An important field of application for artificial intelligence (AI) technologies is increasing the productivity of processes and tasks. This latest phase of process management has a longer development history, which was less the focus of interest than the design of the organizational structure of companies. In the meantime, the use of process-oriented AI has become a new source of competitive advantage. It is therefore interesting to take a closer look at the development of process management.

In this continuation of our series of blog posts on artificial intelligence, I look at the contribution of AI to increasing the productivity of processes, freeing up resources for innovation.

Development of process management

The development of process management has proceeded in three phases. These phases are characterized by the interaction of engineering science, management science and information technology. The focus of the first phase was on production management. In the second phase, the focus shifted to the management of business processes. In the current third phase, the management of process-oriented artificial intelligence (AI) is becoming the focus of interest.

In the first phase, which was characterized by engineering science, the main focus was on breaking down, designing and automating production processes. At the beginning of the 20th century, significant rationalization impulses came from the engineer and consultant Frederick Winslow Taylor, who propagated the decomposition of work processes into small steps. In production engineering, specialization developed in various specialist areas such as mechanical engineering and chemical engineering. Production processes were automated with the help of measurement and control technology and robotics.

German industry has been a world leader in this sector of the economy for many decades, both on the supply and demand side.

IT-supported process optimization and innovation

The decisive foundation for a second phase of process management was laid by the German business informatics professor and company founder August-Wilhelm Scheer. In 1984, Scheer called for the creation of an IT-supported process organization in his book EDV-orientierte Betriebswirtschaftslehre. He developed this basic idea further together with SAP, now the world’s leading provider of enterprise resource planning (ERP) systems. In his book Architecture of Integrated Information Systems ARIS, published in 1991, he provided a framework concept for the description of business processes^1.

The optimization and innovation of business processes is supported by reference models. In the early 1990s, August Wilhelm Scheer together with SAP developed the event-driven process chain (EPC) model to describe more complex processes. This model formed the basis for SAP’s R/3 system. An EPC consists of four elements²

• event: When should something be done?
• function: What should be done?
• organizational unit: Who should do what?
• and information objects: What information is needed?

An Enterprise Resource Planning (ERP) system is a modular software system in which business applications are linked by a common database. Typical applications are finance and controlling or purchasing and logistics. Other features of ERP systems include process integration, a uniform development concept, a client/server architecture and the separation of the organizational view and the technical view (multi-client capability). ERP systems are introduced step by step in a function-oriented or process-oriented manner (successive) or on a key date basis (big bang).³

In the 1990s, the reengineering of business processes was one of the most important management topics. The hype was triggered by the book Reengineering the Corporation by Michael Hammer and James Champy, published in 1993.⁴ Thomas Davenport’s book Process Innovation, also published in 1993, describes information technologies as the driving force.⁵ My book Reengineering-Programme umsetzen, published in 1994, was the first German-language book on the subject.⁶

The subtitle „From rigid structures to flowing processes“ ties in with my habilitation published in 1991, in which I discuss the gradual paradigm shift in strategic management from mechanistic to managing complexity.⁷ This change of paradigm should continue in an evolutionary change of organizations. One example is research and production that is more strongly oriented towards customer needs.

Since 2015, SAP has been accelerating the migration of its customers to the SAP S/4 HANA ERP system. The following versions are available

• on-Premise: Customer purchases software license
• cloud: Installation in the SAP public cloud and
• hybrid: Installation in a private cloud.

Despite programs such as Rise with SAP, many existing customers have not yet purchased licenses for S/4 HANA. The acquisition of the Bonn-based start-up LeanIX should bring an improvement. Its platform provides companies with an overview of their IT systems („Google Map for IT“). The value proposition is a reduction in the complexity of multiple ERP systems with different individual configurations. In this way, SAP aims to simplify the transition to S/4 HANA and make it easier for its customers to move to the cloud. In 2023, LeanIX introduced an AI assistant based on the GPT language model from OpenAI.⁸

The first definition of big data was given by Doug Laney in 2001 („data volumes that are larger than we are used to“). Since around 2010, the term has replaced other terms, such as Business Intelligence and Analytics. The five Vs are important here: Volume, Velocity, Variety, Value and Validity. Large volumes of data can be processed using cloud computing. The global market leader in this „hyperscaler“ business area is Amazon Web Services (AWS), followed by Microsoft (Azure). Many companies use the pay-per-use business model.

Increasing productivity with robotic process automation

New opportunities to increase productivity arise from AI technologies based on process mining. Process mining emerged at the intersection of business process management and big data. It analyzes data generated by real processes, compares target and actual data and eliminates deficiencies. This results in innovative approaches to increasing productivity with the help of artificial intelligence (AI) technologies. One such AI technology is robotic process automation (RPA). RPA programs are rule-based software robots („bots“) that can take over routine tasks, e.g. travel expense accounting.⁹ The most valuable German start-up, Celonis from Munich, is active in this field.

