Generative AI helps people to write, research, program, or evaluate content faster. A huge benefit, but one that also comes with a serious problem: according to a McKinsey study, while 80% of AI users report increased personal productivity, only six percent of companies say that AI significantly increased their operating profit.

• How can this gap be closed? The answer lies in helping companies detect risks, defaults, losses, and liquidity pressures sooner—and manage them more effectively.

For Germany and Europe, it is not an abstract challenge, but the key to future prosperity. Our economic strength lies in industry: in mechanical engineering, in the automotive supply chain, in complex manufacturing and logistics processes. According to German Economic Institute estimates, AI can generate around €440 billion of additional gross value in Germany by 2034.

Good, but not in every case

To get the economy moving again, we need a different kind of AI. Because it is not enough to process and generate language; AI must also be able to deal with data that companies work with the most. And this is where common large language models (LLMs) reach their limits. Structured data—for example, numbers in tables—help companies understand what is happening right now and what may come next.

This requires AI models that understand the relationships among rows, columns, entities, and numbers. This is exactly what current research results show: several independent benchmarks from the year 2026 prove that even high-performance LLMs perform significantly weaker in table reasoning tasks—for example, in the ability to draw conclusions from tables and to recognize relationships—than in language tasks.

When data anticipates what’s next

Tables follow their own logic. Values do not stand next to each other in isolation, but in relationships between columns, rows, timeframes, and business transactions. Accordingly, tabular AI is designed to recognize patterns across columns as well as in the associated tables that make up a company system. The real added value lies in this networked context. Looking at a customer ID in isolation is one thing. Seeing which orders this customer places and predicting warehouse requirements from these data is a completely different thing.

A practical example: a manufacturer wants to identify early which customers are most likely to pay late. For this, a single piece of information is not enough. Among other things, relevant factors include payment history, order history, delivery data, and external industry information. Only the interaction of these data makes reliable predictions possible. That’s precisely what tabular AI models are built for.

What this means for AI investments

Classic machine learning methods are still used in many companies today for predictions based on structured data. They often deliver good results, but typically have to be developed, trained, and operated separately for each individual application: one model for demand forecasting, another for payment default risks or for prioritizing sales contacts, each with data preparation, training, monitoring, and ongoing maintenance.

Tabular foundation models change this equation. They do not have to be redeveloped for every business question but are universally applicable. This creates an immense scaling effect. In the future, instead of hundreds of specialized models, a single one—such as SAP-RPT-1.6, which we have just made available to customers—will suffice. With this, companies can build on historic examples and existing patterns from their business data in order to identify new risks and opportunities at an early stage.

For companies, this means a concrete shift: AI predictions will no longer have to be developed as complex individual projects in the future. In other words, the path from a specific question to the universal use of a model for any questions becomes shorter and cheaper.

In addition, since tabular foundation models are not pre-trained on individual use cases, but on large amounts of data, they can also recognize patterns across different use cases that specialized individual models, especially in limited data layers, often overlook. Therefore, in addition to the scaling effect, there is also a gain in knowledge, which previously did not exist in the analytical processing of tabular data.

New category for enterprise AI

At SAP, we are working on the next level of enterprise AI. With the acquisition of Prior Labs in July, we are further accelerating this path: combining Prior Labs’ basic research with the corporate reality of our customers, and the experience of SAP in the application of AI in business-critical business processes.

With TabPFN-3, Prior Labs has developed one of the most powerful open-source models for tabular predictions. Together with the tabular foundation models of the SAP-RPT family and SAP Knowledge Graph, this creates an infrastructure that securely generates structured predictions directly from the company-specific data with the individual business context.

In general, now we are seeing a trend toward various types of foundation models. In addition to tabular foundation models, models that have been trained on the basis of time series—for example, a large number of measurements—are being created. You can evaluate the measurements, recognize patterns, and thus also predict numerical values over time.

Conclusion

The first wave of generative AI is making employees more productive. The next one can make companies more forward-looking. The decisive factor here is not whether companies use LLMs or tabular foundation models. It is crucial to combine the two where they provide the greatest benefit: LLMs for interaction with language and knowledge, tabular models for forecasts based on structured business data.

