strategic realignment | Competivation

Design-oriented innovation research on AI applications

25.03.26

Around the world, many companies face the challenge of keeping pace with artificial intelligence (AI) and gaining a competitive edge through AI tools that leverage their specific expertise. This development is leading to a renaissance in design-oriented management and innovation research, which aims to bridge the gap between theory and practice. In the process, research is shifting from empirical work at universities to connective design in real-world laboratories of change.

In this blog post, I outline the development of the design-oriented research approach, highlight its advantages, and explain a general procedural concept.

A decisive year for AI adoption

2026 could be the year that determines for many companies whether they achieve a breakthrough in AI adoption or remain stuck with isolated pilot projects. According to an analysis by the market research firm Forrester, only 15 percent of decision-makers report that the use of AI has so far made a measurable contribution to their companies’ operating results. This could lead them to postpone about a quarter of their planned AI spending until 2027. However, the danger of being too hesitant in adopting AI is that it creates a gap that is difficult to close.¹

On the other hand, pioneering companies are succeeding in achieving significant competitive advantages with AI tailored to their specific situations. These pioneers practice design-oriented management and innovation research to lay the groundwork for success. One example is Siemens, which aims to develop the world’s largest and broadest range of industrial AI applications and double its enterprise value.²

The German Economic Research Institute (IW) forecasts that the potential for increasing gross value added through the use of AI in Germany by 2034 stands at 440 billion euros. Of this, 110 billion euros is attributable to potential innovations and 330 billion euros to increased productivity. For Germany as an industrial hub, the application of AI in particular presents an opportunity. Google, a subsidiary of the U.S. technology conglomerate Alphabet, has also recognized this. It plans to invest 5.5 billion euros in Germany and open a center for AI applications in Berlin, where its own researchers will also be based.³

This new situation has exciting implications for the design of AI ecosystems.

Designing AI ecosystems

The opportunity for established companies lies in knowledge-specific AI, where AI tools leverage and amplify the companies’ specialized expertise.⁴ This is most effectively achieved within AI ecosystems.

The term “AI ecosystem” refers to a dynamic network with strong connections between various actors who use AI technologies to create and disseminate innovations. AI ecosystems can be concentrated in a single region and have developed around a central hub, such as a university or a company. The world’s best-known and most influential AI ecosystem emerged in Silicon Valley in the San Francisco Bay Area. Stanford University near Palo Alto formed the core of Silicon Valley in the 1930s. In 1939, Bill Hewlett and David Packard founded the company HP there in a garage. Later, leading semiconductor companies – which gave the valley its name – and some of the major AI providers established themselves here.

Stanford University served as a model for Helmut Schöneberger, the head of the Munich-based startup incubator UnternehmerTUM. Crucial to its success were the collaboration with the Technical University of Munich and the support of BMW shareholder Susanne Klatten. In addition, other startup incubators have emerged in Munich, such as the Center for Digital Technology and Management (CDTM), which offers the transdisciplinary master’s program in Technology Management, works closely with industry partners, and has spawned over 250 startups.

By 2025, startups in Munich had received 3.3 billion euros in venture capital, thereby pushing Berlin into second place in this category, where startups raised 2.7 billion euros. However, AI ecosystems with successful startups have also emerged in other German regions.

Key players in AI ecosystems include:

Decision-makers in relevant policy areas and at various levels of state and regional government
Universities and schools
AI startups that originated as spin-offs or have established themselves in a region
Venture capitalists who use venture capital to finance the growth of AI startups
Major providers of AI hardware and software that shape financial markets with their financial clout and have a significant impact on the environment and society, as well as
established companies and their employees, who use AI, collaborate with startups, and invest in them.

Pharmaceutical research provides an interesting example of new opportunities for collaboration between established companies and startups.

In the pharmaceutical industry, tech-bio companies, such as the French startup Owkin, are accelerating early-stage drug discovery with the help of AI. A first wave of drugs developed in this way is currently in clinical trials. One possible form of collaboration is for AI biotechs to take on early-stage drug discovery and for established pharmaceutical companies to handle the later phases and commercialization. Another option is for pharmaceutical companies to acquire AI startups and build up their own relevant capabilities. In any case, this research-intensive industry is facing a fundamental change, in which the design of AI ecosystems is a key success factor.⁵ This example illustrates that as industries evolve, management research changes as well.

