and a map system, say, what's the quickest route? And yet for defense, this is still relatively new in many cases because partly attention's on the battlespace as opposed to that command. 00:03:59:03 - 00:04:17:09 Unknown And control. And how do we make things more efficient? But then, of course, the biggest area, like every organization is in the office space, and that's where I think everybody is looking and saying, here's how AI today is making a difference to how we work, what we do and how we deliver the the outcomes that we have to achieve. 00:04:17:11 - 00:04:40:03 Unknown So there's sort of battlespace office space, command space. And right now you're saying fair amounts of it's happening in the office space and the command space okay. It's interesting because as you rightly say, all the headlines immediately go to the battlespace, but all the values and the impact is happening in the other, too, right? Yeah. And I think that, yes, there is a lot of work in battlespace. 00:04:40:03 - 00:05:00:23 Unknown And yeah, we see, many things in the headlines because it's always going to grab the attention. Last week, Ukraine announced that they had seized ground in Ukraine. Back off Russians, using purely autonomous systems. No humans involved at all. Apart from on the Russian side and Russian surrender to a small group of, remote controlled drones. 00:05:01:01 - 00:05:26:17 Unknown Yeah. So they is always going to be a headline grabbing story because nobody's interested in. I've used this AI to help me write this email. It's not going to make the 9:00 news isn't understood. Yeah. I like you. I spent a lot of time talking to mid and central government. And the impression I get is that there's a, there's a real willingness now to adopt AI and apply it to things like logistics and stuff like that. 00:05:26:19 - 00:05:57:12 Unknown You know, who are you typically working with? You know, is it the generals? Yes, I rose, and, you know, what are you hearing from them in terms of how they want to use AI? I think that's a that's a really big question is going to be lots of different ways to answer that. Normally my clients range from, government ministers and departments, who are probably trying to either implement a strategy or be

  5. ٢٤ يونيو

    AI Changes Everything: Can You Insure AI Risk?

