The Tech Sales Newsletter #94: The SAP comeback
Source: SAP Investor Relations
It's no secret that the author of this newsletter has a strong preference for the cutting edge in tech—both in terms of capabilities and go-to-market strategies. As such, there has been little coverage of what I consider "legacy" software companies. The funny thing is that they can still be very lucrative and interesting places to work, assuming you land in the right business unit, territory, and under the right local sales leader. It's just that being part of those organizations would typically reduce your odds of doing something exceptional later because you'd mostly be surrounded by B and C players.
Every now and then, however, an agent of change might be able to climb the ranks of a legacy institution and weaponize it as an extension of their ambition. If Salesforce's pivot toward AI has been mostly about Marc awakening from his slumber and getting interested in the industry again, for SAP the pivotal player in their journey of scaling into cloud and AI has been Christian Klein.
The key takeaway
For tech sales: SAP is a soft recommend—they have a great install base and a CEO who has pushed hard in the right direction. Quality of life is likely to be low for the next 12 months.
For investors: The stock bottomed at $81.50 in September 2022 and recently passed above $300. While Oracle has doubled in the same period, it's not difficult to see that markets are starting to pay serious attention to what's happening, and there is significant payoff potential down the road if they achieve rule of 40 following their GTM revamp. The obvious issue here is that the stock has already run quite aggressively as large players accumulated—they are more likely to dump on you (sorry, I mean "de-risk positions"), which will likely limit upside versus alternatives in cloud infrastructure software.
In his own words
Before we look at SAP's recent performance and the tech sales opportunity there, let's first get a measure of the man based on his own point of view. We’ll use a recent interview with Ben Thompson (thank you Kyle for introducing me to his work) at Stratechery:
CK: When I became Chief Operating Officer of SAP, I was responsible for our IT, and obviously we are running SAP solutions, our ERP, our CIM, everything. And I felt, “Oh, I have to transform this company”, and the ERP plays an instrumental part, because in the cloud you sell differently, code differently, you service your customers differently. I had a very homegrown, customized ERP, a very complex system, extremely hard to upgrade. It served our needs very well, but it was not on the latest and I had all of this great new technology, we will definitely talk about AI in a second, but I was eight years behind the latest release and I thought to myself, “Hey, this business model, highly profitable, highly successful, will not lead SAP into a bright future, because there are all these best of breed competitors coming in”.
All of these lessons which I learned, I brought into my role as CEO and said, “Hey, look, wait a second, we are running here a good business, but we have to disrupt ourselves because customers need way more agile systems. They need the latest innovations. They don’t need to spend hundreds of millions of upgrading a system, they need to be on the latest”.
Now what CK is talking about here is a good insider view of how most legacy companies are starting to warm up to "going to the cloud"—by finally understanding that if you want to run the best version of the software you've invested in, it's smartest to let the vendor actually deliver that for you continuously over time. The biggest handicap of most CIOs and CTOs today is thinking that wasting engineering time running outdated on-premises software is some sort of great efficiency play.
CK: Now, AI, Ben, to your question, of course, in 2015, 2016, we already had our first machine learning modules and so on, but what I didn’t like is we played the “me too”. We played the “me too” of other tech companies’ offering on our platform machine learning services. You could build your own modules, but when you are SAP, think about that — you’re running the most mission-critical business processes of the world, you have access to so much business data. So you have a suite, you have mission-critical data, we have to embed AI right into the business processes of our customers so when you do financial planning, you don’t have to code another AI module for that. We are actually infusing the intelligence into your financial planning so that you can simulate right away the impact of tariffs on your financial guidance, or when you then run a supply chain and you demand in supply chain planning, don’t code with Gen AI or traditional AI, another AI app embedded right away into our solution so that end users can use it out of the box. Obviously they can do some fine-tuning, but this is what SAP can do and as we have the business data other than the consumer-driven LLMs, that is what differentiates us.
When everything in a big corporation ends up being an "efficiency and cost control" decision, it becomes very difficult to actually innovate and change your product in a way that remains competitive. SAP had all the resources in the world to try and create whatever the future product play for replacing ERP would be. Instead, they opted to keep milking the install base and offer the most basic functionality that other vendors delivered much earlier (and much better). This is the first time in a while when somebody is in charge and wants to win with better technology, rather than incumbent advantage.