However, artificial intelligence is not only a driver of productivity and innovation but also poses a potential threat. In light of geopolitical shifts, it is crucial for Europe to regain its ability to shape the future. A trustworthy AI can be an important means to this end. The recent dispute between the U.S. government and the U.S. startup Anthropic illustrates just how political the design of AI ecosystems has become.⁶ This, too, is leading to significant changes in management research.

An important aspect that is often overlooked in AI ecosystems is the environmental and social impact of AI technologies. For instance, large language models require the work of crawlers that collect data and annotators who comment on, evaluate, and label texts and images. This work often takes place out of the public eye, frequently in countries of the “Global South.” Transparency in these supply chains is low.⁷

Management research in real-world laboratories of change

Empirical approaches, predominantly originating from universities, have long dominated business research. The authors publish the results of their work in academic journals, which practitioners rarely read. Critics of this retreat into an “academic ivory tower” argue that the practical relevance of research has diminished. This criticism is not new, but it has grown stronger.⁸

Universities defend their position by arguing that the practical relevance of empirical research is based on “field trips” in which the perspectives of relevant actors are analyzed. However, the goal of such work is generally not to design concrete objects – such as new AI-based business models –within their specific context.⁹

In this respect, business research differs from the design-oriented approach of engineering sciences, which takes place predominantly in laboratories and pilot plants.

Due to the growing importance of inter- or transdisciplinary research, the proportion of connective design is also increasing in management science. This practice- and design-oriented research is increasingly taking place in real-world laboratories of change.¹⁰ A key driver of this development is artificial intelligence, which acts as a catalyst for the founding of startups and the realignment of established companies. We believe that, following a phase of retreat into universities, management research should once again take place more strongly in practice and utilize design-oriented approaches.

This response to the questions of how and where research approaches should be conducted marks the beginning of a new chapter in the history of management research. A key feature of this reorientation toward connective design is research in which scientists, consultants, and practitioners bring their diverse perspectives and strengths together. The benefit lies in better solutions to complex problems based on new scientific insights.¹¹

The development of humanoid robots provides an example. In this rapidly growing market, companies have the best chances when they combine software and hardware expertise. Data from the real world is the biggest bottleneck here. The Metzingen-based manufacturer Neura Robotics has therefore decided to build training facilities for robots. One of the first of these “gyms” is being developed in collaboration with the Technical University of Munich at the Munich Institute of Robotics and Machine Intelligence (MIRMI) at Munich Airport. In international competition with companies from the U.S. and China, rapid scaling is crucial for this AI application.¹²

Since design-oriented management research is less widespread, I would like to briefly outline its history. Personally, over the past few decades, I have repeatedly observed how different the thought and language patterns of the relevant actors are in theory and practice.

Principles and pioneers of creative management research

Design-oriented management research is based on similar principles and has been shaped by several pioneers.¹³ These principles are:

Action research, pioneered by social psychologist Kurt Lewin. Lewin advocated for a connection between theory and practice to solve real-world problems.
General design theory, as defined by Nobel Prize-winning economist Herbert Simon.¹⁴ Simon defines this as the science of designing human-made artifacts and systems.
Action Science, conceived by Harvard professor Chris Argyris.¹⁵ Argyris’s primary goal is to make knowledge usable in order to improve actions within organizations.
The Design Science Research (DSR) approach developed by Alan Hevner at the University of South Florida.¹⁶ This concept, which is particularly widespread in business informatics, focuses on solving complex, real-world problems through innovative IT systems.
Action Design Research (ADR), described by various scholars, which combines Action Research and Design Science Research.

Pioneers of design-oriented management research include Joan Ernst van Aken and Georges Romme, who teach at Eindhoven University, as well as David Denyer from Cranfield University in the UK. Van Aken aims to bridge the gap between management theory and practice. The focus is on transdisciplinary research to solve practical management problems. Building on design theory, Romme tests iterative processes in organizational design.¹⁷ Denyer views management knowledge as a malleable resource for solving real-world problems. His solution-oriented approach seeks to ensure practical application through mechanisms of action and evidence-based verification using various information sources.

The advantages of a design-oriented approach are particularly evident in innovation and sustainability research.

Advantages in innovation and sustainability research

When applying AI, the advantages of a design-oriented approach to innovation and sustainability research unfold most notably in companies that practice this approach in self-similar Strategy 5.0 labs. The goal of such a real-world lab is to connect the various fields of action for AI applications.¹⁸ It is helpful if the countercurrent principle – combining a top-down AI strategy with a bottom-up, harmoniously diverse range of AI applications – is effectively implemented.