    Learn how organizations can quantify AI risk, manage model uncertainty, and build trust as generative AI adoption scales across the enterprise. In this episode of AI Changes Everything, see how AI insurance can help organizations balance innovation, governance, and financial risk. Explore more AI topics at http://www.oracle.com/aichangeseverything   In this episode of AI Changes Everything, Dr. Michael von Gablenz, head of Insure AI at Munich Re, explores one of the most important challenges facing enterprise AI adoption: how to quantify, manage, and insure against AI risk.   As organizations invest in generative AI and large language models, attention often focuses on productivity, automation, and return on investment. Yet every AI system carries uncertainty. Dr. von Gablenz explains why hallucinations, inaccuracies, changing model behavior, copyright exposure, operational disruption, and financial loss must be considered alongside the potential benefits of AI adoption.   The conversation examines how Munich Re is helping organizations understand the unique risk profile of AI systems and why insurance can play a critical role in enabling trustworthy AI. Drawing on examples from real-world deployments, Dr. von Gablenz explains how AI insurance can help organizations address uncertainty, evaluate model performance, and build confidence in AI-powered business processes. He also discusses the importance of continuously monitoring foundation models, assessing changing risk exposure, and understanding the downstream impact of model updates.   The episode explores the relationship between operational risk management and financial risk management, highlighting why leaders need to consider both when evaluating AI initiatives. Dr. von Gablenz shares how insurance can serve not only as financial protection but also as a signal of AI readiness, helping organizations better understand whether a system is fit for purpose and capable of delivering reliable outcomes at scale.   The discussion also covers AI governance, model uncertainty, explainability, trustworthy AI, and the role that chief risk officers, chief information officers, chief financial officers, and AI leaders play in responsible adoption. For organizations looking to accelerate AI deployment while maintaining trust and accountability, this conversation offers valuable insight into one of the fastest-emerging categories in enterprise technology: AI risk management and insurance.   ------------------------------------------------------   Episode Transcript:   00:00:00:00 - 00:00:15:04 Unknown The views expressed in this podcast are those of the individual speakers, and do not necessarily reflect the views or policies of their organizations or iCal or its affiliates. 00:00:15:06 - 00:00:29:21 Unknown As we all race to deploy intelligence, how do we quantify the uncertainty of a large language model? Do we know what risk we're being exposed to? And how do we protect against that? 00:00:29:23 - 00:00:53:07 Unknown Hi, everyone. We're here today with, Doctor Michael von Gablenz from Munich. Re. You're the head of Insure AI. I, we're going to be talking today about the. I mean, what you're doing is quite fascinating, actually. You are, as I understand it, looking at the uncertainty around AI models and then figuring out how to insure against that particular risk, is that right? 00:00:53:12 - 00:01:13:00 Unknown Yeah. That's right. Leland, thank you so much for having me today. This is a great pleasure to be here. Very good. Okay. So can we just start a little bit by talking about what is insure AI, please, if you could just describe it. So Intuit is a team within Munich Re. In Munich re is a big insurance and insurance organization. 00:01:13:02 - 00:01:43:16 Unknown And what my team does is that it structures insurance solutions for different forms of AI risks. In particular, the risk that an AI model or generative AI model hallucinates or produces an incorrect output. From our perspective, that's one of the most pronounced risks when it comes to adoption of an AI model, simply because an organization wants to adopt an AI system or change AI system in order to support and decision making, or to automate certain processes or tasks. 00:01:43:18 - 00:02:07:19 Unknown But if you have, the model produces more mistakes or hallucinates more often than expected, then also the processes, the tasks, the executions will be wrong more often, which can lead to financial losses or liabilities down the road. And we believe that the insuring for those risks is very important. Also to drive an adoption of trustworthy AI systems throughout organizations. 00:02:07:21 - 00:02:28:13 Unknown Amazing. I meet chief AI offices almost every day, right? And CIOs, I don't think this is a market that many people realize exists. So talk a little bit about the background on how insurer I came came to be. It is something fairly recent that how long have you been looking at this area? Yeah. So being sure the first AI system in 2018. 00:02:28:13 - 00:02:53:14 Unknown Wow. This was an AI system which was already taking autonomous decisions in autonomous actions. So in the case, that we, that we did, it was basically an AI system designed to detect fraudulent credit card transactions, and users were merchants, who were relying on this AI system to make decisions within milliseconds to say, yeah, either the credit card transaction is fine. 00:02:53:17 - 00:03:20:11 Unknown Now, accept it, or it seems to be a fraud and block it. And from a user perspective. So from a merchants perspective, this poses a risk, because if the AI system accepts more fraudulent credit card transactions than expected, then the merchant as the user has more fraud costs and especially unbudgeted, fraud costs. So this poses pose the challenge for adopting the AI system. 00:03:20:13 - 00:03:53:21 Unknown And the AI provider approached, yeah. Approached uniquely to structure. Yeah. A solution insurance solution to cover, those AI error risks on behalf of the users, the merchants. Very good. And I mean, are there many players in this market at the moment? I mean, it creates huge, but other startups that are coming into this, into the space or any of the other major reinsurers or insurance companies that you also see, we're not seeing major insurance or reinsurance companies, entering the space yet, but we are seeing, several insurer techs are coming into the space. 00:03:53:21 - 00:04:15:13 Unknown So some from the market, some are from. Yeah. From San Francisco Bay area. So it seems that. Yeah, also, the venture community is picking up, on. Yeah. On this topic. And I think that's not surprising because in our mind, insurance is a fundamental. Yeah, instrument has driven many, you know, adoption of, of new technologies. 00:04:15:15 - 00:04:41:17 Unknown So when we're looking back in history, even something like you have steam boilers, the adoption of steam boilers, so they are also insurance was really fundamental. Yeah. Basically. Yeah. Analyzing the safety of those steam boilers and then, providing insurance against major catastrophe in case an interesting boiler blows up. So in my mind, this also has driven then the Industrial revolution forward had a major impact. 00:04:41:19 - 00:05:05:15 Unknown Yeah. It's interesting that you use that as like an analogy or comparable. That's exactly what I was thinking. I mean, this market could be absolutely huge, right? We look at, you know, $2 trillion of investments in AI training over the last two years, the fastest industry in humankind. I'm not going to put you on the spot and ask you to size the addressable market, but where do you see this? 00:05:05:15 - 00:05:26:22 Unknown Munich. We see this going, you know, are there any particular segments that you're focusing on or any particular industries? Can you can you give a sense to where you see the market developing place? You know, from all perspective, every company, globally is looking into adopting AI. And for every I use case where in case the AI. 00:05:27:00 - 00:05:58:18 Unknown Yeah, produces some form of a mistake, and costs occur. Those might be opportunity costs and those might be also actual costs for, for company to for example, we do some work. Well those might also then be liabilities in case an AI model is used, in some kind of customer facing, application areas in all those, those companies, and for all those kind of use cases, there exists a risk exposure there in where the access to risk exposure, the insurance might provide a solution in order to. 00:05:58:19 - 00:06:22:13 Unknown Yeah, take on take off this risk and therefore also drive adoption of this technology. Right. So all these things I hear like IP infringement hallucinations obviously is one of the big one, accuracy of these models, every single one of those is potentially an opportunity for you to provide some level of certainty and assurance of insurance around this, right? 00:06:22:15 - 00:07:08:08 Unknown Yeah. Correct. Huge opportunity. And, I mean, the MIT, yeah, has created a repository around AI risks. I think they have they are now standing at around 1700 different AI risks, which might seem quite overwhelming, but from our perspective, we bucket those risks into two classes. So one are risks which are really driven by an AI model being just at the end of the day, a statistical system with any statistical model, this uncertainty, uncertainty in the output and many of, the risks that you mentioned, the, inaccuracy, the hallucination risk, but also, things like, the copyright infringement risk, they can be traced back to incorrect output by a model. 00:07:08:14 - 00:07:33:10 Unknown And, yeah, this is now, mind a new class of risk of AI, then also new, you know, insurance solution, this new uprising, this new, underwriting considerations with also new considerations ar

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AI Changes Everything is an Oracle podcast that explores how business leaders, innovators, and practitioners are putting artificial intelligence to work across their organizations. Through conversations with experts shaping the future of business and technology, the series examines real-world AI strategies, emerging trends, and the opportunities and challenges organizations face as AI adoption accelerates. Each episode offers practical insights into how AI is changing the way people work, make decisions, serve customers, and drive innovation. Whether you're a business leader, technology professional, or AI enthusiast, you'll hear perspectives that help connect today's advancements to what's next. Explore the series at http://www.oracle.com/aichangeseverything

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