CK: Now in the next step with AI and our new offering, Business Data Cloud (BDC), we are saying suite-first because we have the very mission-critical SAP data. Then Ali Ghodsi, the CEO of Databricks, once reached out to me and said, “Hey Christian, I have so many SAP customers and your data is the most mission-critical in their company, but I have a lot of non-SAP data and I feel we are creating all of this data in big data lakes and then very expensive data scientists are coming to make somehow meaning to this data, but we can do this way smarter”. I said, “Absolutely, Ali, we can do this way smarter, why we are not building this one data layer where we can semantically match SAP structured with non-SAP also unstructured data?”.
The SAP-Databricks partnership is easily one of the most aggressive plays in the industry today. The corporate boilerplate:
CK: SAP Databricks is an SAP-managed version of the Databricks Data Intelligence Platform embedded natively within SAP Business Data Cloud. SAP Databricks brings industry-leading AI/ML, data science, and data engineering capabilities together with semantically rich, business-ready data from SAP applications. Leveraging zero-copy Delta Sharing, companies can seamlessly integrate semi-structured and unstructured data from any source with data products from SAP applications into a single, harmonized data model—eliminating complex data movement with seamless access to high-quality, reliable data.
Now what that really means is that you basically log into your SAP portal, then click on SAP Databricks and it opens a serverless instance of the Databricks Data Intelligence Platform, branded under an OEM. Databricks users now get access to SAP data within their data lakehouse architecture, while SAP users get access to a cutting-edge modern data warehouse without having to contract and implement a new tool.
CK: Yeah, the two companies have a very compelling and a very complementary portfolio in bringing this together. What I actually see coming out of that, and by the way, we are going to see also more partnerships in that space, but what we are going to see is actually a marketplace of data products when, for example, now many customers are coming to us and said, “Okay, great. SAP you make sure, first of all, that in all countries in the world I have a compliant business because you can translate tariffs into all transactions what we are doing. That is a big benefit, but now help me to simulate the impact of tariffs on my business. How should I deal with price changes? How should I deal with inventory change?”.
Then with BDC and Databricks and SAP, you can build data products. You can say, “Okay, let’s use external data, let’s use some SAP data”, bring it together to simulate the impact of tariffs on your production plan, on your financials, on your pricing, etc. So what we will create is a marketplace of data products, this kind of semantical data layer where customers can pick and choose data products to have an immense value either on the steering, on how they run their business, on the decision-making, but first and foremost, and also of course on AI, because the agents will also have access to this data and coming back to my point, you need the business data, it’s not enough to only have non-structural data. You need this business data, you need it combined with the unstructured data and then the agents can become world-class.
The most important thing is that extremely valuable data is now available to data science teams to start running ML jobs against it and ultimately use it to drive advanced AI applications. As CK puts it:
CK: That is now the evolution in the data game that we are really building this one data platform and we started this with Databricks and it’s very exciting, because then when you think about it, about AI and agentic AI, we are of course building our modules with SAP data, so far but suddenly an AI agent can talk about cashflow collection also based on non-SAP data, because maybe some of your sales data, your commercial data is sitting somewhere else and that is of course also super, super important to make our AI even more powerful. When you want to do data engineering on the Databricks side, same there, you can have access to these semantical data products and you can really create AI based on this semantical data layer with BDC.
The approach of rethinking existing parts of the SAP portfolio and reimagining them from the perspective of "what's the most valuable play for my customers" is playing out in a number of areas:
CK: The Business Network actually, we brought the Business Network in with an acquisition we did with Ariba. The Business Network was there, you are a buyer and you have your suppliers, and you can digitize the transaction, you can digitize the documents. Great, you have much less paperwork, you are more productive, you can procure faster, good.
But then COVID hit, and in times of COVID, a lot of our customers came to us, take for example the vaccine, a lot of pharma bioscience companies came to us and said, “I have no clue how to get all of the ingredients to my factory in a certain location because I have no visibility in my supply chain”, and they said, “But Christian, wait a second. You are running us, but also probably all of our suppliers procure and buy with SAP software”, I said, “Yeah, that’s right”. I have millions of suppliers in my network, so what we started to do for pharma, but also for automotive, we connected the suppliers. So suddenly, for example, take a vaccine, you can see all your suppliers lined up in your supply chain down to the raw materials and really down to the very single ingredients to really track and trace, “Is every supplier able to deliver at what time?”, and, “Can we also make the logistics work”, which was super important in COVID, but still today. Think about tariffs, you also want to understand where to procure from to avoid getting too hard hit about tariffs.