Based on our experience from a series of projects, the following benefits are of particular importance:

The removal of barriers to innovation
high-performing and trustworthy innovation ecosystems
motivated high-performance teams
connective design as a core competency
improved technology transfer
accelerated learning loops using agile methods, as well as
concrete results and measurable success for all stakeholders.

These advantages enable a realignment of innovation systems.

The shortcomings in Germany’s energy and mobility transition are an example of how important it is to remove barriers to innovation.¹⁹ The aim here is to translate new insights from innovation research into practical action.

The goal is to design high-performing and trustworthy innovation ecosystems.²⁰ A hallmark of these systems is improved collaboration among stakeholders from the political, scientific, economic, and social sectors.

Motivated high-performance teams play a key role in shaping innovation research.²¹ Leaders have the task of exemplifying and fostering an entrepreneurial mindset. This begins with education and continues throughout one’s professional career.

A core competency to be developed in this context is connective design.²² This requires a reorientation of teaching and research using AI as a tool. Such human-centric AI permeates all disciplines.

A positive side effect is improved technology transfer.²³ The goal here is to overcome the German paradox between strengths in basic research and weaknesses in commercialization.

One way to achieve this is through accelerated learning loops within organizations.²⁴ Agile methods are used for this purpose. It is important to adapt a general approach to specific types of problems and the situation at hand.

In doing so, all stakeholders should focus on concrete results and measurable successes.²⁵ In joint programs, this is achieved through transparent performance management.

In the following, I would like to explain a suitable approach.

Steps of a general procedural concept

An example of design-oriented innovation and sustainability research is the AI-based strategic and organizational realignment of a company.²⁶ For topics such as this, a general procedural concept consisting of the six steps shown in the figure has proven effective.

The first step is an analysis of the state of research, best-practice examples, and the specific initial situation. The task here is to combine an internal and an external perspective in an audit.

It is then crucial to develop a comprehensive understanding of the problem’s complexity, to identify its causes, and to coordinate efforts among the relevant stakeholders. Collaboration between various disciplines and practitioners is more successful than disciplinary research approaches.

Transdisciplinary teams also play a key role in the subsequent design and selection of creative solutions. What may be uncharted territory for a single organization can, when taken as a whole, contribute significantly to scientific progress.

A defining characteristic of design-oriented innovation research is the implementation of pilot projects for Minimum Viable Solutions (MVS). These pilots are tested in real-world laboratories of change. For established companies, working in learning loops often requires a shift in mindset. Human resources development can support this effort.

The fifth step is planning and executing the implementation. This is closely linked to financing the scaling process. German startups have long complained of disadvantages, for example, compared to the U.S. Therefore, an improvement in the political framework conditions should be sought, particularly in this step.

In parallel, transparent performance measurement takes place, for example, using the Objectives and Key Results (OKR) method. In this context, “transparent” means that performance management does not occur in sectoral silos. For a policy that sets goals but neglects to measure success, this involves a learning process aimed at joint system design by innovation managers.

Innovation managers design complex, evolutionary systems

As early as in our 2014 book publication “The Innovation Manager,” we concluded that a central task of innovation managers lies in the design of innovation systems and the connection of various fields of action.²⁷ Subsequently, we engaged intensively with the behavioral economic perspective of such connective design.²⁸

The scientific foundation for connective design is the theory of complex, evolutionary systems. The application of this approach to socio-technical systems has triggered a paradigm shift in strategic management.²⁹ U.S. digital companies have mastered this new management paradigm better than the European economy.³⁰

Another insight is that innovation research is a transdisciplinary design task.

Interdisciplinary or transdisciplinary?

We deliberately use the term transdisciplinary to make it clear that, unlike the term interdisciplinary, it is not merely about mediating between scientific disciplines, but also about involving non-scientific actors and establishing a connection between theory and practical design.³¹ The particular complexity of transdisciplinary innovation research stems from

the heterogeneity of the disciplines, which ranges from natural science and technical research through various policy fields to management science and organizational psychology
the diverse interests and thought patterns, e.g., of scientists and practitioners
the dynamics of development and the number of levels, from geopolitics down to the individual, as well as
the different roles of the actors, e.g., as neutral observers or personally affected individuals.

Unfortunately, transdisciplinary innovation research has so far lacked recognition in universities. The reasons for this are manifold. One important reason is likely that the traditional academic and publication system tends to reward disciplinary excellence. This presents an opportunity for applied research.

Transdisciplinary research on sustainability innovations

Our book, published in 1994, on the “ecological reorientation” of automotive companies emerged from consulting projects and accompanying research at the University of Stuttgart.³² This research was transdisciplinary, but had only a limited impact because German companies and policymakers at the time did not implement our recommendations – for example, regarding new propulsion systems.