Done right, AI becomes more than a productivity tool. It becomes a driver of better business decisions—and a foundation for a stronger economy.

To learn more, read this recent interview with SAP CTO Philipp Herzig (in German).

When SAP issued a challenge to its partner ecosystem ahead of SAP Sapphire in 2026, it set an ambitious goal: 100 to 200 partner-built AI agents before the event opened its doors. What happened next surprised everyone.

By the time SAP Sapphire kicked off in May, partners had delivered over 680 agents built on SAP Business AI Platform—more than triple the original target.

For SAP Chief Partner Officer Karl Fahrbach, the number was not just a data point. It was a signal. “That talks about the power of the ecosystem,” he said.

That momentum is now being channeled into the next challenge. SAP has since raised the bar, setting a new target of 1,000 partner-built agents ahead of SAP TechEd. This time, building an agent is not enough. The focus is to deploy partner AI agents at SAP customers, providing them real business value with measurable business outcomes.

Redefining what “good” looks like

Behind this fundamental change, SAP is challenging traditional notions of growth and redefining the path to partner success. For years, the dominant metric was transactional: how much did a partner sell, how many projects did they deliver. That model, Fahrbach said, is no longer sufficient: “We need to start measuring the partners on real customer outcomes, not how many projects they deliver, but what the customer has really achieved: cost savings, top-line growth, or process efficiency.”

This reflects a broader change in customer expectations. A year ago, the question customers were asking was: what can AI do? Today, it is: what business outcome can AI deliver? The experimentation phase is over. “That’s what we’re hearing from many customers, and it’s exactly where partners have an important role to play,” Fahrbach said.

The shift also extends to how customer outcomes are delivered. “Customers don’t want large implementations anymore,” he explained. “They are increasingly seeking AI to help them run faster and leaner with their projects so they can get to value much sooner. That’s the perfect foundation for becoming an Autonomous Enterprise.”

The €100 million fund: early traction

A €100 million investment fund SAP announced at SAP Sapphire for partners that build and successfully deploy AI agents is now drawing submissions. The fund is designed to both accelerate customers’ adoption of AI and attract a new generation of AI-native companies into the SAP ecosystem. Demand has been significant, with nearly 1,000 customers expressing interest. As of October 1, more than 500 customers had been confirmed, and more than 400 partners had signed up to participate.

“We’re seeing strong interest from new partners in the fund,” Fahrbach confirmed. “It adds another element of appeal for new partners to join the already strong and thriving ecosystem.”

The evidence for that pull was already visible on the SAP Sapphire show floor, where Fahrbach noted a shift: a new cohort of AI-native startups appeared alongside the established system integrators—companies that were, in his words, “born in the AI world.”

But the initiative is not only intended to bring new partners into the fold; it also reflects SAP’s commitment to the partners that have helped build the ecosystem over decades. Fahrbach said SAP is committed to helping all its partners enhance their AI capabilities and capitalize on emerging opportunities to thrive in an AI-driven market.

Where partner agents will live

While many SAP partners have already developed agents for SAP, it’s now about making those agents visible and deploying them with customers. To date, over 120 partner-developed agents are published on SAP Discovery Center, and by the end of this year, the number is forecasted to rise to more than 300 partner AI agents, showcasing the power of the SAP ecosystem.

One of the structural changes shaping the ecosystem concerns how customers find and provision partner-built agents. SAP has created a seamless discover-to-operate experience by bringing partner agents directly into the SAP AI Agent Hub, which plays a central role for agent discovery, governance, and lifecycle management alongside SAP’s native agents. SAP AI Agent Hub is free of charge for customers managing up to 500 AI agents, and already governs agents across hundreds of companies.

“SAP AI Agent Hub allows customers to view SAP and partner agents in a single marketplace,” Fahrbach said. “They can browse, select, and provision partner-developed agents as easily as they do SAP’s own. Partner agents are integrated into the experience on equal footing with ours.”  

The push toward SAP TechEd

To reduce barriers ahead of SAP TechEd, SAP is offering a range of incentives to its partner ecosystem. Qualifying partners can receive complimentary conference passes, enabling developers and consultants to gain hands-on experience with SAP Business AI Platform.

