SAP: Bringing the ‘Operating System’ of a Company into the AI Era with CTO Philipp Herzig

There's a lot of exciting opportunities and new things you can build that we only dreamed of in the last I don't know 20 years at least since I'm a developer.

We are serving some of the largest customers. They have a lot of heritage.

customers. They have a lot of heritage.

They have a lot of complex landscape.

They can reduce like 30% of their efforts right to get to the outcome faster which of course then directly reduces the cost. The time is clearly over where you design software that

requires the intelligence to sit in front of the computer. If you look at classical software, what did you do? You

design a user interface to teach a human how they get their tasks done by clicking through the UI. Essentially,

this is over.

Hey listeners, welcome back to No Priors. Today I'm here with Philip

Priors. Today I'm here with Philip Herzig, the CTO of SAP, the enterprise juggernaut. We talk about their AI

juggernaut. We talk about their AI strategy, why SAP has endured and thrived through several technology transitions, why entrepreneurs are underestimating the challenges of scale,

why AI is a business model transition, not just a technology transition, why he thinks that LLMs are not enough for predictive analytics, and even about the traveling salesman problem in the real

world and the straight of Hermuz.

Welcome, Philip. Philip, thanks so much for being with us.

Yeah, it's a pleasure to be here. Thank

you. Everybody knows the name SAP but I do think that for uh lots of engineers or people who aren't close to the system in a larger enterprise they don't really

know like the breadth and function of the platform like can you just describe uh what you guys do for customers? Oh,

absolutely. I mean, look, SAP is the market leader, right, in enterprise of software applications and platforms, right? So, 400,000 enterprise customers

right? So, 400,000 enterprise customers and usually I des running their finance and HR and you know, supply chain, manufacturing execution logistics

warehouse management and then of course everything on the customer side, sales, services commerce procurement you name it, right? Um, end to end, right?

like SAP we always say we have the broadest portfolio in terms of uh end to end running the business end to end this is where SAP started with right the real giving real time insight and usually I

really describe this as it's not just software in itself it's kind of the operating system right uh of a company essentially uh in order to um get you

know from from everything from order to cash or source to pay right end to end managed for companies around the around the entire globe.

Um I definitely want to talk about AI uh LLM some of the stuff that you guys are doing internally and then around um predictive models as well. Uh but just

because the the macro backdrop is on everyone's mind both from a technology and an economic perspective. Oh sure

um I want to talk about like SAP's position in the market a little bit. SAP

has has stood the test of time through multiple technology and market cycles. I

as a earlystage venture capitalist am kind of on the other side of this where the narrative is like well when you have internet and cloud and mobile and AI and

social like you have um uh an opportunity for new players. Um what

what do you like SAP you know even today is the um I believe the largest like market cap enterprise software vendor versus sort of the the last generation

of the new guard like the sales forces of the world.

Um how has that happened like how did you how did you do it and what is make what makes it so durable? Well, what

makes it so durable, right, at the end of the day, I mean, if you think about this and it's happening as a little bit the same way also when we talk about the SAS debt narrative or the SAS apocalypse, right? I mean, anyway, we're

apocalypse, right? I mean, anyway, we're I I have the feeling like in this market last year AI was in a big bubble and everybody was kind of doing that, no, it's not and this here SAS is dead. Uh,

and and so on and so forth. Look, the

the reality is now of course with the the the costs of building being so low, right? With specifically agentic coding

right? With specifically agentic coding and all these latest powerful models. I

mean, something has always prevailed over the years because even when SAP was founded, right, in 1972, right, a long time ago. Um, I mean, why was it

time ago. Um, I mean, why was it started? Because actually in the 70s

started? Because actually in the 70s when the founders of SAP worked still at IBM, what did they do? They went to each customer, right? And they implemented

customer, right? And they implemented the finance system again and again and again and again and then they said like hey this makes no sense right because the economics it doesn't scale right

because of course you can do this right but you can only add so much value right in any given time and by the way we are basically programming the system very similar of course there's always a

little bit that is specific then to the customer and this was the idea where standard the notion of a standard software was born essentially right And then of course that stood the test of

time right because there is simply things and companies that need to get managed right end to end and that also has transformed throughout the years you've mentioned that right first from the mainframe to client server right

then to the internet then mobile and now of course AI so of course the software has changed and evolved all along with these techn what hasn't changed is what customers are seeking for which is