Thirty years later, this industry – so vital to the German economy – is grappling with serious problems. VW, the world’s second-largest automaker by vehicle sales, is undergoing a process of strategic and organizational realignment.³³ At the same time, the geopolitical landscape is marked by extreme uncertainty.

This example illustrates that, when it comes to sustainability innovation, transdisciplinary and design-oriented research is of crucial importance for securing our country’s prosperity. ³⁴ In recent years, the field has evolved dynamically. The intersection of environmental technology and artificial intelligence has moved to the center of attention. New market leaders can emerge from collaborations between established companies and digital greentech startups if European policymakers succeed in improving the framework conditions.³⁵

One example is the German-Luxembourgish startup R3 Robotics, which has developed an AI-powered robotics platform for battery recycling. In this way, Europe can reduce its dependence on imports and increase the sustainability of batteries.³⁶

In my personal experience, students remain very interested in such topics because they recognize the resulting career opportunities. This provides important impetus for our expert network.

Combining management consulting and human resources development with design-oriented innovation and sustainability research

Our expert network Competivation has long combined management consulting and human resources development. The advantage for clients is a better value for money than with traditional consultants, as the focus is on the qualifications of the employees who are actively involved in the projects.

For several years now, we have been supplementing this service with design-oriented innovation and sustainability research. In doing so, we supervise the theses of dual-track students and external doctoral candidates who are working on relevant projects within the client organization. The advantage here is that the research is tailored to the specific situation of the company. The researchers and their companies benefit from the extensive experience of our experts.

With this approach, Competivation has created a unique international selling point geared toward the needs of the AI era.

Design-oriented research also has far-reaching implications for university teaching. We see ourselves as an innovative educational provider that teaches the ability to a of AI-supported connective design of solutions for complex management problems.³⁷

Conclusion

When applying AI, many companies face the challenge of defending their position and gaining competitive advantages through a specific approach
In this context, the design of AI ecosystems is crucial
Parallel to this development, research approaches and locations are shifting toward design-oriented innovation research in real-world laboratories of change
In this context, design-oriented management and innovation research is experiencing a renaissance stemming from a number of advantages
In this transdisciplinary research approach, a six-step procedure has proven effective, which is adapted to the respective problem type and situation.

Designing trustworthy high-performance systems

29.01.26

In recent years, the importance of connective strategic management has continued to grow. In light of the dynamic development of artificial intelligence (AI) and new geopolitical challenges, the design of trustworthy high-performance systems has become a focal point of interest. In this context, the term „high performance“ is being reinterpreted in business and politics. An important field of action here is design-oriented management research.

In our first blog post of 2026, I address the question of what important areas of action for Europe in terms of trustworthy high-performance systems are.

High performance in business and politics reinterpreted

For Jeanette zu Fürstenberg, who is responsible for Europe at the US fund General Catalyst, there is an opportunity for the old continent in connecting startups with the world of established industrial companies. Her successful investments include Mistral in France and the defense company Helsing in Germany. These companies focus on artificial intelligence (AI) that uses highly specialized application knowledge. Her publication „Wie gut wir sind, zeigt sich in Krisenzeiten“ (How good we are is revealed in times of crisis) was named Management Book of the Year in 2025. For her, the basis for a European high-performance system that can achieve reindustrialization is resilience, which enables recovery as quickly as possible after external shocks.¹ In 2025^, the number of startups founded in Germany reached a record high.

The topic of high-performance organizations is not new. High-performance organizations are characterized by high-performance teams. As early as the 1950s, the British Tavistock Institute developed an initial foundation with its socio-technical systems approach. I described the results of consulting projects on the characteristics of high-performance organizations in an article in Harvard Manager magazine in 1988. One important finding is that visionary leadership creates the framework for teams that work in a more self-organized manner.²

McKinsey consultants Jon Katzenbach and Douglas Smith examined the question of what characterizes high-performance teams.³ However, further developments have shown that, despite considerable efforts, empirical research is struggling with the design of high-performance organizations.⁴ AI is now giving performance management new impetus to improve the connection between strategy implementation and motivation.⁵

US tech companies with artificial intelligence (AI) now dominate the global economy. By the end of 2025, 61 of the world’s 100 most valuable companies will be from the US. The dominance of the US results from the unique strength of seven tech giants, which together have a market value of €18.3 trillion. An important driver of this development is the hype surrounding artificial intelligence (AI). Germany is represented in the top 100 ranking with the companies SAP (rank 40), Siemens (72), the European joint venture Airbus (91), and Allianz (100). In view of geopolitical changes, this concentration of power raises the question of how great the danger of dependence on the US is. ⁶ In the AI chip market, competitive pressure is increasing for market leader Nvidia.

