The legal disputes between Germany’s largest software company, SAP, and the Munich-based process mining specialist Celonis took another turn last week. After the German Federal Cartel Office most recently dismissed a complaint filed by Celonis in July, the Munich I Regional Court ruled entirely in favor of Celonis in a case regarding the infringement of a European patent held by Celonis.

Case No. 7 O 12849/25 concerned patent EP 3 139 274 B1 describing a method for determining a net lead time between process steps using process data stored in a structured manner in main memory (in-memory).

Celonis had filed a lawsuit alleging that the patent was infringed by a feature of the SAP Signavio Process Intelligence software solution — SAP acquired Celonis’s rival Signavio in March 2021.

SAP must cease use and provide information

The court ruling prohibits SAP from offering the patent-protected method in Germany or using it itself. SAP must also provide Celonis with information regarding the extent to which it has infringed the patent since April 22, 2020, including details on:

• where the patent-infringing processes originated and through which channels they were offered and distributed;
• which customers purchased them, including names and addresses;
• how many access licenses were granted and how much users paid for them;
• for how many users the methods were used per calendar year;
• which other individuals were involved in offering and using the methods.

In addition, SAP must provide a transparent account of the extent of the patent infringements based on its financial figures. The company is obligated to pay appropriate compensation or damages.

“The ruling by the Munich Regional Court confirms our legal position: SAP Signavio’s technology infringes on Celonis’s patent rights. We consistently protect our intellectual property and remain committed to fair competition,” explained a Celonis spokesperson.

SAP says no consequences for its customers

SAP, however, points out that the court ruling pertains to a single, specific feature within SAP Signavio Process Intelligence, not to the product as a whole. SAP Signavio Process Intelligence remains available to customers, according to a company spokesperson.

The spokesperson also dismissed concerns that the ruling could hinder companies’ migration to SAP S/4HANA Cloud. Migrations to SAP S/4HANA Cloud are not affected by this, he said. SAP is working to ensure that there are no disruptions for its customers.

The concern is not entirely unfounded: SAP Signavio is part of Rise with SAP, a SaaS solution from the Walldorf-based company designed to support migration to the S/4HANA Cloud. SAP Signavio’s process mining tools are designed to provide insight into actual process flows and identify inefficiencies and bottlenecks.

SAP will appeal

The Regional Court’s ruling does not mean the matter is by any means settled: As the spokesperson for the Walldorf-based software giant explained, SAP does not share the Munich court’s legal opinion and will file an appeal against the ruling. In addition, SAP has challenged the validity of the patent in question before the Federal Patent Court.

“SAP’s top priority is to ensure that customers are not affected by this. All other issues will be resolved through the appropriate legal channels,” said the spokesperson for the Walldorf-based company.

This article first appeared on Computerwoche on Oct. 13, 2026.

There is a lot of discussion about deploying AI across the enterprise. Most of it starts with the model, the platform or the tool. I start somewhere else: the work.

What decision are we trying to improve? What information does a person need at that moment? What gets in the way today? And what should the technology do when the situation does not fit the standard process? Those questions matter whether the employee is working at a desk, supporting a customer, writing software or managing a shift.

The work is different across those environments, but the technology challenge is connected. AI creates value when it is grounded in the context of the work and built into the workflow. It does not create value simply because it has been deployed.

That distinction is important for CIOs. At UKG, our own employees are primarily desk-based. My responsibility is not to run frontline operations. But we build technology for organizations whose work includes both corporate and frontline employees, so we have to understand both environments. The CIO’s role is to create the architecture, standards and operating model that allow AI to be useful across them.

Start with the outcome

The first question should not be, “Where can we add AI?” It should be, “Where would better information or faster execution change the outcome?”

For a desk-based team, that might mean reducing the time required to find the answer to a customer question, helping an engineer understand a complex codebase or giving a finance leader a clearer view of a business trend. For a frontline manager, it might mean identifying a staffing risk before a shift begins or helping resolve an exception without moving between several systems.

The use cases are different, but the method is the same. Map the workflow end to end. Identify the decisions, handoffs, systems and exceptions. Then decide what should be automated, what should be augmented and what should remain a human decision. If the process does not change, AI often becomes another layer on top of the existing friction.

Build the context layer

AI is not plug and play. A model can generate an answer, but a reliable answer depends on the context behind it: the right data, the user’s role and permissions, the current operating state, relevant history and the policies that govern the decision.

That context looks different depending on the work. An employee in an office may need project, customer, financial or technical information. A frontline employee may need current staffing, scheduling, compliance or operational information. In both cases, disconnected data creates the same problem: The system may be able to produce a response, but not necessarily one that is relevant, trusted or actionable.