outcomes right outcomes and return on their investment in order to get the things done right um towards and and of course now AI is an amazing technology

that again uh helps to get more things done in the enterprise right and then that is actually what SAP is standing for right and and so what we are really doing is in given of course also the

breadth of the portfolio and the customers is of course to help customers to achieve more by deeply embedding AI AI agents and of course transforming now

the user interface and so on and so forth to help them get more right done in in whichever industry that they are that they are working in and um and and

and we believe that still will uh can continue right because what this is exactly what we're also seeing right now with of course there's still of course there's tremendous progress but we also see that the AI adoption in the

enterprise is still not where we want to see it right and like there's this gardener curve right where say like there's this AI innovation race and then there's this AI outcome race right then

and the gap almost increases right versus getting getting narrow and and and and that is what what we are really focused on right that customers get the

outcome uh from from AI to to achieve more given of course uh the foundation we have but simultaneously of course the system we we are kind of re-engineering the entire system right with the help of

AI uh in in a in a totally new way yeah um you uh now as CTO as of SAP P have

like a very broad purview. It also

includes the AI strategy piece of it um internal and for your customers like what do you think of as your your own top priorities for the organization and

where is SAP on this um re-engineering or reimagination journey?

No, look I mean we we are in the meantime all in on AI right? Uh so I mean everybody in the company is using agentic coding right like because that's

of course an amazing uh um uh productivity boost right that our developers have no matter in which programming language they are building uh the the software for the customers

but of course it's also really um again focusing on customer outcomes right and we've seen this for example early in the early days now with consulting for example right we built this thing called Jew for consulting which is phenomenal

because it was one of our fastest growing AI products because what this actually helps is to build the the um uh to to help the consultants right in the in an SAP project or in a complex

landscape if they are I mean again right we are serving some of the largest customers they have a lot of heritage they have a lot of complex landscape to help them actually to move into the cloud to adopt the latest AI

capabilities and so on and with dual for consultant they can reduce like 30% of their efforts right to get to the outcome faster which of course then directly reduces the cost not just the

time but also the costs that are necessary in order to get to to to get to the to to the latest software and we've seen this of course uh with conquer for example right where now our

travel booking agents or expense agents are alive and so so there's many of these outcomes that we are designing but when you look from a CTO perspective really it it's in my mind it's three

things that are really getting not not disrupted but are massively changing yeah to me the metaphor is a little bit Like when we moved from onrem to the cloud, right? Originally everybody

cloud, right? Originally everybody thought, hey, we just take the on-rem software we already built, we put it on the internet, call it cloud, right? But

then only over over a certain period of time, people realized, oh, what does actually CI/CD mean, right? You can

deploy daily or multiple times per day.

And then you realize, oh, hm, we always had multi-tenency in the on-rem software already, but then of course in the cloud, you have to learn how to scale it up and down, right? And like all of a sudden the software kind of got

re-engineered right to really you know build real SAS software that that uh that uh with all the concepts right in cloud computing and with AI the same is happening right and it is happening on

three levels it happens of course on the UI side right the time is clearly over where you design software where the dump software where the that requires the intelligence to sit in front of the

computer right so I mean if you look at classical software What did you do? You

designed a user interface.

Trying to hopefully you did some user research. Try to figure out in the

research. Try to figure out in the easiest way or the most intuitive way to figure to to teach a human how they get their task done by clicking through the UI. Essentially, this is over, right?

UI. Essentially, this is over, right?

It's now we call this generative UI, right? So, the UIs get dynamically

right? So, the UIs get dynamically generated, right? If you have analytical

generated, right? If you have analytical questions for example or if you want to do your deep research not just the deep research you find on perplexity or the the usual chatbots but deeply rooted let's say tariffs are being introduced

or new taxes or the straight of foremost what does this mean for my supply chain and then you can analyze this in conjunction with your SAP data so there's a lot of exciting opportunities and new things you can build that were

almost imposs that that we only dreamed of in the last I don't know 20 years at least since I'm a developer where now you know the system becomes is much more multimodal, much more proactive, right? Because it can run

proactive, right? Because it can run overnight and then only if you wake up in the morning tell you, "Hey Sarah, have you looked at this?" Right? Here's

a problem on the sales side. Maybe the

order entry is going down. You should do something here and here are some recommendations already for this. Or

