Transformation Every Day
00:32:57 · Johannes Langguth, JP Haas
[00:32:57] S1: humans [00:32:59] S0: are not made for this. Humans are not made to manually read out what these big code bases and exceptions are doing. There should be technology that helps you to understand technology. That's the first step. And then the second step is even if you even if you can create a technical map of everything, you don't know how that impacts the business end user because, again, you can't read that from from the code and the pure technical objects. And in many ways, that is what these transformations are about. It's figuring out how are these technical objects actually used by the business side of the house in their day to day work. And if you don't have that visibility, which comes down to a communication and coordination issue between these different teams, then it's also very, very hard to make these decisions. Do you agree with that? Or what's what's your take on why why there is so much anxiety in this space?
00:33:59 · Johannes Langguth
[00:33:59] S1: For me, I think this is it's a complex topic. It's it's a very psychologically human topic, definitely. It's about the unknown, but I think nobody's really safe from that. If if I look into my typical customers and so on, they're experts in their field, and they are working in their organizations and their processes and so on. And based on what they know about their process, they have optimized themselves. And this kind of knowledge, of course, how broad this kind of knowledge is is 100% casual for what comes out of it in that way. Yeah. Because on the one hand, you have the kind of user type who needs this kind of guardrails and so on, who who is feeling safe with a UI, which is more or less leading him through certain steps and so on. And this is necessary for for many activities, of course. Then you have the other kind of of user who is much broader and who needs this kind of tools, like this kind of intelligent tools where he has basically unlimited possibilities. And what you are now getting from and we are here now in the business AI perspective is, of course, helping, in my opinion, more the second type of users. Because you're getting now an unlimited set, you have LLMs within your ERP system, and you can ask him anything or can make the system do basically anything. But the problem is, no, you have to know a lot of things about your business, about your system, and so on to be enabled to make this kind of decision and to search for new found tiers within your organization, with your architecture, and the way you work with that. And this kind of limiting knowledge that we had in the past, yeah, where everything was just one way, was strict or one example where you... Where optimization was always always a black box and you had to rely on it. Or if you do not feel good about it, you couldn't really work with it. So you did it manually again. Was in my opinion one of the limiting factors why so many things are now slow in the past because data volume has risen, complexity has risen, but the kind of way to work with that data has remained in many areas scaringly the same. Yeah. Because these kinds of, let's say, doors that we were talking about in the beginning still exist in that way. Coming now, new solution will help you to get this kind of transparency, to get this kind of knowledge, to make some sparring.
00:36:24 · Johannes Langguth
Then I I think something what I prefer to to call insight to action is actually possible. And this is also some principle that you, for example, follow very, very closely with Conduct AI, that it's one thing to get the analysis and, like, the bad news, yeah, which is something that systems or also AI, when I ask AI, like, give me give me something of the biggest problems or the biggest missing things that I have in my system. That's where they excel. Yeah. In presenting you with bad news. But this is nice, but it doesn't really help you if you cannot generate any consequences out of this, which is the interaction part, which should be like options, simulations, and so on. And also if you want to execute this, do not want to then execute it manually, but you want the system to execute this. You want to make the decision and the system then executes the decision. This is something which is for me the actual really game changer because it gives you an absolute maximum inflexibility and resilience into anything that now in this kind of multi crisis world we are living in approaches us. And that's why I think these kinds of capabilities that many AI tools, models, agents, and so on giving us is not the frightening thing that many do out of them. Like, oh, it will cost all our jobs. To be honest, talking about that point, I think every generation has its technology will take away our job moments. The only difference now, and Scott Galloway did a very good thing, a very good statement about this, is the only thing that's now different is that the technology now is having a better marketing budget than it used to be. Yeah. So the frightening is a little bit more and more difficult. But in reality, it's giving you wonderful, powerful tools where you still remain in the driver's seat if you are doing it right. And that's what I think it's a good thing about that, and that's why I'm absolutely excited about the possibilities there.
00:38:25 · JP Haas
[00:38:25] S0: And it all comes down to speed and being able to do things faster with more visibility and with more confidence. Yeah. The effect of working, but I would argue in probably all of all of the different fields is humans now can take decisions with much more visibility. They have more more power at their hands, and they can they can do all of their work and implement it much faster. And that's that's powerful, especially in times when business model change and having to be able to adapt so so rapidly is is more more crucial than ever before. You said it, Alex. Right? I I really agree with that.
00:39:05 · Johannes Langguth
[00:39:05] S1: Absolutely. But when we look into, let's say, the use case itself, let's go down. You have now a customer. Customer who is now in the typical position of, okay, I will now move system wise, new technology, and I really am 100% convinced that I have to clean up, I have to do clean code, I have to enable myself to use much more of the technology benefits than I did in the past. What are the, let's say, lifecycle of the Conduct AI activities within these kinds of initiatives? Where's the best point to start with you? Yeah. Is... Or is there a best point where you say, like, okay, that's that's the point where where you get the the most out of it? How should I rep the team or enable the team to use it the best? And where do you see, let's say, an optimum life cycle based on... Yeah. We have to talk about now, yeah, because you are also growing with your capabilities on a daily bay... Basis almost. But based on now, how should I use you in a in a best practice way?
00:40:08 · JP Haas
[00:40:08] S0: Let's segment this into two scenarios. Number one, the transformation or the S4HANA migration is currently top of mind. And number two, you're looking at the ongoing run state of just maintaining your systems, implementing new features. And let's start on on the migration side. The best possible time is is yesterday, and it's when you're when you're setting up a project and you you're trying to understand what are all of the all of the capabilities I have in my system when I start to to then run the fit gap analysis and then you move move things over. So the sooner you can you can start working with tools like ours in order to get that visibility, the better it will be. It will it will shorten the the overall project length. It will give everyone more more confidence that they know what they what they even do. Let's make this one step more concrete. The the pivotal point in most of the of the migration projects is the the fit gap phase. We we spoke about this a bit earlier already. But to repeat this, the the guiding principle here is, do I know what my business needs in order to keep on running smoothly and ideally better than before? And that's that process of capturing all of the requirements from the business users based on what the system today does. That process is really where we start with most of our customers today, and that's something we can we can automate and accelerate a lot. Today, what this is what this looks like is you have teams of IT and consultants flying around the world and interviewing users and trying to reverse engineer these these sets of of requirements, we can do this based on on the code and reading it out reading it out directly from the system and then giving people a checklist. Once that's done, you then take a look at how can we differentiate between what are the features that are really needed and which of them are less critical. So that's the the kind of workshop stage and and the the filtering stage. And then as a next step, where can I can I move this back into SAP standard without losing any or at least significant business capabilities? So that's the fit to standard side of it. Once you've done that, you you have strengthened and completed the template of what do you want your new SAP system in the S/4 world to look like.