 Hi, this is Hoso Pupati and we are here at SAP Safari. And today we have with us NDS Welsh, VP and Head of Marketing and Solutions of AI at SAP, and we are scared to have you on the show. Thank you so much for having me. It's my pleasure to host you today. Before we dive into this discussion, since we are here, David, I would love to hear from you how you are seeing the evolution of AI in this enterprise and the ERP space. Sure. So look, AI offers tremendous opportunities for business to enrich their business processes with AI to get better insights and better outcomes from, we call this business AI. It's AI built for business. It means it's ready to use on day one. It's built on deep industry expertise and knowledge that SAP has combined with the data that is in business systems of our customers to deliver outcomes that are relevant to them and to their business. And it's built responsibly, with responsibility with deep AI ethics built into it from the very beginning. What kind of adoption has been the economy? Of course, all these tools they use. In the early days also, security was using some AI, business intelligence was there. But if you can also know how much has it evolved over time, when you look back at it, it's like basically helping business customers. Their developer time is being saved. Their sales are becoming more efficient. One example that I'm really excited about is that AI from SAP is built into the applications our customers use every day. So let me give an example out of travel and expense. We're here at the conference and as soon as we're back, it's back to filling in the expense reports. Now, with our solution called SAP Concur, where we're able to scan the receipts. You as a traveler are able to scan the receipts as you receive them, scan them with your mobile phone, upload them to SAP Concur. In the background, AI already extracts the information from those receipts and pre-populates your travel expenses. So that means by the time you get home, you have a lot less work to do. You don't even need to look for the receipts anymore. You've lost them previously or something. And that really speeds up that process in a very seamless fashion. And that's part of the value we bring actually to more than 23,000 customers using this solution today. You did talk about business AI, but if you look at SAP in general, where is AI in your policy strategy? What is your AI strategy? And once again, at Sapphire, was there any announcement that were made which aligns with the strategy? So AI is centered to our strategy. We built it into all business applications, whether it's in ERP, SAP S for HANA, or it's in HR, SAP Success Factors, AI is built into these business applications to help users be more efficient and be more effective. And that really speaks to the comprehensiveness. So let me give you an example in finance. So take the example of a function called accounts receivable, where your teams receive payments from your customers for open invoices that you have. Previously, matching these payments to open invoices so you can balance out your account when it's accounted for correctly has taken a long time. So even when you set up rules, these rules might take a good chunk out of that automation work over for you, but they don't cover everything, especially as your business evolves, as you onboard new customers, their challenges to maintaining a static rule framework. So with AI, we're able to match these incoming payments to open invoices very, very quickly and with a high rate of accuracy so that you only need to look at the exceptions in that process. Some of the core customers or industries that SAP serve, some of these industries have been around for a very long time. And some of these industries are also in a very early stage of digital transformation, cloud adoption. Do you see, though, that they are running into some kind of roadblocks or hurdles when they look at the AI technologies and SAP, once again, help them to not only onboard them, move over quickly, or just help them wherever they are in their journey? I think that's where one of the beauties of the approaches and what our customers tell is that it is built-in, right? So you don't need to build anything. In addition, you don't need to go off and spin up your own environment, get the data, train the model, figure out that your data isn't as clean as you thought it would be or as complete as you thought it would be. We take care of that heavy lifting for you so that you can really consume it on day one in the applications you use. We are here that when you have a lot of customers, they are also sharing their own stories. Of course, I will not ask you to pick and choose one because they are all your kind of babies. But if there was any specific use case where this industry was kind of suddenly a leaf frog because of AI... I think there specifically it's actually about business functions, right? And, for example, the Swiss Railway Company is using AI from SAP in their accounts receivable management process and is able to automate up to 99% of their process now. For them, that's about a million payments a year. They no longer need to assign manually. A huge time saving for them. Now, let's talk about which is the hottest topic in these days, GDP and now Genitive AI. First of all, how new folks are looking at it? Genitive AI is part of our vision of business AI and our strategy for business AI, right? Like I said, business AI is AI built for business and Genitive AI is an additional and new, very capable and powerful technology that helps us realize this. With Genitive AI in SAP's business applications, we