here there's a problem in the supply chain, right? Uh because now if you're

chain, right? Uh because now if you're an oil and gas customer, obviously you want to know what are my options you need to replan, right? Like so all these things become super important for customers and that changes the UI. Then

the second one is of course the business processes like an order to cash in the past of course it has variances and so on and so forth but it was a rather rigid process but not like the standard

operating procedure of a company but now of course with these agents right we can blend the structured and the unstructured world yeah more seemingly so to get actually more work done. So

this whole move from software as a service to some call it service as a software outcome as a service that is of course what these agents are building for us. And then of course below that

for us. And then of course below that you have the whole data layer right the whole data layer of bringing of course SEP has a lot of super valuable data for a company right like all your general

ledger and your invoices and your warehouse and inventory information etc. But of course you now want to combine this right with the plethora of other data right in order to

kind of build this one harmonized semantical view because only we always say AI is only as powerful as the data is right so and and that is exactly what we are then also doing and transforming

on the data side to help our customers to benefit from a from a from a globally harmonized data model to fuel the AI.

what is the um biggest engineering or technical challenge for for for you guys when you look at you know these three bodies of work or anything else that you're doing at SAP? Well, the the

biggest challenge quite frankly is of course when you when you look at this is how do you it's actually not the AI so much right but it's actually teaching the AI to do the right thing at scale

right because I mean you can look you can build two years ago right everybody build a rack service right and you can you could easily with a P blew off everybody's the CEO's socks like look

how easy it is to build a chatbot on 10 documents right but that of but but SAP and and alo these large customers right they are always have a problem of scale okay what do you now with 100 documents well becomes a little harder thousand

documents becomes a engineering challenge and now if you go into jewel you are Sarah you're maybe an SAP US employee right of course if you ask a question of course for a travel policy

for example of course you expect a very different answer that me as a German employee right would would get so you then now need to connect this actually with your master data like where are you located in which country are of under

which payroll are you actually which taxes apply to you and so on. So all of a sudden it becomes a very very tricky problem. Same with MCP like last year oh

problem. Same with MCP like last year oh everybody could build an MCP server was so super simple right to to to hook up your MCP server and do amazing things with it but that's becomes like for 10

API is not an issue 100 you get already context blo and all these these challenges but we have 20,000 APIs right so it becomes this like Yeah, like

because it's so huge, right? Because

there's so much things. So it becomes this problem of scale, right? And doing

this really end to end for the customer because what we also built is really an integrated experience across. So you can ask your finance question, you ask your HR question, your supply chain, you can correlate that like this is this is the

biggest challenge to bring that so to speak to to to together. Yeah. And

design it then really for the for the for for the right outcome. Yeah. And you

said this also it's what's interesting and another interesting thing is um from my perspective is you had recently this other podcast I

think with Andre you had it uh on the the most important thing from a development perspective is actually that people start writing their evals that is with like I was on this tour for

a very long time um because um the the problem why does agendic coding work so well Sarah is um of course you can verify by the outcome, right? Uh you can either say, hey, is

right? Uh you can either say, hey, is the program compiling or that are your unit tests, right? Does it work? etc.

And of course, combined with a little bit of taste uh and a lot of hard engineering work, entropic and openi built these phenomenal code generation models.

The problem is if you now want to build a reliable outcome in finance and so on, you need the data that say hey with this input that's the output, right? in order

so that the coding agent can validate that and assert that against the reliable outcome. And that's something

reliable outcome. And that's something where there's a mindset shift in terms of how you describe the right boundary conditions to your coding agent, right?

the harness like like all the boundary conditions need to be true from a security perspective and from the data privacy perspective all the code qualities because you also still want to maintain that code on on day two and day

three and day four not just get get the first version vibe coded and then of course these eval right that then tell you hey this agent is actually doing what it's supposed to be doing in a in a

in a in a variety of ways do you still and you sometimes need to laugh because do you still remember in when I was a computer science student that where the Google guys came in in a in a lecture

and they said like hey I can go home at 5:00 p.m. because I wrote my tests and

5:00 p.m. because I wrote my tests and of course this was non you remember that like test first or test driven development.

Yeah, it's coming back.

It's coming back. Exactly. The the

reality is nobody did it. At least I never did it because a it was so much more fun.

It was not very popular at the end. Why

was it? Because a a it was a so much more fun to write the code first, right?