SAP is also providing free access to the new Joule Studio solution for the SAP Early Adopter Care program customers through the end of 2026. More than two dozen partners are currently contributing to the SAP Early Adopter Care program. Joule Studio introduces a new paradigm of intent-based development, enabling users to create applications and solutions by describing what they want to achieve, rather than writing code manually. This significantly lowers the technical barrier to application development and accelerates innovation.

A key attraction at SAP TechEd will be the Agent Lab, where partners can gain hands-on experience building with SAP’s latest technologies, including Joule Studio. The goal is to move beyond product demonstrations and enable participants to build and experiment with real solutions.

“We want developers and consultants to come to SAP TechEd and gain real, hands-on experience with the platform,” Fahrbach said. “Not just to observe, but to build, experiment, and leave with something they can immediately put into practice.”

The push to accelerate partner enablement is supported by an already significant skills investment. The SAP ecosystem now includes more than 200,000 cloud-certified consultants and over 38,000 business AI-certified professionals, creating a deep bench of expertise as the company expands its AI ambitions.

To help partners navigate quality standards, SAP has introduced a partner validation program for SAP Business AI Platform with certification and development requirements, and a review process that agents must pass before they qualify for distribution.

Where the differentiation lies

Increasingly, the value of AI is being strengthened through industry differentiation by partner, with AI that understands not just a function like procurement or finance, but procurement in automotive, finance in pharmaceuticals, or supply chain in retail. Generic AI may drive productivity, but industry differentiation by partner drives competitive advantage. That is where partners, with their deep knowledge of specific sectors and micro-verticals, can create the most differentiation.

An AI strategy built on partners

With the fund now active, a 1,000-agent target on the board, and partner agents embedded directly in the SAP AI Agent Hub, the stage is set. SAP TechEd will be the first major test of how quickly the ecosystem can translate that vision into tangible outcomes. The message from SAP is unambiguous: the future of its AI strategy will be built in partnership.

“SAP cannot do it alone,” Fahrbach says. “Our partners are essential. They provide the speed, scale, and specialized expertise needed to bring AI innovation to SAP customers.”

Artificial intelligence can do far more for public administration than answer questions or digitize individual interactions.

Its real potential emerges when AI connects with the data and processes behind public services, turning a citizen request into coordinated action across the administration.

For public institutions, there is another essential requirement: innovation must go hand in hand with transparency, accountability, and digital sovereignty. SAP combines decades of process expertise with enterprise applications, data, AI, and sovereign cloud technologies.

S.Public, the new SAP Experience Center location for the public sector, brings these capabilities to life. It officially opened in Berlin on October 12, 2026.

From a citizen request to coordinated action

Damage in a public space can be reported digitally in a matter of moments. But that is where the actual administrative process begins: determining responsibilities, planning the work, checking resources and budgets, procuring materials if needed, and closing the case. SAP solutions can connect citizen engagement, maintenance, procurement, and financial management across this end-to-end process.

At S.Public, a playground scenario makes this tangible. A citizen notices damage, such as a broken swing, and reports it by smartphone. The steps that follow show how a single report becomes coordinated action. The real innovation is not the reporting form itself, but the intelligently connected processes behind it.

When AI understands the administrative context

The same principle applies to public funding. Applications involve documents, eligibility requirements, and financial information. AI can check submitted materials for completeness and consistency, flag missing information early, and provide employees with an explained recommendation for further review. The final decision remains with the responsible public authority.

A funding application for a solar installation illustrates this approach in Berlin. The SAP capability behind it is broadly applicable: AI is embedded where employees evaluate information and manage processes. Rather than automating isolated tasks, it supports the broader administrative context.

When digital sovereignty meets AI

How can sensitive data be used with AI while remaining under an organization’s own control? SAP Sovereign Cloud addresses four dimensions of digital sovereignty: data, operational, technical, and legal sovereignty.

At S.Public, this concept becomes something visitors can actually walk through. The new Sovereign Cloud Showroom makes SAP’s security and sovereignty concept physically tangible for the first time. Visitors pass through three security zones: administration, control, and restricted zones. They experience firsthand how the operating environment for SAP Sovereign Cloud and the protection of classified information work together.