AI chips are becoming increasingly powerful, but at the same time, AI increases the risk of disinformation. With a global market share of 85.2%, AI chip manufacturer Nvidia has a dominant position ahead of Broadcom (10.3%), Marvell (2.1%), and AMD (1.8%). Challengers AMD and Meta have announced a new AI system for data centers (Helios platform) that is expected to deliver a significant performance boost. Nvidia is countering with its new Rubin chip generation.⁷

However, there is a risk of a loss of trust in AI due to the risk of disinformation from fake accounts. AI bots falsify content, imitate people, and post automatically on social media. Such deepfakes can cause great economic damage and^, for example, ruin a brand’s reputation.⁸

Large language models and free AI tools often lead to a loss of quality and trust because they are not trained for high performance, but rather for the production of average knowledge. When AI users are under time pressure and there are no quality standards in place, „AI slop“ can result. Although this produces faster results, the quality declines. Possible consequences include a loss of reputation and trust. When using AI^, it is therefore important to supplement content with expert knowledge after quality control.⁹

AI and geopolitical challenges are reinterpreting the concept of high performance. Not all AI is trustworthy. We understand a trustworthy high-performance system to be a system (e.g., a company, a region, or a state) that performs very well compared to the competition and is trusted by the recipients of its services. In addition, these service recipients are willing and able to pay for the services. High-performance systems must therefore justify their higher prices (e.g., through „German quality,“ technical superiority, or a luxury brand).

Reinterpreting high performance means that high-performance systems are characterized by both success and trustworthy behavior. If neither of these is the case, we speak of system failure. Most socio-technical systems fall somewhere in between. Cases where only one of the two criteria is met are interesting. An existing pattern of success is at risk when a previously successful system, such as that of the AI champions, loses trust. This could result in a transitional phase with new opportunities, for example, if Europe, which has been less successful in digitalization to date, scores points with trust.

Peter Frankopan, a British professor of global history teaching at Oxford, sees the world in a transitional phase similar to that of the 1920s, when the old order was not yet dead and a new one had not yet been born.¹⁰

The question is therefore how Europe can seize its opportunities and become a designer of trustworthy high-performance systems.

Strategic realignment in a phase of transition

In the first nine months of 2025, DAX companies spent €6 billion on restructuring. The highest restructuring costs in 2025 were incurred by Mercedes (€1.4 billion), Volkswagen (€900 million), Siemens and Commerzbank (€500 million each). The automotive, mechanical engineering, and chemical industries are particularly affected. At the end of September 2025, 120,300 fewer people were employed in German industry than a year earlier. Many companies are offering generous severance packages. Often, one round of restructuring is followed by another without solving the underlying problems. This would require a strategic realignment after restructuring.¹¹

The term „strategic realignment“ describes an innovative approach to coordinating existing and new system elements (e.g., business model, strategy, technologies, customers, competencies, organization, culture, and environment). Realignments usually have a profound effect over a longer, undefined period of time in many parallel learning steps. Complex interactions play an important role in this process, resulting in specific patterns that are difficult to predict.

During a phase of transition, companies must manage complex realignment processes. In a successful, innovative company, important system elements are well coordinated. This alignment often takes place through fine-tuning, in which management continuously adapts the strategy to changes in the environment, for example. If this is not done, the company develops in the direction of misalignment. Management and supervisory boards often recognize this creeping decline too late. The result is an established company in a permanent crisis that requires restructuring.

The terms restructuring and transformation are now often used synonymously. Both terms describe a temporary, comprehensive change. Unfortunately, the inflationary use of the term transformation conveys the illusion that complex realignment processes are limited in time. The example of artificial intelligence clearly shows that such a static worldview is naive.

A longer-term goal of strategic realignments is the design of trustworthy high-performance systems.

Fields of action for trustworthy high-performance systems

In times of increasing polarization, high-performance systems are characterized by their ability to bring people together. History teaches us that the risk of polarization increases during periods of technological and political upheaval. This also applies to the changes brought about by artificial intelligence (AI). It is crucial that people see themselves as active participants in shaping change rather than passive objects of it. The complementarity of humans and AI is a malleable system. The performance of such a system depends on the ability to improve connections between the actors and the system elements. In our application-oriented research and teaching, we start with the thesis that the following fields of action, shown in the figure, are important in the design of trustworthy high-performance systems:

A connective strategic management for a triple realignment
high-performance teams with a growth mindset in a phase of transition
the connection of trustworthy partners from politics, business, science, and society, and
design-oriented management research in real-world laboratories of change.