And then b of course usually the product manager gave you a very messy requirement. It

was very hard for you to to write the test actually first. So you while you wrote the code you kind of iteratively discovered what the system how the system would actually behave

right now the behavior and the writing the code is so much automated right because now you can write almost software like completely on its own but of course now you need to describe the

right outcome what you want from this thing and so that changes very much how the developers of course also uh now need to work specifically now that the models have the steps changed since

December last year.

It's really hard uh or I think it's not obvious how to picture um if there's a a

version of agents and uh models powering those agents in enterprise systems like SAP getting better in a compounding way

the way they have in uh generic code generation. Do you think it's possible

generation. Do you think it's possible in terms of verifiability or the ability to um go understand and evaluate against that intent because it is much um I

don't know if I would say it's more diverse than code but it is uh uh it's not it's not obviously verifiable as you pointed out like do you think it can be that that's exactly the point that is

where the the starting condition is great right I think in terms of like two lanes the first lane is of course you have the system of record today right you know exactly in the system. Hey,

given this or that instruction, right, what is the outcome, right? Because you

can see it in the database, right? And

then you can construct, hey, if the order to cache process runs like this, then you need to expect, right, that that uh and the the cash like the accounts receivable needs to come in this way, right, with the following

taxes and so on and so forth. So that

gives you verifiability. Now, the

challenge of course is rather that is never enough, right? Because uh if you just look into

right? Because uh if you just look into the system of record today that data is insufficient for this grand vision that everybody has that it becomes this autonomous enterprise or like the agency

of these agents is increasing right over time. So to at the beginning the agents

time. So to at the beginning the agents of course are coming back to you some people call this human in the loop or whatever right so they need to come back to you like also still with cloud code

or codeex and still ask you some clarifying questions. Hey, I have now I

clarifying questions. Hey, I have now I could now go this way. I could do that way. And with that, what what what you

way. And with that, what what what you want to design for is that you start to capture more of that context, right? I

always call this the tribal knowledge.

The stuff that is not in the system stored somewhere that just lives in people's heads or maybe in Slack channels, maybe in Teams channels, maybe it was just a discussion on the phone, right? So, it's not stored anywhere. So,

right? So, it's not stored anywhere. So,

how can you drive a decision from that?

And then, so the question is the agent needs to come back, ask you for input.

Now you want to store that and now what we do in the past we called this process mining now we call it agent mining because you record all these decision traces these context of what the users

are entering into the system and then you can either use it to say like hey wait a minute this is actually an anomaly that folks in I don't know in UK

from our company or the folks in Australia shouldn't do this because the standard operating procedure is this or you say like oh that's actually a very good improvement And then you can elevate this to be the new standard

operating procedure maybe not just for Australia but maybe for the rest of the world or more countries to run your company more efficient because now you learn something how the organization behaves because it can go two ways right

it could be either good uh or it could be a bad thing and then you maybe want to uh streamline the process how people then actually conduct the process in a different way and that then leads to

this kind of I call this then this data flywheel so to speak. So because with every trace, every input a user gives you with all the observability that an agent writes you, you have new data

sources that can then lead to new evals, right? Where somebody says yes, that's a

right? Where somebody says yes, that's a verified output uh so to speak that I want and then of course you can optimize the system more towards that outcome uh depending on what which data you

gathered. Do you uh have a strong point

gathered. Do you uh have a strong point of view today as to whether uh agents operating against these business processes

uh within SAP or otherwise in enterprise software? Do you do you think it's going

software? Do you do you think it's going to be um computer use? Do you think it is all you know code and tool use on APIs?

It's an interesting question. Uh I have not a a very spec uh a finite answer yet to this. So I think um given of course

to this. So I think um given of course also how clunky UIs are and so on and so forth and knowing the challenges also from UI automation from the past. I mean

it's phenomenal what they can do already today quite frankly. I mean they're still a little bit slow right and and and so on and so forth. But I still believe for the most part uh it will

that the majority will live with tool calling right and and agents running in the background and so on right because you also don't uh you know uh uh maybe

want to have the browser open all the time okay you can do this with headless browsers and so on but I mean if you can do this right with a more structured approach from an integration point of view I think that will be the preferred

method but then of course there will be always kind of things where an API is maybe not available or you have a legacy uh system for a time being and so on and then of course these computer use

approaches and so on will nicely tie in so to speak um as as well. If we zoom out a little bit and just think about um

um agents and um automated business processes in what domains do you uh hope customers will see that be most effective first?

Well, I mean we need to be clear, right?