A public sector scenario at a seaport shows how this can be combined with intelligent analysis. SAP solutions and the AI assistant Joule bring together selected data from internal and external systems in a sovereign environment. Data on vessel arrivals, terminals, containers, and available inspection capacity is turned into a prioritized overview. Analysts can query it in natural language, while the responsible public officials decide which inspections to carry out.

More than a place for demonstrations

The three examples point to a common development: public services become more intelligent when applications, data, and AI work together across organizational and functional boundaries while accountability remains clear.

S.Public creates a place where customers, partners, and experts can explore and discuss these possibilities. The SAP capabilities behind them extend far beyond Berlin.

The future of public services will not be determined by how much technology public administrations adopt, but by what they enable for people and society.

Andre Bechtold is president of SAP Industries & Experiences and chief revenue officer of Industry AI at SAP.

Major security incidents can quickly affect enterprise value, operational continuity, regulatory standing, and reputation.

As organizations become more dependent on connected infrastructure, cloud services, AI, and third parties, weaknesses in one part of the ecosystem can cascade across the business.

A Board is not expected to make technical decisions. Its role is to exercise informed oversight: set expectations, test whether accountability is clear, require evidence of resilience, and ensure that management integrates security and compliance into strategy, investment, operations, and crisis preparedness.

The following questions are designed for a Board of Directors and its relevant committees. They broaden the original cybersecurity focus to include compliance, privacy, data governance, third-party assurance, regulatory change, and demonstrable control effectiveness. 

1. Are security and compliance roles, decision rights, and escalation paths clearly defined?

Treating security or compliance as an isolated specialist function weakens governance. Accountability should extend across executive leadership, business operations, technology, legal, risk, privacy, internal audit, and the security organization. The Board should understand who owns material risks, what is the risk appetite who can accept them, and how significant incidents, control failures, vulnerabilities, or regulatory concerns reach the appropriate committee or full Board.

Evidence to request:

• Does the security and compliance leadership team have direct access to executive management and the Board?
• Are material risks escalated before they materialize into active incidents?
• Do responsibilities cover prevention, detection, response, recovery, regulatory obligations, and assurance?

2. Are security and compliance built into strategy, investment, and transformation decisions?

Security and compliance should shape strategic choices from the beginning, not be added after commitments have been made. Major investments, cloud programs, acquisitions, new products, AI initiatives, and supplier selections should be assessed alongside security and compliance considerations. Early involvement helps identify structural risk while decisions are still practical to change. 

Evidence to request:

• Which strategic decisions require a documented security and compliance review?
• What risks or control requirements influenced the final decision?
• Are funding and accountability aligned with the risk created by the initiative?

3. Do we have a transparent, decision-ready view of security and compliance risk?

Boards need more than technical activity reports. Management should translate important threats, vulnerabilities, control gaps, legal obligations, and business dependencies into clear enterprise risk scenarios. Reporting should show potential operational, financial, legal, and reputational consequences, as well as the residual risk that remains after controls are applied. 

Evidence to request:

• Which scenarios could materially affect our strategic objectives?
• How are likelihood, business impact, and control effectiveness assessed?
• What residual risks have been accepted, by whom, and for how long? 

4. Is our compliance strategy prepared for regulatory change?

Compliance should be managed as a continuing capability rather than a periodic response to individual requirements. The Board should assess whether regulatory developments are monitored, interpreted consistently, assigned to accountable owners, and translated into controls, technology roadmaps, evidence, and reporting. This includes requirements affecting security, privacy, data sovereignty, operational resilience, AI, and third-party services. 

Evidence to request: 

• Which emerging requirements could materially affect the business?
• How quickly can new obligations be translated into operational controls? 
• Can management demonstrate compliance consistently across regions, business units, and technology environments?

5. Are legacy systems and technical debt creating unacceptable security or compliance exposure?

Systems that are no longer fully supported can accumulate security weaknesses, limit monitoring, complicate recovery, and make compliance evidence harder to produce. The Board should view aging critical systems as an enterprise risk and investment issue, not just an IT lifecycle matter. Where modernization is delayed, management should document compensating controls, ownership, and a time-bound plan. 