Now, providing essentially an interconnected digital marketplace between suppliers and primary manufacturing companies is not a new idea—most of the big players in the space have some ghost-town version of it. AI is the real unlock for this process, as it could add automated agentic workflows that would trigger and execute key buying processes within this network.
CK: On the infrastructure, on the commodity level, we decided indeed to rely on the hyperscalers, because they have a very powerful offering, they are multinational. We still have our own data centers, by the way, we have 50 data centers around the world, but we give customers choice. Why? Because we believe it’s not so strategic for SAP. We can partner and partner and our customers love to having the choice. Maybe you have already Microsoft 365 and you want to combine your workloads on Azure, we give it to them. Maybe you already want some of your data in BigQuery so you can combine everything on GCP, and the same with AWS. I feel this is a winning formula that we are saying, “Look, we are open for these partnerships”, but what we also of course do, Ben, and especially in this geopolitical times we are having right now, which are by far not easy. Of course we have various sovereign cloud offerings in the EU, in the United States, in China, in Asia, so we can give our customers all what they need and focus our R&D on the stuff where customers really see us on the platform and then on the application layer.
SAP choosing to go aggressively on AWS/GCP/Azure marketplaces is not an obvious choice. Companies like this have a strong preference for maintaining independence in offering services (keeping their large datacenter estates running). The smart players in the industry have of course realized a while ago that all IT spend will consolidate on cloud marketplaces.
CK: Where we are going with SAP and what our strategy around AI is, I definitely believe the best-of-suite is now even more important, and that the end of the best-of-breed is pretty close. Because why? You can have of course as an end user have access to thousands of copilots and digital assistants, etc., but at the end, the way how business runs, business runs across end-to-end business processes.
Think about a process like when you source-to-pay. You source, you need to find the right supplier, you put up an order, you actually pay the supplier, you want to have it connected to your supply chain, and this is SAP. So the best-of-suite matters and because you need the data, you need the business context, that’s the suite.
Then when you put a Joule, a digital assistant on top and say, “Hey, you know what, Joule? I want to source a supplier for this material, give me a choice of supplier based on cost, based on availability to deliver quality, etc”, we have all of this data. We are not only having procurement data, we have the suite data and with BDC, we have sometimes then also external data. Then Joule, our digital assistant, can help to source. But Joule then and our agents then in the supply chain can also take this new material and say, “Okay, supplier will deliver by date X, let’s make sure that it goes to our manufacturing team so that they are able to understand how do they do the factory planning, the shop floor planning”.
So you see how important the suite is so that a digital assistant can really work across. When you think now about agentic AI, I mean all of these agents, they are great, but the agents need to be able to talk to each other, to run across end-to-end processes. At a certain date, I’m pretty sure we will have see all agents wanting businesses, processes end-to-end, and for that you need the data, you need to understand the business context and there is no need absolutely to put another layer on top of more agents, we can embed that right away in the businesses of our customers.
Now this is a great litmus test for whether Christian "gets it" or is a performative CEO who had one obvious observation about the monetization of cloud infrastructure through on-premises SAP deployments.
The definitive tech sales guide to selling AI (LLMs and Enterprise-grade Machine Learning).
Most legacy corporations today are trying to launch their own little copilot as the "trusted" source. At the board level, this is met with a lot of distrust both in terms of compliance/privacy, as well as just the practical cost scaling of such an approach. So what they end up doing is either essentially a wrapper around an initially competent model that gets replaced later with a worse, but cheaper performer, or they run a fine-tuned open-source model on their infrastructure.
Due to how quickly the space is moving, both approaches show a lack of understanding of where the opportunity lies, i.e., let customers adapt quickly to the changing ecosystem through model choice. If they have made the choice to start using AI tools within their vendors, odds are that there are at least some competent leaders on the customer end who have done their due diligence and have a basic understanding of how quickly things are moving.
Source: SAP AI Core
SAP has taken this approach by building a managed service with a self-trained model for the most fundamental workflows, while leaving the ability to deploy agentic workflows or use third-party LLMs natively (both open-source and from leading players such as OpenAI, Anthropic, etc.). This happens in close collaboration with the hyperscalers (it integrates via their LLM-as-a-service workflows and uses their infrastructure).
