उपशीर्षक (547)
0:00Hello and welcome back to Beyond
0:03Resilience, our series of conversations
0:06exploring what is next for enterprise
0:09success and adaptability.
0:16Our guest today brings the perfect
0:19perspective to navigate these changes.
0:22From my 25 years of experience
0:25implementing AI, I have learned that
0:28staying ahead requires three key
0:33The first one is technical expertise.
0:37Then a human centric focus and finally
0:41an entrepreneurial mindset.
0:44Stefan Deus from SAP embodies exactly
0:50Stefan, welcome to the show.
0:52>> Yeah, thank you. You know, excited to be
0:56>> Could you tell more to our audience
0:59about you and what gets you excited? I
1:01>> mean, I'm Stefan uh Dutch uh I'm the
1:04president of the global business at SAP.
1:07So in essence that means you know the
1:08global go to market of our application
1:11portfolio our data data platform and our
1:14AI capabilities is where I hold a global
1:17responsibility for and you know today
1:19we'll deep dive in in the business how
1:21it comes to life you know what are
1:23practical examples and also what are
1:24some of the innovation that we are
1:26seeing what gets me excited is is
1:29applying technology to deliver business
1:31value to our client base because
1:33ultimately you know tech for the sake of
1:35tech is not So exciting, but the moment
1:38it has, you know, true impact, you know,
1:41for our customers and they see that
1:42impact back in their business results,
1:45that gets me excited.
1:46>> Yes. I love this. And I think, yeah,
1:48business imperatives and business impact
1:53uh any action with technology. And
1:55that's that's exactly what you said.
1:57>> Yeah. No, 100%. And look, you know, that
1:59that's, you know, with technology, we're
2:02often, you know, talking about, you
2:04know, AI and new innovation, but it
2:06should never be for the sake of
2:08innovation. It's all about, okay, but
2:10how does that actually help you solve a
2:12particular business challenge that an
2:14organization is facing? And then if you
2:16solve that challenge, you know, can we
2:18see that back in the P&L or the balance
2:20sheet in terms of, you know, true value
2:22delivered. So yeah, that is what it is
2:25>> Completely makes sense. You have an
2:29entrepreneurial journey and you've been
2:32at different startups and this is quite
2:35remarkable because you've been driving
2:37revenue, you've been driving rapid
2:39scaling and such transformations that
2:42you've driven uh require incredible
2:46resilience and you know we talk a lot
2:48about resilience in in this show.
2:50>> Yeah. How has that experience influenced
2:53you uh in the way you help your
2:56customers today at SAP?
2:59>> No, absolutely. You look the you know
3:00the the first you know 10 years of my
3:02career I was in finance and supply chain
3:04and then the past decade in tech and
3:07especially the past seven years sort of
3:09going from scale up or startup actually
3:12you know 50 people couple of clients to
3:14then scale up with more than 3,000
3:16people was just an you know interesting
3:19experience and then coming into an
3:20organization like SAP which obviously
3:23you know well established uh you know
3:25very loyal customer base um you know an
3:28amazing product portfol folio good
3:30people uh was just interesting to see
3:32the difference between you know uh
3:34startup scaleup phase to you know
3:36company like SAP but I I would have to
3:38say I find that the way we operate and
3:42the way we think there's a lot of things
3:44from the startup scaleup scene that I'm
3:46seeing and the reason is all about the
3:49innovation right there's massive
3:50innovation around data there's massive
3:52innovation around AI agentic things like
3:55that so how do you then apply a startup
3:57mindset to then solving you know
4:00problems but do that you know at pace do
4:02that jointly with customers uh and make
4:05sure that you know we continue then to
4:07deliver so I find um it is different but
4:11also actually there's a lot of
4:13commonalities in terms of how we think
4:15and you know how we how we drive success
4:18>> the mindset the mindset of of a startup
4:23>> to for innovation and solving problems
4:27>> yeah and ultimately look at The startup
4:28it's simple, right? You have an idea and
4:30you say, "I want to solve this problem
4:32for you." And you say, "Hey, can I help
4:34you solve this problem?" So, we solve
4:35this problem for you. You like it. You
4:37tell your neighbor, you know, and and we
4:39try to, you know, go around, you know,
4:42and tell some other people. But
4:44ultimately what you often see in
4:46startups, it is that customer obsession,
4:49right? That is an obsession of solving a
4:51problem. And then the moment you do
4:52that, well, obviously, you know, startup
4:55becomes scale up, scale up becomes a big
4:56enterprise. But I think now what we are
4:59seeing given the speed of innovation the
5:02only way for a big company right to to
5:05keep actually operating like they do
5:08today is to you know get into that
5:11mindset of you know caring deeply about
5:15you know customers caring deeply about
5:17solving their challenges and then making
5:19sure that you know you apply the latest
5:21technology to do so and we'll talk more
5:23about that but I think I love it
5:25>> you know that's that's how both worlds
5:26are coming together in my mind
5:28>> a craft mind shift
5:33>> Iteration iterations with clients trying
5:36and making sure you you get to to fit
5:38their needs at the end of the day.