This is why I think of the context layer as an architectural priority. CIOs need clear systems of record, reliable data, appropriate access controls and a defined way to retrieve and validate information. They also need to understand the difference between an answer, a recommendation and an action. Those are not interchangeable.

Redesign the work, not just the interface

There is a productivity gap that shows up in many AI programs. An organization deploys a tool, people use it and leaders expect the return to appear. But the work, roles and decision rights have not changed. That is not transformation. It is an additional tool.

If an AI system identifies a risk, who is responsible for acting on it? Can the person closest to the work resolve it? What constraints apply? When does the issue require escalation? A useful AI experience has to answer those questions as part of the workflow.

That requires close partnership among IT, business leaders, HR and the people doing the work. The technology team can provide the platform, integration and controls, but the business owns the outcome. The design also has to reflect how people actually work: different devices, different levels of access, different amounts of time and different levels of technical comfort.

Design with people, not for them

The people closest to the work are an important source of context. That includes an analyst who knows where a reporting process breaks, a customer-support employee who sees recurring issues or a frontline manager who understands what a staffing recommendation would mean in practice.

Representative employees should be involved in mapping, testing and improving the workflow. They should have a clear way to identify an incorrect answer, challenge a recommendation and report when a new process creates unintended work. That feedback is not a change-management afterthought. It is part of building a reliable system.

The CIO also has to be clear about the role of AI. The objective is not to remove human judgment from every process. It is to use technology where it improves speed, consistency and decision quality, while keeping people accountable for the decisions that require expertise, empathy or judgment.

Make the “3 Ts” decision

When we look at an AI use case, I find it useful to make what I call the “3 Ts” decision. Does the work require Talent for judgment or expertise? Can existing Tools solve it efficiently? Or do Tokens create enough value to justify the investment?

The answer will vary by workflow. A high-volume task may be a strong candidate for automation. A complex decision may need AI to surface the relevant information while a person remains accountable. In some cases, the best answer is not another model; it is a simpler process or a better integration.

This is also where CIOs need discipline around measurement. Prompts, licenses, logins and interactions are signals of activity. They are not proof of value. Measure whether the work is better: whether decisions are stronger, cycle times are shorter, errors are reduced, customers are better served or employees can spend more time on high-value work. Then pair those measures with trust and experience signals.

The CIO agenda

If I were evaluating an AI opportunity today, I would ask these questions:

• What business outcome are we trying to improve?
• What context does the workflow require and can we provide it reliably and securely?
• What needs to change in the process, role or decision rights?
• Have we designed with the people who will use the system and the people affected by its decisions?
• How will we know the work is better, rather than simply knowing that the tool is being used?

Enterprise AI will not be one experience for every employee. A desk-based employee and a frontline employee may need different interfaces, information and workflows. But they should be supported by the same underlying discipline: trusted context, clear accountability, human judgment and a focus on outcomes.

That is the work of the CIO. Simplify where we can. Standardize where it creates leverage. Scale what proves valuable. The goal is not to deploy more AI. The goal is to help people make better decisions and do better work — wherever that work happens.

Artificial intelligence has quickly become one of the biggest priorities in the executive suite. Organizations are investing heavily in new capabilities, employees are experimenting with AI every day, and technology leaders are under pressure to identify opportunities that improve productivity and reduce costs.

In many organizations, the first question is, “What can we automate?”

It sounds like the right place to start, but I believe it is the wrong question.

Too often, organizations use AI to automate workflows that were designed years ago for a very different business environment. Those workflows have accumulated unnecessary approvals, duplicate activities, manual handoffs and outdated policies over time. AI may execute those processes faster, but it does nothing to address the underlying complexity.

This challenge is not unique to my experience. In its article, The secret to successful AI-driven process redesign, Harvard Business Review explains that organizations create the greatest value when they rethink business processes before applying AI, rather than simply layering technology onto existing ways of working. Likewise, MIT Sloan’s article, How AI is reshaping workflows and redefining jobs, argues that AI delivers its biggest impact when organizations redesign how work flows across the enterprise instead of focusing only on automating individual tasks.

Those findings reinforce an important lesson for leaders. Before asking where AI belongs, ask whether the workflow itself still makes sense.

Every workflow reflects yesterday’s decisions

Most business processes were never designed from beginning to end. They evolved over many years as organizations expanded into new markets, acquired businesses, introduced new systems, responded to audits or adapted to changing regulations.

Each change made sense at the time. Collectively, they often create unnecessary complexity.

Consider a purchasing process that requires six approvals before an order can be placed. One approval may have been added after an audit. Another may have resulted from an acquisition. A third may have been introduced because one business unit wanted additional oversight. Eventually, those approvals simply become “the way we do things.”