want to help our users and customers define the outcome and then have Genitive AI figure out how to get there. And today, we're very excited to announce the first set of six AI scenarios that we're planning to build into SAP's applications. And these are, let me give you one example of them. For example, we've worked with customers in the automotive and manufacturing industry. Imagine you're a large automotive company. You have lots of trucks coming to your gate to the factory and they deliver parts for the cars that you assemble. Now, your logistics clerk needs to make sure that actually there's the right number and the right type of components on that truck that has just shown up to your gate. And it takes a lot of time, right? Even in just the paperwork, the goods receipts, the delivery notes. And by combining SAP's document processing with large language models, we're able to extract that information very, very quickly. We're getting up to 70% of accuracy at the moment right out of the gate. And the huge advantage there is on the back-end side, actually, if you think back to previous approaches, you would have had to have tens of thousands of documents and examples and annotations to get to good data set and training set. By combining now document processing with large language models, we're able to completely skip that step and you're able to go live on the day one of this. We're really excited about the opportunities. And that's just one of many. You mentioned that here you talked about six AI scenarios. Can you talk with one of those? Sure, of course. So one scenario that we're really excited about is in the area of transportation management. We're combining our document processing capabilities with large language models to help our customers extract information from documents like goods receipts and delivery notes very, very quickly without the need to have training data which is usually very expensive and cumbersome to obtain. Also in the area of HR, you've likely seen our partnership with Microsoft that we have announced today. We will be using OpenAI GPT services through Azure and combining them with SAP Success Factors Recruiting by leveraging the GPT technology with the deep data and knowledge that is in the Success Factors. We're able to fine-tune the prompt engineering to get you very specific and tailored job descriptions because as a high-ring manager, you take usually something that's lying around a job description that you've used in the past. It's not very specific. It might have changed by combining Success Factors Recruiting data with large language models. We're able to help you and get to a very good and tailored graph very, very quickly. Now, the other examples are around SAP's digital assistant that we've announced that we're building it into a product like SAP Stardin Success Factors in our customer experience suite. There, through the help of large language models, we're able to provide a natural language processing capability embedded into the applications that our customers use every day to help them schedule a vacation, find out information about their purchase orders and many, many more things. So those are just some of the examples that we've announced today. One thing I would ask you is that when we look at the options they've generated, we also, like, governments are getting great and some of the customers or industries are raised to see that they're compliant and that they're dealing with some sensitive data. So do you also have some worry, not essentially, but the customers, when they look at it, it could be ethical, it could be legal. And if, yes, how do you also ensure that you know when you use or what technology, you don't have to worry about those aspects? SAP has actually been one of the first companies as far back as 2018 to define AI-guiding principles and since then we've also developed a binding AI ethics policy, binding in the sense that it applies to every SAP employee in how we work with AI technology, how we build AI embedded systems because we believe that responsible AI is at the core and has to be at the core of what we're doing. So these same principles apply also to how we approach generative AI. Since we are here, let me talk a bit more if the folks made an announcement we have to AI. We're so very excited because we've actually announced 15 new AI capabilities across SAP's entire business application portfolio and a large number of them are actually based on generative AI. Can you just give a little glimpse of, you know, of course we cannot go into all the 15, but you know where you feel hey, these are important ones. Sure. So the most important ones that we like to take on from generative AI actually are in the area of transportation management where we're able to extract information from delivery notes, goods receipts very, very quickly without the need of customers having training data to do that and also in the area of process management SAP's Signavio Suite where we're integrating large language models to help process owners and analysts create new processes and identify the right KPIs for these very, very quickly saving about 50 to 60 percent of time that you would usually need to set this up. Thank you so much for taking time out today and of course give us not only an overview of the announcement you folks made but also how you are helping customers with AI, generative AI in their journey not only where they are but how to take them forward. Thank you for sharing all those insights and as usual I would love to chat with you again soon. Thank you. Thank you so much for having me. It was a pleasure.