5:40>> Uh with the flexibility and the mindset
5:42of an entrepreneur. Love it.
5:44>> Uh you shared a bit about SAP business
5:49>> and that being a pathway to resilience
5:54So, it's a big topic, you know, here in
5:56our conversations on on this show. Um,
5:59and you like to use a phrase about it
6:01that I will always remember. You talk
6:04about best of sweets rather than best of
6:07breed. Can you tell us more about this?
6:09>> Yeah. Yeah, absolutely. And ultimately,
6:11you know, let me break down the business
6:14because the way we envision the business
6:19of apps, data, and AI coming together.
6:22Now, why a flywheel? You know, in
6:24physics, a flywheel is all about
6:26individual components that are coming
6:28together to create new energy
6:29>> and we believe that holds true in
6:31enterprise software as well. Now, it
6:33starts with the application layer. So,
6:36the apps that we provide uh run the end
6:39to end business processes of an
6:41organization. So, think about you know
6:43there's the core ERP but then there's
6:44finance, there's supply chain, there's
6:47procurement, there's you know customer
6:49experience, there's HR and so forth. So
6:51every single core business process is
6:54run in an application. If I just take
6:56supply chain, if you would double click
6:57on that, there is planning, there is
7:00manufacturing, there is logistics, there
7:02is warehousing, there is asset
7:04management and so forth. So SAP has the
7:07broadest portfolio of apps, you know, of
7:10of all software companies in the world.
7:12But these apps do not only run end to
7:15end business processes. They also
7:17generate a lot of extremely valuable
7:19data. Now that data is what we combine
7:24with non SAP data in what we call the
7:27business data cloud that does a couple
7:29of things. The first thing it builds a
7:32reliable and trusted source to do
7:36>> And second is often times why does AI
7:39fail or why do transformations fail
7:42because companies haven't solved for the
7:44data problem. So I think you know that
7:46is extremely important that there has to
7:48be you know a data foundation that then
7:51supports organizations when it comes to
7:53AI. Now what is extremely important is
7:56the notion of what we call semantically
8:00>> because it's very easy to pull data from
8:02different systems but you lose the
8:04semantics. So give you an example simple
8:06a purchase order or a sales order. If
8:09you would have to pull that from SAP
8:10systems you now need to pull data from
8:13you know 15 different tables but then
8:15you need to recreate the logic that that
8:18is a sales order purchase order or you
8:20know anything else. So often times
8:23companies spend an enormous amount of
8:25time and effort in pulling data from
8:27different applications trying to
8:29recreate the semantics but now the data
8:31is disconnected from the source
8:33application. the data is disconnected
8:35from the end to-end business process and
8:38again it it takes an enormous amount of
8:40time on getting getting it ready for AI.
8:43>> So apps data then comes AI. Now what I
8:47found very interesting you might have
8:49seen that uh MIT they uh released this
8:54uh article and said hey 95% of Gen AI
8:58>> Everybody talks about it.
8:59>> There's a couple of things to that that
9:00are interesting. I think a lot of
9:02companies are trying to treat AI like a
9:07separate layer somewhere in the
9:08technology stack. So it's disconnected
9:10from your end to end business processes,
9:12it might be disconnected from your data
9:14strategy, it's something you know
9:16somewhere but the moment it doesn't make
9:19it back to the end to end business
9:20process context is very very difficult
9:23to drive value. So one of the strategies
9:25from SAP from from early on when it
9:28comes to AI is to embed AI back in the
9:32core application. Now think about this
9:35in an you know finance you want to get
9:38to autonomous closing and you want AI to
9:41help you book acrals. So that is an
9:43activity that needs to make it back to
9:45the core application. In supply chain,
9:48you want to create AI to predict future
9:51demand so that you can then synchronize
9:53you know manufacturing and things like
9:55that has to make it back to the core
9:57application. Procurement uh think about
10:00you know how do I get to autonomous
10:02sourcing has to make it back to the core
10:04application. So we do not believe that
10:07there is some wrapper somewhere out
10:09there. It has to come back to the to the
10:11core application and that's why we are
10:13saying there's an app layer there's a
10:15data layer and then there's embedded AI
10:17in the application and agents that sort
10:19of come together to deliver a much
10:22better sort of enterprise technology
10:24experience to our customers but also
10:27very much focused on you know solving
10:29some of the business challenges. So the
10:30insight that is created at AI level
10:32comes down back to the app level where
10:35people are working currently and people
10:38can see this insight and and work with
10:40this insight in mind and yes
10:42>> and and make better decisions.