Evidence to request: 

• Which critical systems are out of support or approaching end of support?
• What business processes and regulatory obligations depend on them?
• What is the modernization plan and how is interim risk independently validated?

6. Are AI security, compliance, and governance embedded in the operating model?

AI introduces security, legal, privacy, data, ethical, and operational questions that require coordinated governance. Whether AI is developed internally or obtained from a third party, management should know what data it can access, how outputs are used, which decisions remain subject to human review, and how systems are tested and monitored as they change.

Evidence to request:

• Who is accountable for approving and monitoring AI use cases?
• What controls protect sensitive data and prevent unauthorized use?
• How are legal, ethical, security, compliance, and human-rights considerations evaluated and evidenced?

7. Are identity and access management strong enough for our risk profile? 

Compromised credentials and excessive access can allow attackers or insiders to reach critical systems and data. Identity and access management should be treated as a business control, not simply an administrative technology process. Strong governance connects employee, contractor, third-party, and system identities to reliable business records and removes access when it is no longer required. 

Evidence to request: 

• Are strong authentication and least-privilege access applied to critical systems?
• How quickly is access changed when roles, employment, or supplier relationships change?
• Are privileged and non-human identities monitored, reviewed, and time-limited where appropriate? 

8. Does our culture measurably reduce human and conduct risk?

Policies and technical controls depend on people making sound decisions. A mature program goes beyond annual training by addressing role-specific exposure, leadership behavior, phishing and social engineering, secure ways of working, speak-up channels, and consequences for repeated noncompliance. The Board should seek measurable evidence that behavior and risk are improving. 

Evidence to request: 

• Which roles face the highest security and compliance exposure?
• What do simulations, incidents, training results, and behavioral indicators show over time?
• How visibly do leaders reinforce expected behavior and accountability?

9. Are third-party and supply-chain risks continuously assessed and governed? 

Security and compliance obligations do not stop at the enterprise boundary. Critical suppliers, cloud providers, software dependencies, advisers, and other partners can create operational, regulatory, data, concentration, and geopolitical exposure. Third-party risk should be integrated into enterprise risk management and monitored throughout the relationship, not treated as a one-time procurement exercise. 

Evidence to request: 

• Which third parties are critical to operations, data, or regulatory obligations?
• What independent evidence supports their security, compliance, and resilience claims?
• How are concentration, subcontractor, geographic, and exit risks managed? 

10. Are we prepared to detect, contain, disclose, and recover from a major incident? 

Prevention alone is not enough. The Board should understand whether the organization can identify significant events early, contain their impact, make timely legal and regulatory decisions, communicate credibly, restore priority services, and learn from the event. Readiness should cover ransomware, account takeover, data compromise, fraud, third-party disruption, and other plausible business scenarios. 

Evidence to request: 

• When were crisis, incident response, business continuity, and recovery plans last exercised together?
• Are decision rights clear for regulatory disclosure, law enforcement, ransom demands, customer communication, and service restoration?
• What gaps were found in recent exercises or incidents, and are remediation actions closed?

From reactive oversight to demonstrable resilience 

These 10 questions help a Board move the discussion from technical activity to governance, accountability, evidence, and business outcomes. Effective oversight requires an ongoing dialogue with management, constructive challenge of assumptions, and decisions that reflect the organization’s risk profile, regulatory obligations, and digital maturity.

Modernization can be an important governance lever when it improves standardization, transparency, control automation, resilience, and the ability to produce reliable compliance evidence. Responsibility, however, remains shared across leaders, business owners, technology teams, security, compliance, legal, risk, audit, employees, and critical partners. 

Board takeaway

Security and compliance are not separate from business strategy. They are essential to continuity, lawful operation, stakeholder trust, and long-term value protection. 

Marielle Ehrmann is chief security officer and a member of the Supervisory Board of SAP SE.

Note: This piece has been adapted from the German article “Cybersicherheit – Zehn Leitfragen für den Aufsichtsrat” by original authors Dr. Friederike Rotsch and Sebastian Lange alongside Marielle Ehrmann.