Interdisciplinary university teaching faces the task of imparting the relevant skills for these fields, e.g., in the area of entrepreneurship for AI applications.

In the following, I will discuss these fields of action and skills in more detail.

Connective strategic management for a triple realignment

Since the 1960s, new challenges have led to various stages of development in strategic management.¹³ We distinguish between

a market- and finance-oriented stage (Strategy 1.0)
a technology- and innovation-oriented stage (Strategy 2.0)
a sustainability-oriented stage (Strategy 3.0) and
a resilience-oriented stage (Strategy 4.0).

In the current fifth stage of development (Strategy 5.0), the challenge lies in connecting the previous stages. Companies must become more resilient, more digital, and more sustainable at the same time.¹⁴ This requires the connective design of threefold strategic and organizational realignments. Such a triple realignment takes place in the context of serious changes in the political environment. The current situation is historically unprecedented. Therefore, the contextual intelligence of management plays an important role.¹⁵

German policymakers should create the framework for a cohesive strategic management approach through fundamental reforms. When the first signs of macroeconomic weakness appeared in 2018, they were harbingers of the most severe and longest industrial recession the Federal Republic has ever experienced. German industry has since lost much of its competitiveness. Experts are calling for new approaches to supply-oriented innovation policy and communication that conveys the need for a change of course. Politicians must implement the promised fundamental reforms. Such a new beginning can only succeed with solidarity instead of polarization.¹⁶

In this environment, resilience-oriented strategic management is becoming increasingly important.¹⁷ At this year’s World Economic Forum in Davos, the differing positions of the US president and European representatives clashed.¹⁸ Canadian Prime Minister Mark Carney suggests that in a world where major powers are becoming imperialists who blackmail other states, middle powers and smaller countries should form trustworthy partnerships.¹⁹

Such cooperation plays a decisive role not only at the geopolitical level, but also in high-performance teams.

High-performance teams with a growth mindset in a phase of transition

Black Forest Lab (BFL), currently Germany’s most valuable AI startup, is based in Freiburg, was founded in 2024, and develops AI models for image generation based on text. The founders are part of the core team behind the open-source AI model Stable Diffusion, the text-to-image model that generates digital images from text and, alongside ChatGPT, sparked the global AI hype in 2022. BFL’s Flux models are now one of Google’s biggest competitors. Important impetus for the work of the founding team came from Björn Ommer, a professor of computer science at LMU Munich. This example shows that high-performance teams can also emerge in Germany in the field of AI.²⁰

New ideas and the creation of something new often originate from people who find a state of flow motivating. The term flow (in the sense of „being in the flow“) was coined by psychology professor Csikszentmihalyi back in 1975. It refers to being completely absorbed in an activity, which usually involves a high level of intrinsic motivation and a change in the perception of time. Interviews in which outstanding creative personalities from various fields look back on their working lives show that their motivation stems primarily from the creative process. For many people, the foundations for possible flow states are often laid in their youth, based on their growth mindset.²¹

In her book Growth Mindset, Stanford professor Carol Dweck distinguishes between a static and a growth mindset.²² The following figure compares these two mindsets. High-performance systems often have leaders with a growth mindset. An important characteristic is that these people are aware of their talents, but place greater emphasis on their further development and learning processes. In contrast, people with a static worldview place greater hope in the effect of their innate talents.

Microsoft CEO Satya Nadella writes that the book had a strong influence on his personal development.²³

A person’s self-image and their environment are closely linked. High performance therefore arises from an interplay between the two. Social psychologist Mary Murphy has extended the growth mindset concept to organizations, their culture, and the environment surrounding them:²⁴

According to this, a growth culture promotes the potential of all employees. This culture emphasizes collaboration, continuous learning, and the development of skills.
A genius culture, on the other hand, believes in innate talent. This leads to internal competition, risk aversion, and a reluctance to admit mistakes.

Recommendations for action for managers are

create psychological safety and
giving constructive feedback.

However, simplistic application of this approach in practice underestimates the complexity of implementation. This can lead to demotivation among exceptional talents.