10:44>> Yes. And those three components of the
10:46flywheel always have to work together.
10:47Maybe give you an example working
10:49capital. So let's say you're the CFO uh
10:54>> and um I work in your team and you would
10:57ask me you know Monday morning Stefan
11:00you know how's our cash conversion cycle
11:02doing and at that moment in time I
11:05should be able to use software to
11:07immediately tell you hey cash conversion
11:09cycle is going up so your follow-up
11:11question hey Stephan that's not great
11:14why is it going up so I should now at my
11:16fingertips have information is this
11:18inventory is this payables Is this
11:20receivables? Now let's say it's
11:22receivables. These I would say are
11:24insights that you today might be able to
11:26get from some reports. But now the
11:28question is what do I do with the
11:30insights? So let's say it's receivables.
11:32Your follow-up question then is okay
11:33Stefan where? So let's say in North
11:36America I have a segment of customers
11:38that is paying me late or is at the risk
11:42of actually not paying me at all. Then
11:44your question is okay Stefan will this
11:47continue? What do I do about it? So now
11:49it comes interesting. Now you need
11:51historical payment information. You
11:53might want to pull some data, interest
11:55rates, GDP growth, um you know some
11:59credit ratings from external agencies.
12:01Then you need to apply an algorithm
12:04>> to then predict cash conversion cycle
12:06going forward. Now assume we do all of
12:09that and the cash conversion cycle is
12:10predicted to go up. Then your question
12:12is okay what do I do about it?
12:14Assurance, some factoring, whatever it
12:17is. And that is how the flywheel should
12:19come together right where apps working
12:22capital app comes together with data SAP
12:26data non SAP data then apply AI to make
12:29a prediction and based on that
12:31prediction we want to act and we want to
12:33act now so I think you know that is how
12:35that flywheel comes together in an
12:37endto-end business context
12:38>> what do you see as other trends more
12:42specifically in AI uh for the clients
12:44that you you're supporting
12:46>> yeah so first and foremost I I mean the
12:48embedded AI that makes it back to the
12:51core application uh is something that
12:53clients really really appreciate because
12:55they also understand that this sort of
12:57wrapper on top uh I mean by MIT research
13:01right is is not really working. So I
13:03think embedded is one.
13:04>> Second is there is a lot of excitement
13:09>> Yes. Because agents can not only you
13:13know do a whole series of activities in
13:16an autonomous way but they can connect
13:19one business process with another one.
13:21So they can also you know break down
13:24silos. So give an example there's an
13:27agent on the commercial side that can
13:29help us predict which deals we will
13:32close and which deals will not or what
13:34we should do to actually get those deals
13:37closed so that we can increase in sales.
13:39But if we increase sales, we need to
13:41make sure that we can actually deliver.
13:43So human is a manufacturing company. We
13:45need to have manufacturing capacity. We
13:48need logistics and warehousing capacity.
13:50We need you know raw materials or
13:53components uh readily available. We need
13:55to make sure that our suppliers can
13:57deliver those and so forth. So if you
13:59just think about an entire value chain
14:01from getting a product in the hands of a
14:03customer to sourcing the components that
14:07has to be orchestrated by a series of
14:10agents that can help you know
14:13organizations get to you know better
14:16decisions and improve business results.
14:18So what we are seeing is an increase in
14:22the need for agents. uh customers want
14:24to work together with us to get there
14:26but they also understand that this has
14:28to be uh sort of across you know
14:31business processes and I think the
14:33excitement around AI when it comes to
14:35you know productivity uh that's sort of
14:38fading right yes we are all using the
14:40different LLMs and yes you know we all
14:43get to information you know faster but
14:46an LLM per se or you know the sort of
14:50productivity AI use cases are not
14:52necessarily necessarily impacting
14:53topline. They're not necessarily
14:55impacting you know working capital of an
14:58organization and they're also uh not
15:00necessarily impacting the cost profile
15:01of an organization. So now the key
15:03question is and that's you know where we
15:06are in the forefront of you know working
15:08together with our customers to deliver
15:10aic experience across different business
15:12processes that then ultimately leads to
15:16you know proper you know business
15:18results in in a pen of a company.