I mean it has been for the most part uh very productive in what I call the unstructured world, right? Because let's

face it, I mean large language models are very good in the unstructured world, right? text and images and stuff like

right? text and images and stuff like that. And so of course everything where

that. And so of course everything where unstructured data is concerned for the most part like in services and in support and uh and maybe sales right and

then of course in anything related to knowledge work right that deals a lot with documents of course this is where we see like just do it for consultant product I've mentioned right this is a lot of unstructured information this is

of course where you know it was the easiest to get quickly to the to the to the to the return on investment it was harder now to kind of combined is also you mentioned tool use for example right

I mean the models had to learn of course first of all and got need to get got better right on how to use the tools and then you need to build orchestrators right and dismbiguate oh what does an order actually mean you mean a

maintenance order sales order purchase order the many what what order is a very overloaded term it's very uh uh very ambiguative and um so and that's of

course this orchestration lo logic that's is a hard thing to build yeah and um and So I think uh overall but now that it become gotten better right now

you can do things like chat with your data right and instead of going to their data analyst business analyst that curates you some dashboards and in 80% of the cases that might be a good enough dashboard but for all the other

20% of the question you always need to go back to your IT department. No, now

you can just converse in natural language with the system. It pulls the data right natural language to SQL or whatever have you pulls that data you converse it until you have that point of view of the data that you want to have

and then you just pin it and you say like okay that's actually my problem now I want to manage that problem for the next I don't know two three weeks until the problem maybe has disappeared and then of course you you you you move on

maybe then you delete that tile uh and so on and so forth like so this this kind of um combination of the structured unstructured world which is required right if if you want to go into the

tabular world, right? Because lots of data in finance is stored in tables and sales and in the supply chain and so on and so forth, right? Um unlocking that

took a little bit of time, but uh now it's actually we are seeing through for example the knowledge graph, the SAP knowledge graph that we've built, which is kind of the glue between natural language and the structured data in the

system to to to really bring this together. That actually leads to one of

together. That actually leads to one of your um I I think like I don't know if it's unconventional, but it's certainly not the the dominant narrative in AI

right now, which is your interest in like predictive and tabular um models.

Uh can you can you talk about like you know why LLMs aren't the be all end all here? why we can't just use um uh

all here? why we can't just use um uh tools and um uh calculation external to the model in com in combination with LLM to achieve what you want to achieve.

Yeah. Now first of all from a business motivation it's a great question right Sarah I mean first from the business motivation point of view right again LLM's unstructured world that's all good

right but most of the time if you if you want to plan forward right if you want to make good decisions in a company you need predictions right you need predictions in terms of oh what's my

demand right for oh is this depending on the seasonality effects and so on what's my demand forecast maybe right for my products in the retail store or what's my demand right uh for my product so I

can plan accordingly my manufacturing right if I'm a manufacturing customer or you want to predict your cash flow right uh you want to predict and that has a bunch of input variables like oh what

are actually my data sales outstanding right and that is determined based on are customers paying yes or no that's a classification question and if you then say okay if a customer is not paying in

the in within the payment terms what's the payment delay a classical regression question and so so on and so forth. Now

the problem is of course still today if we look at these predictive questions right and then you want to maybe do a what if analysis from it right now if you want to do these predictions quite frankly then the challenge is large

language models are not made for this right the way how they you know generate just one token after another essentially in a sequencetosequence modeling I mean they're language models right so that

and they do this phenomenally well but if you still want to do these predictors when you have to go back to these classical machine learning approaches, right? You use XG boost or auto gluon

right? You use XG boost or auto gluon and and many of these AutoML approaches, right? That that might be that that are

right? That that might be that that are still out there. The problem is just it doesn't scale, right? So, we haven't seen in the predictive space the same level of democratization, right? You

still need to hire a very good talented data scientist, right? And then if you, for example, if you're a large company, we did this for example at a pharmaceutical company. If you just want

pharmaceutical company. If you just want to solve the payment delay prediction problem I've mentioned, right, you have they they are running in 90 countries around the world and they need

these two models. So you end up with 180 models you need to train. You need to create the data. You need to train the models, figure out right what the right model uh is feature engineering like the

classical uh machine learning kind of approach, right? That uh that that was

approach, right? That uh that that was used in the in the past. And what we said all the time is okay look we have all this data in stored in these tables right hundred thousands of tables right

where all this information is stored can we not apply the same idea that large language models or multimodel models did for the unstructured world actually for the structured in order to start predicting things. So you can just

predicting things. So you can just basically provide a little bit of context, a small amount of data, not a large amount of data because that was always a problem, small amount of data and then starting making high accurate

predictions so to speak in that domain.