This raises the question of whether there are any current examples of a growth culture in Germany. A new bridge in South Westphalia has become a symbol of connective design. The Sauerland motorway is the most important transport link between the Ruhr area and Frankfurt. Due to the risk of collapse, the Rahmedetal bridge near Lüdenscheid, where I grew up, had to be suddenly closed in December 2021 and later blown up. This was a disaster for the economy with its many hidden champions and for the people in the region. Every day, 20,000 vehicles had to be diverted via bypasses and through residential areas. The German Economic Institute estimates the damage to businesses at around 1.5 billion euros. In Germany, new construction normally takes around eight to ten years. However, traffic is already rolling across one side of the A45 bridge via the after a record-breaking four years. This was made possible by smooth cooperation between the parties involved, a new planning procedure, and innovative construction methods. The German Chancellor sees this as a model for other renovations, and for the Minister President of North Rhine-Westphalia, the new benchmark for implementation speed in Germany is called „Rahmede“.²⁵

We can therefore summarize that the culture of socio-technical systems is strongly influenced by the mindset-image of important stakeholders and prevailing design patterns.

High-performance cultures are characterized by a growth mindset and a connective pattern. The opposite is a silo culture or, in extreme cases, a culture of self-satisfaction. Here, a static mindset and a divisive design dominate. Descriptions of outstanding leaders often heroize a lone wolf culture. These individuals are attributed with a growth mindset. At the same time, however, the impression is created that their successes were achieved single-handedly and in isolation from others, which is usually not the case. Until a few years ago, a culture of complacency was widespread in Germany. People rested on the successes of the past, but the mindset in politics and business was rather static and not very future-oriented.

Managers serve as role models in this regard. Their growth mindset is transferred to their employees. Conversely, managers with a static mindset and isolating behavior are responsible for the emergence of toxic cultures. Their position of power enables them to oust internal competitors and employees with a growth mindset that they perceive as a threat. Unfortunately, the role of consultants is often to secure and expand the position of power of the „static“ individuals. Attempts by external parties to change silo cultures are usually met with rejection and fail. It is therefore the task of supervisory bodies to review the dysfunctional mindsets of managers and take timely action. If this does not happen, there is a risk of system failure.

On its way to becoming a high-performance system, Europe currently finds itself in a difficult situation.

Connection of trustworthy partners from politics, business, science, and society

Europe first needs a resilience program against its enemies from outside and within. US political scientist Francis Fukuyama believes that Trumpism will continue even without Trump. For open democratic societies, this is an extremely dangerous development. He fears a relapse into the world order of the 19th century. It is therefore important that Western societies develop sufficient resilience. It should also be taken into account that tech billionaires primarily act in their own economic interests. The greatest danger for Europe is resignation.²⁶

Marc Tüngler, head of the German Association for the Protection of Securities Holders, laments the lack of political support necessary for innovation and economic restructuring. Germany is no longer internationally competitive in terms of electricity prices, for example. Politicians are responsible for this. Important levers would therefore be an improved location policy and a more innovation-friendly climate. We are far from the necessary solidarity between business and politics. He expects 2026 to be a year of decisions for politicians.²⁷

In his book „Wir Krisenakrobaten“ (We Crisis Acrobats), Stephan Grünewald, co-founder of the Cologne-based opinion research company Rheingold, describes the hope for self-efficacy that would enable our society to overcome the multitude of current crises. His recommendation consists of six points:

Truthfulness (clear identification of problems)
focus (successful national projects)
participation (making one’s own contribution clear)
fairness (unreasonable demands must be perceived as fair)
culture of debate (dealing more productively with changes in perspective) and
solidarity (which must be relearned).

Unfortunately, silo thinking („silodarity“) still prevails at present.²⁸

Martin Keller has returned to Germany from the US to become the new president of the Helmholtz Association. The association comprises 18 independent research centers with almost 48,000 employees and a budget of more than six billion euros. Keller wants to use a plan of action to ensure that Germany remains or becomes a global leader in selected fields of innovation. This requires closer cooperation, e.g., in the context of public-private partnerships (PPP), in which politics, research, and business cooperate in order to become more competitive. He believes it is time to break down old structures.²⁹

In his book „Visionen braucht das Land“ (The Country Needs Visions), Jochen Andritzky, co-initiator of Zukunft-Fabrik 2050, calls on politicians to develop visions of the future that can be discussed and provide guidance. This approach is more promising than short-term pseudo-solutions that merely combat the symptoms.³⁰ This return to the power of vision provides important impetus for management research, which in the past has often been content with incremental improvements. Design-oriented management research aims to be more practice-oriented in this regard.

