WEBVTT 00:00:00.166 --> 00:00:03.545 Thomas, Vikas, thank you so much in advance for the conversation. 00:00:03.628 --> 00:00:04.796 How is GenAI, 00:00:04.879 --> 00:00:07.924 Thomas, reshaping customer expectations? 00:00:07.924 --> 00:00:11.344 It’s clear that they change, because we see AI will change how people work, 00:00:11.344 --> 00:00:12.637 how businesses work, 00:00:12.637 --> 00:00:16.349 and with that, naturally, the customer expectations are different. 00:00:16.641 --> 00:00:19.936 And now if we see even the first advancements of generative AI 00:00:19.936 --> 00:00:23.023 towards agentic AI, leveraging agents getting the job done 00:00:23.023 --> 00:00:25.316 fundamentally, this is also an expectation. 00:00:25.358 --> 00:00:28.695 And if you think about yourself in your private life, you just 00:00:28.695 --> 00:00:32.365 expected every service, every product is intelligent by default. 00:00:32.365 --> 00:00:33.658 And customers of SAP 00:00:33.658 --> 00:00:35.910 are leveraging AI to differentiate in the market. 00:00:36.119 --> 00:00:37.746 So, I believe it’s super exciting to be here. 00:00:37.746 --> 00:00:40.040 So you mention that and SAP's role, 00:00:40.040 --> 00:00:43.710 how is it helping businesses to advance? 00:00:44.586 --> 00:00:47.964 I think if you think about our portfolio, we help the entire enterprise 00:00:47.964 --> 00:00:50.884 —from HR to finance to production, supply chain— 00:00:50.884 --> 00:00:52.385 in all aspects to run their business. 00:00:52.552 --> 00:00:56.014 And now with the power of AI, we can rethink business processes. 00:00:56.014 --> 00:00:59.392 We can rethink business models actually, indeed, leveraging AI. 00:00:59.392 --> 00:01:02.520 And agentic AI is, for us, an opportunity to drive a total 00:01:02.520 --> 00:01:05.315 different level of productivity in the companies together, 00:01:05.315 --> 00:01:08.651 tapping into the huge data set. Because in the end of the day, 00:01:08.651 --> 00:01:11.112 AI is only as good as the data in that sense. 00:01:11.362 --> 00:01:14.491 And that's something, if you think about your data treasure 00:01:14.491 --> 00:01:17.619 in the company, it's in SAP systems. And we basically, with 00:01:17.619 --> 00:01:21.039 our technology, enable that you use the most relevant, reliable, 00:01:21.039 --> 00:01:23.958 and responsible AI using the data in the context of the 00:01:23.958 --> 00:01:26.628 business, in the business process, in the business 00:01:26.628 --> 00:01:30.298 decision-making processes of our customers leveraging our software. 00:01:30.298 --> 00:01:33.093 And that's something where we are super excited, together with 00:01:33.093 --> 00:01:36.513 our partners like BCG, to get the most out of that value for our clients. 00:01:36.513 --> 00:01:40.225 So picking up on that data point, Vikas, how, what are you 00:01:40.225 --> 00:01:43.978 hearing from your clients, and how are they navigating this 00:01:43.978 --> 00:01:45.980 very rapidly changing landscape? 00:01:46.314 --> 00:01:50.401 One of the things that I'm hearing a lot is customers are 00:01:50.401 --> 00:01:54.531 now moving from a conversation around experimentation and 00:01:54.531 --> 00:02:00.245 proofs of concept to really asking about value creation and ROI. 00:02:00.245 --> 00:02:02.372 And so we've gone from, you know, an AI 00:02:02.372 --> 00:02:06.960 experimentation discussion to very much a focus now on value creation. 00:02:07.335 --> 00:02:09.129 And I think Thomas is exactly right. 00:02:09.462 --> 00:02:15.969 The quality of your AI outcomes is essentially tied to the quality of your data. 00:02:15.969 --> 00:02:20.306 And that's where companies, such as SAP, which have an incredible 00:02:20.306 --> 00:02:24.102 trove of extremely actionable customer data, have a huge 00:02:24.102 --> 00:02:28.398 advantage, because you know that the data quality is going to be 00:02:28.398 --> 00:02:29.482 extremely high. 00:02:29.482 --> 00:02:31.401 Yeah, so the building blocks are there. 00:02:31.484 --> 00:02:35.697 So Thomas, when you think about the organizations that are doing this well 00:02:35.697 --> 00:02:38.950 —cloud, AI— what are they doing correctly? 00:02:39.826 --> 00:02:41.411 I think two aspects, which are important. 00:02:41.411 --> 00:02:43.788 The one is, first, just do it. Really embrace the future, 00:02:43.788 --> 00:02:44.747 embrace the innovation. 00:02:44.747 --> 00:02:47.667 Don't wait for regulation, don't wait for something being 00:02:47.667 --> 00:02:51.045 perfect, but embrace the innovation early to differentiate early. 00:02:51.212 --> 00:02:52.964 Second is you need a strategy. 00:02:53.089 --> 00:02:56.217 Just trying to embrace AI is not giving you the most value. 