Artificial intelligence can summarize purchase requests, route approvals automatically, notify managers and even recommend decisions. What it cannot determine on its own is whether six approvals are still necessary.

That requires leadership.

The same pattern exists throughout finance, manufacturing, supply chain, human resources, customer service and countless other business functions. Organizations often focus on making individual activities faster while overlooking opportunities to eliminate activities altogether.

This is where workflow redesign becomes essential. Instead of asking how AI can automate each step, leaders should ask which steps continue to create value, and which exist simply because they have always been part of the process.

Sometimes the greatest improvement comes from eliminating work rather than automating it.

Redesign first, automate second

The organizations creating the most business value from AI tend to approach the problem differently. Rather than starting with technology, they begin with the business outcome they want to achieve.

That outcome might be reducing order cycle time, improving forecast accuracy, increasing manufacturing throughput, accelerating product development or improving customer responsiveness. A clearly defined objective creates a much stronger foundation than simply looking for places to use AI.

Once the outcome is clear, the next step is understanding the entire workflow. Many delays occur not because individual tasks are inefficient, but because work passes through too many people, too many systems or too many approval points. Mapping the complete process often reveals unnecessary handoffs and redundant activities that can be removed before automation is introduced.

Deloitte has reached a similar conclusion in its ongoing research on enterprise AI adoption. Its latest State of Generative AI in the Enterprise report highlights that organizations generating the greatest business value are redesigning how work is performed rather than simply automating existing tasks. In other words, they view AI as an opportunity to change how work gets done instead of accelerating yesterday’s approach.

Leaders should also distinguish between administrative work and human judgment.

AI is exceptionally good at gathering information, organizing data, preparing summaries and performing repetitive tasks. People continue to provide the greatest value when decisions require experience, context, creativity, negotiation or ethical judgment.

The objective should not be to replace people. It should be to remove low-value administrative work so employees can spend more time applying their expertise where it matters most.

Standardization is equally important. When every business unit performs the same work differently, AI solutions become more difficult to implement, maintain and scale. Simplifying and standardizing workflows before introducing AI creates a stronger foundation for enterprise adoption while producing more consistent business results.

Finally, organizations should measure business outcomes instead of technology activity.

The number of AI assistants deployed or prompts submitted may indicate adoption, but they do not demonstrate business value. Leaders should instead measure improvements in cycle time, quality, customer satisfaction, operating cost, revenue growth and employee productivity. Those are the outcomes executives ultimately care about.

A simple framework for AI-enabled workflow redesign

Over the past several years, I have found it helpful to think about workflow redesign as a simple four-step sequence.

• Simplify. Remove unnecessary work, approvals, reports and handoffs before introducing technology.
• Standardize. Create a consistent way of working across the organization so improvements can be repeated and scaled.
• Redesign. Build the workflow around the desired business outcome instead of existing organizational structures or legacy systems.
• Automate. Apply AI only after the process has been simplified and redesigned.

Organizations often reverse these steps. They automate first and hope efficiency follows. In reality, automation should be the final step, not the first.

Following this sequence helps ensure AI is solving the right problem rather than making an outdated process run faster.

AI should improve work, not preserve it

One of the most valuable questions leaders can ask is surprisingly simple.

If we were designing this process today, would we build it the same way?

That question changes the conversation. It encourages people to challenge assumptions, eliminate unnecessary complexity and rethink how work should flow before technology enters the discussion.

It is also remarkably consistent with what leading researchers are finding. Harvard Business Review emphasizes that successful AI initiatives begin by improving the underlying process. MIT Sloan concludes that organizations achieve the greatest impact when they redesign workflows instead of automating isolated tasks. Deloitte’s research points to the same pattern, showing that the strongest business results come from treating AI as an opportunity to rethink operations rather than simply increase efficiency.

When independent research consistently reaches the same conclusion, it is worth paying attention.

Artificial intelligence is one of the most significant technologies organizations have adopted in decades. Its greatest value will not come from helping us execute yesterday’s workflows more quickly. It will come from allowing us to rethink how work should be done in the first place.

Leaders who redesign workflows before automating them will create simpler processes, better employee experiences and stronger business outcomes. Those who automate first may improve efficiency for a while, but they also risk embedding yesterday’s assumptions into tomorrow’s technology.

SAP is not unfairly preventing enterprises from extracting their data from its systems for use with competitors’ applications, the German Federal Cartel Office (Bundeskartellamt) concluded Thursday after a preliminary investigation.

The Bundeskartellamt does not currently intend to initiate abuse proceedings against SAP, although it will continue to monitor developments in what it views as a dynamic market, it said in a news release.

It launched its investigation into SAP’s practices following complaints by software companies including Celonis, a developer of process mining tools, alleging that SAP makes it difficult for customers and third parties to access data from its ERP systems and favors its own Signavio process mining tool.