Similar to Celonis, SAP is now also aiming to take the next development step. As part of its AI-first strategy, SAP has  a new Business Data Cloud that is designed to facilitate the processing and analysis of data. According to a study by the Bitkom association, less than 40 percent of companies in Germany are exploiting the potential of the data available to them. The new AI platform is a kind of translator designed to convert data into a standardized language. SAP is working together with the US company Databricks. The database forms the basis for the further development of the SAP AI assistant Joule into a kind of „super agent“ for a variety of tasks. SAP wants to use this to bring its products back together into a „unified system“ in the cloud. The value proposition is artificial intelligence that makes the entire organization of SAP customers more productive.¹⁰

Collaboration with AI assistants

Databricks‘ data intelligence platform is based on a lakehouse architecture that combines data lakes and warehouses. A lakehouse is based on open source and open standards. It simplifies the data inventory by eliminating silos.

Start-ups are also developing AI solutions for specific business processes such as quality management. The Munich-based start-up Datagon AI has developed an AI that aims to convert production data into individual and optimized quality management. The AI learns which data structure indicates error-free production of vehicles, for example, and recognizes deviations from this structure. The aim is to detect 15 to 20 percent more errors than with standardized test procedures. The basis is the data pattern of an error-free production process. Datagon describes its AI solution as a game changer for quality management.¹¹

AI assistants for routine tasks are also playing a more important role in increasing productivity. One such AI assistant supports Würth’s sales department, for example. Reinhold Würth’s motto is: „You are not employed by Würth, but by your customers“. Würth has expanded his father’s screw business into a global group for assembly and fastening technology with sales of more than EUR 20 billion. Pico, the AI assistant developed at Würth, helps sales employees with¹²

• route planning for customer appointments
• administrative tasks via voice control in the car, e.g. when writing invoices and
• answering complex questions in preparation for customer appointments

This is based on systematic data collection of the company’s products and customers. The annual IT budget currently stands at half a billion euros. Around a tenth of this is invested in new AI applications.

While large organizations develop their own AI assistants, smaller companies need a partner. The savings banks are planning to equip all workstations with a personal AI assistant. This chatbot, S-KI-Pilot, was developed by the savings banks‘ IT service provider Finanz Informatik (FI). The AI applications run in FI data centers. The development of the chatbot is 80 percent based on the freely accessible GPT model of the French company Mistral AI. The training is based on specific internal savings bank knowledge that is available on the Internet and in process documentation. In this way, the group aims to maintain its AI sovereignty and counteract the increasing shortage of specialists. One important goal is to make processes more efficient and faster.¹³ Smaller companies generally do not have these options. They therefore need a trustworthy service provider, preferably from Europe, in order achieve competitive advantages with knowledge-specific AI.

Fields of action for organizational change

Increasing productivity with process-oriented AI is not the only task in organizational change. There are also the fields of action organizational structure, project management and human resource development. Combining these fields of action is currently a major challenge.

In addition to traditional approaches to reducing hierarchies, companies such as Bayer are testing the humanocracy approach developed by Gary Hamel to create a leaner organizational structure.¹⁴ The implementation of agile project management is associated with a change in mindset, especially in established companies. In addition, upskilling and reskilling a large number of employees requires new approaches to human resource development when it comes to AI. Currently there is no general theoretical basis for the connection these fields of action.

Conclusion

• The interdisciplinary field of process management has developed in three phases
• In the current final phase, new possibilities for increasing productivity are emerging with the help of artificial intelligence
• Robotic process automation and AI assistants are providing important impetus here
• A new platform developed jointly by SAP and Databricks is based on the Lakehouse architecture.

Literature

[1] Scheer, A.W., ARIS – From Business Process to Application System, 4th edition, Berlin 2002

[2] Gadatsch, A., Grundkurs Geschäftsprozess-Management, 10th edition, Wiesbaden 2023, p. 126ff.

[3] Gadatsch, op. cit. p. 202ff.

[4] Hammer, M., Champy, J., Reengineering the Corporation – A Manifesto for Business Revolution, New York 1993

[5] Davenport, T.H., Process Innovation – Reengineering Work through Innovation Technology, Boston 1993

[6] Servatius, H.G., Reengineering-Programme umsetzen – Von erstarrten Strukturen zu fließenden Prozessen, Stuttgart 1994

[7] Servatius, H.G., Vom strategischen Management zur evolutionären Führung – Auf dem Weg zu einem ganzheitlichen Denken und Handeln, Stuttgart 1991

[8] Alvares de Souza Soares, P., et al, SAP buys Bonn-based software start-up LeanIX. In: Handelsblatt, September 8/9/10, 2023, p. 23

[9] Gadatsch, op. cit. p. 289ff.

[10] Kerkmann, C., SAP introduces „super agents“. In: Handelsblatt, February 14/15/16, 2025, p. 30-31

[11] Knees, L., Up to 20 percent fewer errors. In: Handelsblatt, February 21/22/23, 2025, p. 34

[12] Buchenau, M., A passenger named Pico. In: Handelsblatt, December 27/28/29, 2024, p.32

[13] Atzler, E., Kröner, A., AI assistant for 190,000 employees. In: Handelsblatt, January 9, 2025, p.28-29

[14] Hamel, G., Zanini, M., Humanocracy – Creating Organizations as Amazing as the People Inside Them, Boston 2020