And that led actually this was two years of research. We published that also at

of research. We published that also at Europe and a bunch of other conferences.

Uh we call this RPT1. So rapid one stand for relational pre-trained transformers.

It's still based on the transformer architecture but with a very different uh uh architecture. uh we released this and we see some some some meanwhile some very very good results from that in in

various domains where as I said classification and regression sometimes time series and so on uh are concerned and we believe this will be huge because it obviously will allow way more people

from a business impact to uh to to make these predictions which large language models have a really hard time with when you um

uh think about the gap in uh I don't know I think you described it as like hype versus adoption within the enterprise customers like uh the innovation race there versus the

outcome race innovation race versus outcome race um uh it's a it's a good framing like the change is happening very quickly

that's hard for companies to absorb where where do you see um challenges for the enterprise and adoption today and where are customers making the most progress with with you or were they most

excited?

Yeah, it's a good good question, right?

I mean, usually I say the the the primary problem as I said is is is the problem of a data, right?

Because most of the time the data is of course very disagregated in a in in a company, right? I mean, for a variety of

company, right? I mean, for a variety of reasons, right? either because you made

reasons, right? either because you made certain decisions uh how you purchased uh solutions in the past or you did an M&A right so you acquired a company naturally of course they bring a very

different IT system landscape as well and so on and so forth right so you have this segregated information and the problem is of course that limits the potential of what you can actually do with AI right and and then the question

next is how do you integrate this safely and what I see is clearly customers who did that kind of homework right now of course It's not a new topic. We're discussing

this for 10, 15, maybe more years, right? The ones that did their their

right? The ones that did their their homework, they of course have a much easier life, right? To then also reap the benefits for example of AI, right?

The second one as I mentioned is already is the problem of scale, right? The

bigger the more complex the landscape and so on, right? Then of course also then bringing this together in a unified experience is a challenge. And then

finally, of course, everything around then security and so on and so forth, right? because then there's always then

right? because then there's always then this gap between oh there's an amazing innovation take open claw for example right I mean amazing uh what what what this has brought to the world in terms

of further ideas and of course I mean from a security perspective that's so that's a problem you don't want to run this like just like it is there on GitHub and deploy this in your

organization do something I mean this is nobody would ever do this right so then of course you need to bring this make it secure I mean we have seen with light LLM How long is this now ago? Two weeks

or something. You probably saw it, right? Like with this vulnerability that

right? Like with this vulnerability that still all of a sudden steals all your keys and credentials and so on and so forth like and that you don't want right if you're the chief information and

secure the security officer in the company right you don't have a job anymore right and that's and that's of course another big challenge from an adoption perspective as well. What do

you uh think the um uh function of a finance or an HR or a supply chain team that would have been

operating out of SAP in their day-to-day work um you know a year ago? What do you think that looks like a few years from now if they're successful if you and

your customers are successful with the um AI transformation? Yeah, I mean first of all it's very simple. They will get rid of a lot of the mundane work right like collecting information and

preparing powerpoints for decision making and so on. So what we going to see is a much much faster way of making decisions making better decisions right and then of course automating the

mundane work. So what the people will do

mundane work. So what the people will do is they will run more scenarios. they

will run get better deeper insights in a much faster way in order to then really think about we always call it this more strategic thinking right and kind of in a way Sarah if you will for me this the

same way like like everybody who works today maybe in the finance shared service center right it's for me the equivalent of a junior developer today with cloud code so now they actually

become they got one level higher right they're now not so much anymore uh um tasked with then writing a lot of the code right with with with codec or with

cloud code, but they actually then start supervising the code, give feedback, right? And capture of course the essence

right? And capture of course the essence of what the code should look like and then you know do much more review and then rather think about what to build next, right? Think about the next

next, right? Think about the next requirement and how is that actually differentiating. So it will like every

differentiating. So it will like every every role every level will kind of get upleveled so to speak, right? because

the the the work that's being done today will be pushed down to will be pushed down to these agents, right? And there

by therefore I think we I believe in general what we will see is that um people will just achieve so much more because there is a lot of intelligence baked into the system that gets rid of

of many of the things that we're that we're doing today and that are actually uh well at least in many cases not a lot of fun.