CK: I would say it’s not monolithic, it’s a modular ERP, but it’s sitting on one platform. So you have modules, you have the agility, you can decide to consume HR, then you go to Finance, then you go to Supply Chain, you go to CIM, Sales, Commerce, you do this step-by-step. It’s not monolithic, but there’s still a platform underneath that gives you the out-of-the-box integration, the integration of your workflows, the integration of business processes, the integration of data. On top, we are building these agents who then can use this high quality data and then Joule who actually becomes our UX because in the future, there is no need anymore to type data out or get data out of an SAP system, you are actually running SAP.
So Joule will become our new UI, and it’s very much you use SAP via your human language, but there is not anymore this need to type data in, get data out, screen documents. That is what Joule can do for you in the future.
Now if you believe that it's in your best interest to educate and onboard your customers on the cutting edge of AI within your legacy corporate platform, then it's not surprising that Christian has already realized that the legacy interface of an ugly dashboard will likely evolve into a multi-modal interaction with an agent. At the end of the day, users want to be able to tell the thing to do the thing, not spend hours clicking around and failing to achieve the task.
CK: I’ll give you a real life example. Last week, I was in Switzerland, talked to a company. They are producing elevators and they said, “Christian, even the elevator business, it’s changing a lot, we want to package services to that, we want to infuse our AI for predictive maintenance. But guess what? Our frontline, our sellers, they configure something and our supply chain, our people in the supply chain functions have no clue about what has been configured at the frontline. Help me, I want to get rid of some of the best-of-breed, I want to go to a best-of-suite where my sales, where my services engine work seamlessly together with my manufacturing, with my logistics engine”.
So we are bringing this together in the cloud, modular, but then on top we say, “Hey, let us infuse AI. When you configure a new elevator, our configurator should tell you what is the best service package? What is the best price? Are you able to deliver at that time? Let the AI agent for sales do this job for you”. Then we transfer the data to the fulfillment functions of this company, and then it says, “Hey, there’s new demand coming and by the way, do we have everything on stock? How does this configuration need to be translated into the factory line?”, then we ship it and then we already plan the shipping. All of that will be done on one data module, consistent workflows and AI embedded to automate and to drive this way more intelligent than in the past, and this customer told me, “Christian, you know what? I get rid of four or five best-of-breed software produts because I would definitely believe in the best-of-suite, because I know I grow better and I actually be more productive if I want this end-to-end in the core with SAP, obviously there will be also non-SAP, but in the core, this is where I need the suite”.
Don't play games with us, Christian—we all know it's Schindler Group.
Still, it's a good example since it captures the essence of why AI-powered platforms will consolidate heavily in their industries—small players, even if best-in-class, are unlikely to be able to compete with the deep integration that agentic workflows will be able to drive.
If you are on the bullish side of AI, it's not difficult to jump to the next logical argument, which is that at some point those agents will outsmart the developers in the best-in-breed companies anyway.
The overall play
Let's take a look now at how this is all being packaged from vision into execution.
Source: SAP Sapphire Financial Analyst Conference (5/21/2025)
CK: Everything what we are going to show on stage is either real or will be delivered in the next six months. For me it's very important financially but also product wise that what we promise we deliver. And so if we then go into the transformation so far promising but deliver and execute, I guess we have proven that over the last five years and it's super, super important that we always keep our promises. And when you think back about five years and you saw yesterday the keynote, I mean behind the scenes when I saw the suite in action, I mean that is a lot of work which stands behind the financial numbers which has happened, which is a proof for very good execution also inside SAP. I mean the BTP, the seamless data module, the seamless integration where we can now show why is it better to run with a modular suite versus best of breed.
The reason why legacy corporations become, well, legacy, is typically an issue of vision, rather than execution. At the end of the day, they didn't get as large as they did by constantly failing. So there is technical muscle (or the means to hire one), but it requires somebody with a vision and the will to power.
Source: SAP Sapphire Financial Analyst Conference (5/21/2025)
CK: And everyone knows exactly what is my role, what do I have to do to further execute on the strategy to make the company win. When we then start with product innovation, I mean, yesterday you heard me talking about the flywheel. I guess a lot of what has happened in the last four years actually brought us to the strong position we are in. Because imagine what we explained yesterday on tariffs. I mean I have customers talking to me here at Sapphire but especially also over the last months who said, thanks to SAP I can react very quickly, very fast, real time to any updates in the geopolitical market to any kind of new tariffs hitting the market?