“Companies must generally also be able to use their own data in third-party applications. With large software platforms, in particular, non-discriminatory access to data is crucial to effective competition,” said Bundeskartellamt President Andreas Mundt. “Our preliminary investigation has found that there are currently sufficient data extraction options available and that there have so far been no indications of exclusionary practices that may be relevant under competition law.”

SAP changed its policies on accessing data held in its applications via APIs in April, prompting customer pushback.

But, said Mundt, the Bundeskartellamt found that despite the API policy change, data extraction options that were previously permissible are still available.

Data extraction is possible

SAP welcomed the Bundeskartellamt decision, saying that “as the authority states, SAP customers and partners have sufficient and permissible technical options to extract data from SAP systems and use it in solutions from other providers. The SAP API Policy does not restrict these capabilities.”

Celonis also issued a statement, noting that the Bundeskartellamt ruling underlined the continued importance of unrestricted data access, and warning, “The decision is based on the key premise that data extraction for software from providers such as Celonis will remain possible even under SAP’s new API policy — a premise that SAP has been unwilling to confirm to date.”

The Celonis statement continued, “We remain steadfast in our conviction that company data belongs entirely to the customers who generate it. No provider should restrict a company’s right to extract its own information or prevent users from working with third-party providers such as Celonis that offer added value to customers.”

Celonis is also attacking SAP’s policies on data extraction in court in California. It filed a complaint in March 2025 alleging that SAP was leveraging its software to “prevent SAP customers from sharing their own data with third-party providers, including Celonis, without paying prohibitively expensive fees.” The judge dismissed some of the claims in that case, leaving three to be tested in a trial then scheduled for December 2026. Celonis has since amended its complaint to include 10 claims, and the trial has been rescheduled for 2027, the company said.

“Our litigation continues to uncover evidence of SAP’s unlawful behavior, including anticompetitive conduct and theft of intellectual property, and we are confident in the evidence that we will present at trial,” Celonis said following the German authority’s decision.

The Bundeskartellamt’s failure to find sufficient evidence to open a ‘formal abuse of dominance proceeding’ is a small win for SAP, said Scott Bickley, advisory fellow at Info-Tech Research, but “CIOs should not mistake it for a validation of SAP’s data access model.”

Although SAP recognizes customers’ right to decide they use their data, it does not make it easy for them to do so, he said. “CIOs may technically retain vendor choice but be faced with expensive replication architectures, API rate and volume restrictions, additional platform costs, performance lags and data migration costs, all with a dependency on an SAP-approved technical pattern, which can be a moving target.”

Data ownership as a procurement issue

Justin Greis, CEO of consulting firm Acceligence, sees the decision as an instructive one for enterprise CIOs.

“This isn’t a reason to stop asking hard questions of your ERP vendor. Whether it’s SAP, Oracle, Microsoft, Salesforce, or anyone else, enterprises should continue to evaluate how easy it is to access their own operational data, integrate third-party applications, and migrate workloads if business priorities change. Those questions are becoming strategic procurement issues, not just technical ones,” Greis said.

CIOs should consider data portability early in the procurement process, said Kaan Dincer, CEO of data migration vendor Settle: “Negotiate export rights, API access on reasonable terms, and documentation of the data model before signing and test a real extraction while the vendor still wants your renewal. The cost of your eventual exit is set on the day you implement, not the day you leave. ERP data now feeds analytics and automation outside the system of record, so access friction that used to be an IT annoyance is becoming a strategy constraint.”

In the SAP case, he said, “the regulator answered a narrow legal question, not the operational one. Declining to open proceedings means the friction was not shown to be anticompetitive. It does not mean the friction is not real. The Bundeskartellamt’s own findings acknowledge that extracting large data volumes is technically demanding and it said explicitly that it will keep watching as access mechanisms and license models evolve. That is not a clean bill of health. It is a decision to hold fire.”

Srinivasulu Reddy Battu, a senior software engineer with cloud vendor ZT Systems, said the big takeaway is the difference between difficult and impossible. SAP’s argument is that the data migration outside of its environment is possible, but Battu said it can be a time-consuming and expensive process.

“When the ruling says ‘various permissible and viable options’ exist, that’s technically true, but it glosses over how much expertise it actually takes to use them,” Battu said. “CIOs should still watch how process mining gets packaged in their contracts. If Signavio comes included by default, teams will naturally start using it and that quietly reduces your negotiating power with other vendors over time. This isn’t just about SAP: Oracle, Microsoft, every major ERP vendor sits on a massive amount of your business data. If any of them decided to tighten their API policies tomorrow, most companies would be scrambling.”