15:20>> I love it. So it's about um uh
15:24collapsing all those silos that we've
15:26seen in companies from I mean typically
15:28traditionally from for centuries
15:31and having those agents working across
15:34those different functions
15:36um they don't know silos they are agents
15:39and% the other big trend in AI UIUX is
15:44changing fundamentally
15:46so you know a lot of organizations they
15:48have you know thousand 2,000 different
15:50applications and users you know logging
15:53into the applications and doing all
15:55kinds of work but that is not how we
15:59interact with software in the future so
16:01we have a capability that we call dual
16:04at SAP so see that as a new UIUX the
16:07super orchestrator it's it's a you know
16:09check GPT perplexity like sort of
16:13>> so you ask questions but you also give
16:16instructions but now you as an as a You
16:21don't have to log into five different
16:22applications to do something that is
16:26being orchestrated by Juul. So the way
16:28we start thinking about interacting with
16:31software becomes different. Stefan, I've
16:33seen so many companies struggle with
16:36legacy software with on premises systems
16:40and the pain is real. I mean slow
16:42processes, high cost, constant
16:44breakdowns. How can SAP solve for that?
16:48>> Yeah, absolutely. So obviously you know
16:50a lot of our customer base you know
16:52understands that in order to consume
16:54innovation at pace and at scale you have
16:58to you know bring your core applications
17:01and your data capabilities in the cloud.
17:04>> Uh so there is you know long list of
17:07clients that understood that you know
17:10that went early and are now the ones
17:13that can actually consume you know a lot
17:15of the AI innovation. Now obviously
17:17there's still a group of clients that is
17:19still on prem and still not in the
17:21cloud. So obviously the way uh to get
17:24there is to move to the cloud and in my
17:27mind it's like okay you know let's get
17:28the core foundation to the cloud and
17:30then you know there's a lot a lot of
17:32innovation uh that we can work on
17:35>> Stefan all this is very exciting but big
17:39question comes to my mind which is what
17:41is next? What's next to business suite?
17:44What's in your mind in your vision? We
17:46believe that the app layer will be
17:51>> Yes. Now very very similar to actually
17:54the infrastructure layer while critical
17:57uh got commoditized by the cloud we
17:59believe the app layer will be
18:00commoditized by AI that means
18:04>> especially by agents
18:05>> I mean ultimately from an end user
18:07perspective yes we would still need apps
18:10to orchestrate the workflow and the end
18:11to end process but the value sits in the
18:15data and in the AI layer. So the future
18:17of the business suite will be we commit
18:20to have the best possible embedded AI
18:22experience you know in every single
18:24application. Um you know we have around
18:27400 use cases already embedded in the
18:32>> Uh we absolutely want to be a winner in
18:34the agent space. Uh we also believe that
18:38that is something that we will do more
18:39and more with partnerships with other AI
18:42companies. uh and then we absolutely
18:44envision that that front end user you
18:48know experience uh will be conducted
18:51more and more on duel uh so we believe
18:54it is extremely exciting times to be at
18:56the company uh we're also you know
18:59getting amazing feedback from clients
19:00that are working with us you know hand
19:02in hand to truly embark on this business
19:06v journey and then ultimately I you know
19:08we started this conversation it is not
19:11technology for the sake of techn
19:12technology. It's not an agent for the
19:14sake of an agent. It is AI innovation to
19:18deliver a better outcome for the
19:20businesses that we serve. And I think
19:22that is really uh the future and how we
19:25envision that is yes we are doing all
19:27those things but ultimately we do that
19:30because we want to deliver you know
19:32better topline you know reduce cost and
19:34improve working capital for our customer
19:37>> It totally makes sense. Thank you
19:40Stefan. Moving beyond resilience means
19:44looking toward the future.
19:47And as we close, what is the one message
19:50you would like to leave the audience
19:52with about the possibilities of business
19:55street for their own businesses? Yeah,
19:58the main message is it is absolutely
20:01possible and what we are finding from
20:04obviously you know dealing with you know
20:06four 400,000 plus customers that we have
20:09is the customers that have this curious
20:13mindset that you know throws you know
20:15these big hairy problems that say hey
20:18you know I want to reimagine you know
20:20how I conduct my business they're
20:22absolutely you know seeing the benefits
20:24and are you know full on the journey at
20:26the same time there's obviously also a
20:28group of customers that is you know
20:30slower when it comes to adoption of new
20:33technology maybe more conservative
20:36um but but I do believe that we are
20:38going to see a divide between companies
20:41that say hey I'm going for this you know
20:44I need to innovate I need to change the
20:46way I conduct business and I have a very
20:49very strong point of view that they will
20:50definitely be winners in the marketplace
20:52so I think the key message that I would
20:54have is hey innovation is
20:59>> You know, be part of it.
21:01>> I love this. That's really a powerful
21:04message and and a message that I believe
21:08every leader should carry with them as
21:10they plan for for the future of their
21:13Stefan, thank you for joining us. uh
21:17your examples made it clear how SAP
21:20turns values like innovation and agility
21:23into real practical outcomes for
21:28>> We appreciate it and um you know great
21:30being here and great catching up with
21:32>> I also want to thank you for joining us
21:34for these conversations. I hope you have
21:37learned as much as I have and that you
21:40live with a clear vision of adaptability
21:42built not just on resilience but on