00:02:56.634 --> 00:02:58.386 So having a strategy about 00:02:58.386 --> 00:03:01.222 basically, the four dimensions you need to do a true 00:03:01.222 --> 00:03:04.767 business transformation—data, application, processes, and the 00:03:04.767 --> 00:03:07.395 people change management aspect—is critical. 00:03:07.562 --> 00:03:11.024 And the last one I just mentioned, people change, is critical. 00:03:11.024 --> 00:03:15.904 I mean, AI will fundamentally change the roles of every worker in the future. 00:03:15.904 --> 00:03:19.199 But it's a leadership task to take everybody with us to have 00:03:19.199 --> 00:03:20.909 education, learning, enablement. 00:03:21.159 --> 00:03:24.162 And this is something which we should not forget in the topic about AI. 00:03:24.162 --> 00:03:26.748 So education, learning, enablement. 00:03:26.748 --> 00:03:28.625 From your point of view and this 00:03:28.625 --> 00:03:32.128 idea of leadership, giving advice, how are you helping them 00:03:32.128 --> 00:03:35.131 to really leverage these potential tech solutions? 00:03:36.049 --> 00:03:37.342 I would say two things. 00:03:37.342 --> 00:03:39.093 One, I really love Thomas's point around the 00:03:39.093 --> 00:03:40.803 importance of change management. 00:03:40.845 --> 00:03:44.349 You know, at BCG, we have this framework around 10/20/70, which 00:03:44.349 --> 00:03:47.936 is, if you look at a large-scale technology change, about 10% of 00:03:47.936 --> 00:03:51.147 the challenge is around the algorithm, 20% importantly on 00:03:51.147 --> 00:03:54.609 the data, you know, which was also Thomas's point from before, 00:03:54.609 --> 00:03:58.154 but 70% is actually the change management to actually make sure 00:03:58.154 --> 00:04:01.741 all of the technology implementation sticks, 00:04:01.741 --> 00:04:03.576 the incentives that changed, and so on and so forth. 00:04:03.576 --> 00:04:05.328 The other thing I would just say is focus. 00:04:05.662 --> 00:04:09.707 One of the things we found, it's maybe a bit counterintuitive, is 00:04:09.707 --> 00:04:13.378 companies that have done a lot haven't necessarily been the 00:04:13.378 --> 00:04:15.880 ones that have been the most successful. 00:04:16.172 --> 00:04:19.717 It's those that have actually focused on three, four of the 00:04:19.717 --> 00:04:23.554 highest, most value-creating use cases and then really done all 00:04:23.554 --> 00:04:27.058 the hard work that Thomas was describing to make sure that 00:04:27.058 --> 00:04:30.812 they got to the implementation and the value at the other end. 00:04:30.812 --> 00:04:34.107 But Thomas, what is the role of partnerships when you think 00:04:34.107 --> 00:04:37.485 about it in driving adoption and success of new technologies? 00:04:37.694 --> 00:04:39.904 I mean, as a company, we are absolutely convinced that you 00:04:39.904 --> 00:04:41.489 only can innovate together with partners. 00:04:41.489 --> 00:04:43.825 We have 26,000 partners around the globe. 00:04:44.242 --> 00:04:46.744 And if you think about AI, you've so many different nuances 00:04:46.744 --> 00:04:49.330 and layers from the application side, then you have the large 00:04:49.330 --> 00:04:52.000 language models, then you have the infrastructure side with the 00:04:52.000 --> 00:04:52.875 GPUs and everything. 00:04:53.084 --> 00:04:55.503 So fundamentally, only if you line up the partnership 00:04:55.503 --> 00:04:57.547 entirely, then you get the most out of that. 00:04:57.547 --> 00:05:01.592 So for us, partnerships in AI, it's the most critical one to be successful. 00:05:01.592 --> 00:05:04.721 And then on top of that, as Vikas said, to really get the 00:05:04.721 --> 00:05:08.057 value out of that, we need also the partners like BCG to lead 00:05:08.057 --> 00:05:09.726 the way with the value conversation. 00:05:09.726 --> 00:05:11.853 So I believe it's all about partnerships. 00:05:12.562 --> 00:05:14.480 Vikas, how do you see partnerships? 00:05:14.981 --> 00:05:17.567 I don't think I can say it better than it's all about partnerships. 00:05:17.567 --> 00:05:21.904 I totally agree, Thomas. The landscape is changing so rapidly 00:05:21.904 --> 00:05:26.284 and the competency and the skill set that's required is such a 00:05:26.284 --> 00:05:27.452 moving target, 00:05:27.452 --> 00:05:31.998 you have to work actually in an ecosystem of partnerships. 00:05:32.707 --> 00:05:35.793 And so we're thrilled with our partnership with SAP, and we're 00:05:35.793 --> 00:05:39.380 excited about the prospect of partnering with them going forward as well. 00:05:39.380 --> 00:05:41.257 A good business partner actually once told me: 00:05:41.257 --> 00:05:45.470 we need to get away from ego-system to ecosystem. 00:05:46.137 --> 00:05:49.849 There's no better way to wrap up this conversation. Well said. Thomas, 00:05:49.849 --> 00:05:52.602 Vikas, thank you for sharing your expertise with us.