In many B2B organizations, creating a customer quote still involves a familiar sequence of emails, approvals, contract reviews, and manual handoffs. The process can take days or even weeks, creating delays for customers and additional work for employees.

Ericsson knows this challenge well.

As a global leader in telecommunications technology operating in more than 190 countries, Ericsson manages highly complex commercial relationships involving products, services, customer-specific contracts, pricing agreements, and global fulfillment operations. Behind every quote is a web of business rules, approvals, and dependencies that must be coordinated accurately and efficiently.

For years, that complexity created friction in the quoting process. In some cases, generating a quote could take more than two weeks. Today, many of those same requests can be completed in minutes or even seconds.

The hidden complexity behind every quote

At Ericsson, many customer transactions are tied to multi-year framework agreements that include negotiated pricing, contract-specific products, and customer entitlements. Sometimes customers return months or even years after an agreement is signed and need to understand what products, configurations, and pricing are available to them before placing an order.

Historically, that process often required customers to contact their account representative, who would manually gather information, validate contract terms, and prepare a quote. Depending on the complexity of the request, responses could take days or even weeks.

Ericsson’s objective was straightforward: make it easier for customers to do business with the company.

“We want to be easier to work with as a partner to our customers,” said Aaron Thomas, global product owner at Ericsson.

Using SAP Commerce Cloud, Ericsson introduced digital commerce capabilities that allow customers to access contract-specific products, generate quotes, and place orders through self-service channels. Customers can now access dedicated storefronts built around their agreements, browse available products, generate quotes, and complete transactions without relying on manual intervention from account teams.

What once took days or weeks can now be completed in minutes or even seconds, helping improve accuracy and reduce rework across commercial operations.

Beyond automation: Exploring the role of AI agents

Once the quoting process became digitized and streamlined, Ericsson began exploring how AI could take commercial execution to the next level.

“We’ve moved from manual processes to digitized and automated processes,” Thomas explained. “Now we’re asking: can an agent perform those tasks?”

Rather than using AI solely as a productivity tool, the company started evaluating how AI agents could actively participate in commercial processes.

One area Ericsson is exploring is quote creation. The company is evaluating how AI agents could analyze customer requests, identify relevant contract information, assemble recommendations, and prepare proposed quotes for human review.

Employees remain responsible for oversight and decision-making, while AI helps reduce administrative work and allows sales and account teams to spend more time advising customers, solving business challenges, and strengthening relationships.

The company is also exploring other AI-driven capabilities, including agents that can improve product descriptions and search experiences across its internal product portfolio, helping employees find information more quickly across a large and complex catalog.

Defining the next chapter of B2B commerce

For Ericsson, AI is becoming more than a productivity tool. It is creating new possibilities for how commercial work gets done.

Reducing quote creation from weeks to seconds delivered immediate value for customers and employees alike. But the company’s exploration of AI agents points to a broader shift, one where intelligent systems help businesses execute complex processes faster, more consistently, and at greater scale.

For Ericsson, the journey from weeks-long quoting cycles to AI-assisted execution is about more than efficiency. It is about creating a simpler experience for customers and helping employees focus on the work that matters most. As the company continues exploring how agents can support commercial operations, it is helping redefine how complex B2B processes can be executed in the age of AI.

To learn more about Ericsson’s approach to AI and the future of agentic commerce, watch the webinar: Agentic B2B Commerce: Finding the fast track to profitable growth. You can also explore SAP’s vision for unified commerce at sap.com/unified-commerce and learn how organizations are connecting commerce, customer experiences, and operational execution to drive better business outcomes.

The Germany-based logistics company DACHSER has set itself an ambitious goal: to become the world’s most digitally integrated logistics service provider by 2030, seamlessly aligning its resources, experience, and modern digital operational capabilities with the needs of its customers, employees, and partners.

Following eight months of close collaboration with SAP, DACHSER has reached a major milestone on this journey: the successful migration of human capital management (HCM) to SAP S/4HANA via RISE with SAP.