I I must admit my ignorance here. I

don't I'm I'm thinking about this and I want to talk a little bit about the impact on the business if you're right as well. Um I don't actually know how

as well. Um I don't actually know how SAP prices broadly today but the question would be like how do you price and if you are you know delivering more

outcomes for customers or serving them you know services software in a different way do you think that changes the business model for SAP?

It does absolutely I mean there's there's no question and we have prepared for this already. So for me it was always very clear. I mean for the most part SAP software is seatbased licensed

uh uh uh today with a few exceptions like a conquer or field glass for example or the business network. Um but

you know very clearly with AI it was very clear for us that you know step by step it will go towards this consumptive world right at first consumptive and

then maybe in the next step uh once we have more verifiability in the system then also towards maybe an outcome based uh license model uh to for example what

Sierra is do doing and so on and so forth. Um but the reality is also it is

forth. Um but the reality is also it is today for us it's a hybrid model. It's

consumptive but it still has a certain element of seats in there and so on and so forth because also it's a joint journey with the customer because the the customer is saying they are not yet ready

in many cases uh for a purely consumptive model right because they need predictability right but then of course they are not yet fully also everywhere trusting the outcome right

and or know then also of course is the value already there but then they are afraid of that the costs may explode from a consumptive perspective etc etc ETA. So what we so what at the end of

ETA. So what we so what at the end of the day what we have designed is a hybrid that is basically ready for this consumptive world but actually meets the customers where they are today knowing

that they demand still a lot of predictability in the enterprise space in order to cost control uh the the whole thing for themselves as well.

That makes sense. It's unclear how I also believe that transition is going to happen. It's unclear how quickly it

happen. It's unclear how quickly it will.

Exactly. No, absolutely. I agree. I mean

nobody knows this and at first you see customers that are more you have have a wide range right there also of opinions right and of course some customers are a

bit more forwardleaning already and then others are more uh um still asking or demanding a classical model so to speak and so therefore it's a it's um it's a

journey.

What do you um let me let me rephrase that for a second. when when you look forward uh and think about SAP's

position 5 years from now and you compare it to the uh broad market pivot away from SAS and software uh in terms

of just how investors are valuing these businesses and their enthusiasm about their durability. Um my own opinion is

their durability. Um my own opinion is like the challenge is real and yet it will affect these uh the the incumbent software companies like very differently right there will be winners and losers

versus like universally like everybody's market cap come down what do you think is going to be characteristic of a winner or why does SAP get to endure again

I think at the end of the day it's all about adoption and the outcome you bring to the customer right I mean the technology look the reality Reality is for most companies the technology

doesn't matter right like I always tell tell to my developers all the time our job at SAP is to make the technology disappear right we need to get the outcome in front of the customer and of course not just the value itself of

course you also be able to produce and price it in a way so it's a win-win situation for the customer right and and and and of and of course the vendor right at the end of the day so what what we are really trying to do and this is

also why we you know from an architecture we have so flexible right we said like we Don't overindex on a specific L. We have partnerships with

specific L. We have partnerships with all of them, right? And and and really al only invest in the things that are actually differentiating for our customers versus the things that anyway

will likely get commoditized uh in in the tech stack and then try to make sure that we of course bake the enterprise qualities in and the integration is there and the customers can turn these

capabilities on almost instantaneously in order to benefit from it. Why is this important? Because in order because if

important? Because in order because if you take a lot of time to reap the value then your return on investment is essentially gone right and or or it becomes the the business case becomes

harder right and therefore what we are really focusing on is to to deliver these outcomes for the customers and I think that will differentiate the winners from the losers at the end of the day to really focus on the business outcomes for the customer at the end of

the day.

As we wrap up I want to ask you a a few quickfire questions including a little bit more personal one. Um, uh, our listeners always want to know like what do you do all day as like a CTO of SAP?

Like can you just describe how you spend your time?

Well, I spent most of the time reviewing uh the progress with the teams, right?

And we're thinking along, you know, from all the layers with the teams, from the database to the models, right, to the UI, review the progress, give guidance, feedback, learn something new, study, of

course, what happens outside, uh do a little a lot of prototypes, right? While

we speak here, I have a bunch of command line interface instances running here, prototyping a bunch of things, right?

Trying things out. uh see what works, what doesn't work, right? And then give this also as as as kind of inspiration uh back to the team.