What about shipping the products from one place to another place within a certain timeframe to not get hit by tariffs? Or how can I change my production planning to actually avoid getting hit by tariffs also in the near term future? And what about my financial plan bringing all of this together and what can I say to my analysts and to my investors? And that is of course, it's very important that you have a system, a solution landscape which talks to each other. And that is the integration what we have driven over the last years.
And then what differentiates us today is the flywheel we were talking about. About apps, data and AI. And I don't want to go now into detail into this again, but it's pretty simple. And I have to say I'm very, very happy that we didn't focus so much on all of the hype on large language models but we focused our energy, our R&D investments on our AI foundation.
And our AI foundation is all about business data. Because we have so many apps, we have so much data, which then at the end feeds into AI. And that's why we believe around our embedded AI and tool, SAP is truly differentiating because we have this flywheel which no one else has.
Now the funny thing here is that SAP doesn't need to actually execute at the level of what I personally deem top-tier in the context of highly technical cloud infrastructure software companies. What they need to deliver is better integration and outcomes than what all of these non-tech-oriented companies can produce on their own. From an investment perspective, consolidating on SAP then becomes a no-brainer with the added security, compliance, and reputational protections that come from that.
Source: SAP Sapphire Financial Analyst Conference (5/21/2025)
CK: Simplification is of course high on Sebastian Steinhauser's agenda, but let me quickly allude on how we believe we can become a rule of 40 company. I mean the first thing is around sales and marketing. There I would really say our ratios were not best in class. I hope you see now in the recent quarter, you have seen that we are getting better and better. And what can we do further to improve the scale of our go to market engine?
First, channel. I mean 400,000 customers, great, but there is much more potential. So we can really expand their territories for our resellers by a lot. And we can scale with very high margin by giving more and more business to our channel. Second, Commercials.
I mean you wouldn't imagine how much time a seller spends to quote, to price. And when you look at our CPQ and you look at Gartner, we are right at the right top. So we have a world class CPQ system. We are now infusing AI. We are now replacing some of the legacy.
And our sales people have much more time, 30% to 40% to spend with customers instead of working with quoting wise deals and quoting all kinds of deals inside of SAP. And then finally when it comes to marketing, you can feed a lot of dollars into a marketing machine.
You can create pipeline and you can celebrate your pipeline volume every day. It just doesn't matter so much if the conversion is not good. So what we are doing is a lot of work between also especially our digital hubs to say, hey, how can we make the lead conversion better?
What are the best marketing channels for that kind of business? How do we address our buying centers in a much better place? How do we also then get better in lead conversion and qualifying leads? And there's a lot going on because every dollar you spend on marketing is a lost dollar if you finally only convert 3% of it. And this is of course a massive project what we are running inside SAP.
Now while this is not some revolutionary playbook pitch, it's clear that Christian is starting to ask some serious questions around sales execution and will apply the same vigor in applying change here as he did on the product side. The goal is to drive the whole company to a higher performance level, not just innovate for customers who are willing to pay and then falter on sales execution across other verticals. "Net new" is the name of the game, and SAP wants to go after it.
Now, speaking of GTM, attainment on RepVue sits at 45%.
Source: RepVue
Source: RepVue
Source: RepVue
The overall feedback is that their Large Enterprise accounts (LE) remain a very lucrative opportunity, but most of the hiring is in either difficult territories or Mid-Market, where SAP is still going through a transformation.
Realistically speaking, the GTM team is going through a reshuffle and will take a while to become an "obvious" great opportunity. The most obvious risk here will be aggressive layoffs as the company pushes toward higher productivity and new skill sets. As they started hiring for teams that are responsible for positioning the AI capabilities of the platform, mandatory requirements such as passing onboarding tests before being even allowed to speak to customers have become commonplace.
The play here is either to enter now with the expectation of a chaotic first 12 months, or wait it out until a cascade of LinkedIn "open to work" posts starts to pop up.
I think that from a product perspective within what we can consider "traditional" SAP customers, there is an obvious upselling opportunity which the right talent will be able to capture over the next 3 years. That doesn't mean that the opportunity will be "easy"—there is no easy money in this market anymore and it will only get more difficult as the performance expectation rises while OTE remains similar.