Nearly a century has passed since Thomas Dachser founded the company in 1930 with a single truck, transporting cheese from the Allgäu region to the Rhineland and returning with steel. Yet, logistics and warehousing remain the core business of the transport logistics company, which is headquartered in the town of Kempten in south-west Germany. “Our core business is what we call our road business, meaning everything transported by road, across both dry goods and food segments,” says Herbert Egner, head of the IT Products Finance/HR division at DACHSER, who has nearly 40 years of experience in software development at the company.

Currently, DACHSER operates globally with approximately 427 company-owned branches across 43 countries. Out of its roughly 37,500 employees worldwide, 19,000 are based in Germany. Total revenue in 2025 reached €8.3 billion. “On one hand, we are a deeply tradition-conscious family business, now managed by the third and fourth generations. On the other hand, we place a massive emphasis on innovation—and not just at the executive level,” Egner notes. “Every single employee should have the opportunity to contribute here.”

In terms of digitization, the company is currently replacing a multitude of small, manual processes with IT-based solutions. Following DACHSER’s licensing of the SAP Business Technology Platform (SAP BTP) in 2024, employees can now leverage its low-code tools to build apps for their own workflows. For instance, instead of using physical clipboards and paper checklists to document damage to transport vehicles, DACHSER inspectors now use low-code applications to capture that information digitally.

SAP BTP and the SAP Integration Suite also played a vital role in helping DACHSER move HCM to the cloud—which, alongside the license conversion to SAP S/4HANA, marks one of DACHSER’s most significant IT upgrades in recent years. “While we are a long-standing SAP customer, we are certainly not your typical SAP user,” Egner explains. “For transport and warehouse management, we still rely on our proprietary systems, which we began developing back in the 1980s. At the time, SAP did not yet offer the specific system depth we required for our logistics processes.”

Finance software from SAP has been in use at DACHSER since 2009, with journal entries transferred from the company’s internal systems to SAP via an interface. In October 2024, the finance solution was migrated from an on-premise environment under SAP ERP Central Component (SAP ECC) and SAP HANA to the Microsoft Azure cloud. As part of RISE with SAP, DACHSER then successfully migrated its HCM payroll and time management modules for its 19,000 employees in Germany to the cloud in June 2026. This intensive eight-month collaboration between SAP and DACHSER successfully established a cornerstone of the company’s global HCM transformation.

“For HCM, we chose RISE with SAP. However, when we decided to move to the cloud three years ago, it wasn’t yet mature enough to meet our specific needs,” Egner says. “Once our three-year contract with Azure expires, RISE with SAP could become a viable model for our finance environment as well.”

The go-live was flawless: the very first payroll run for all employees in Germany in July 2026 went off without a hitch, and all payments were distributed on time. The system then entered a four-week hypercare phase, during which the project team closely monitored ongoing operations before regular support took over in August. With this step, DACHSER has established the necessary future-proof SAP architecture for its HR operations. This opens up new possibilities for the company, allowing it to focus heavily on implementing new features within the SAP SuccessFactors HCM Suite.

SAP S/4HANA transformation

“When we say we want to be the world’s most digitally integrated logistics service provider by 2030, this applies not only to digitizing our processes but also to integrating our acquisitions, which bring their own SAP systems with them,” Egner says. “Under our ‘SAP First’ strategy, we roll out finance and HCM in these countries before deploying our proprietary transport and warehouse management systems.” This approach was recently executed in Italy and is currently planned for several acquired companies in the Iberia region.

DACHSER’s goal is to integrate the SAP licenses of acquired companies into a global enterprise agreement with SAP, regardless of the country in which the systems are running. In the future, this will be handled via a semi-automated workflow. “When it comes to licensing in particular, it is crucial for us to have a unified agreement moving forward on how to manage these assets, ensuring that neither side faces these administrative overheads anymore,” Egner explains.

In connection with the license conversion from SAP ECC to SAP S/4HANA, both companies sat down once again to realign on their future path to mutual success. “Here at DACHSER, we are noticing a new openness from SAP, as well as a deeper understanding of us as a customer,” says Egner. “This has been received very positively on our end. People are working on both sides, and we want to shape this partnership in a way that allows both parties to benefit based on mutual respect.”