Design-oriented management research in real-world laboratories of change

A research project at Würth has given rise to an AI start-up that could revolutionize the crafts. The aim of the research project conducted by the wholesaler of mounting and fastening material Würth and the AI Lab at the Technical University of Munich was to process inquiries from trade customers in sales more quickly. This led to the spin-off Mercura AI in March 2024, which uses AI to try to solve several problems:

Overcoming the shortage of skilled workers
increasing productivity for highly complex tasks, and
faster processing of inquiries and quotes.

Mercura AI combines semantic models, the recognition of requirements,
company-specific rules, and learning from previous quotes. The software processes both text and speech. The founders have combined AI expertise with industry experience. This example shows the potential of design-oriented management research in companies. ³¹

Nobel Prize winner Herbert Simon provided important impetus for design-oriented management research. His book „The Sciences of the Artificial,“ published in 1969, is not only a fundamental work on AI, but has also had a strong influence on design theory. The basic idea is that, in addition to the natural sciences, there is a universal science of design. This gave rise to the design methods movement. Not only the technical sciences, but also management science deal with the design of the possible (contingent). In the technical sciences, the design of new things is a natural goal. In management, political science, and social science, the diversity of individual systems and subsystems originating from humans has a specific complexity that is difficult to research purely empirically. Simon’s groundbreaking work emphasizes the interdisciplinarity of design.³²

Real-world laboratories of change open up new possibilities for management research. A real-world laboratory (living lab or sandbox) is a research and application space in practice where, for example, companies and their partners design innovative business models. In doing so, they combine research, learning, and action, promote interdisciplinary collaboration, and enable the testing of new legal frameworks (e.g., through the application of experimentation clauses). The concept became known in the 1990s primarily through the work of the Media Lab at the Massachusetts Institute of Technology (MIT).³³ In Europe, real-world laboratories are primarily intended to create modern forms of regulation (e.g., in urban development). Real-world laboratories have been used relatively little in management research to date. Empirical approaches dominate in dissertations. The advantage of real-world laboratories lies in their ability to better connect theory and practice.

Design-oriented management research is not only taking place at universities, but also increasingly in practice. University lecturers are increasingly supervising creative research approaches by employees in their companies. This approach is mainly used in bachelor’s and master’s theses in dual study programs, in which the course of study is organized in parallel with practical work. In the past, this has also been done more frequently in external dissertations and postdoctoral theses, e.g., by management consultants. The focus here was more on practical relevance. Solving complex problems requires research by interdisciplinary teams, whose members then receive their degrees in their respective fields. Universities should work with partners in the field to combine such projects into programs that can also build on each other (e.g., to design a sovereign AI from Europe).³⁴

The following figure summarizes various possible forms of design-oriented management research. Here, we distinguish between the type of degrees, the employment relationship of the researcher and the project and program types. In a part-time doctorate of a consulting employee, for example, it makes sense to compare the results of projects from several organizations and derive new insights from them. What seems important in this research approach is that design-oriented research projects based on theoretical foundations³⁵ now focus more strongly on concrete application in practice.

In 2026, we will further develop this approach to management research in the context of designing high-performance systems in which trustworthiness has become an important competitive advantage. One model for this is the start-up ecosystem in Munich, from which other regions can learn.³⁶

Conclusion

High-performance systems are characterized by their success and trustworthiness. In the current transition phase, Europe should seize this as an opportunity.
To do so, companies must master complex realignment processes and become more resilient, digital, and sustainable
Such connective strategic management (Strategy 5.0) is one of the fields of action of trustworthy high-performance systems
Another field of action is the promotion of high-performance teams with a growth mindset
This requires trustworthy partners and closer cooperation between business and politics
Real-world laboratories of change open up new opportunities for design-oriented management research.

Literature

[1] zu Fürstenberg, J., Kloepfer, I., How good we are is revealed in times of crisis – A wake-up call, Piper 2025

[2] Servatius, H.G., Trimming an organization for performance. In: Harvard Manager, 1988, No. 4, pp. 128-134

[3] Katzenbach, J.R., Smith, D.R., The wisdom of teams – Creating the high performance organization, Harvard Business School Press 1993

[4] de Waal, A., What makes a high performance organization, Warden Press 2019

[5] Servatius, H.G., AI as a tool for strategic management. In: Competivation Blog, May 1, 2025

[6] Sommer, U., US corporations are stronger than ever. In: Handelsblatt, December 29, 2025, pp. 1, 4-6

[7] Alvarez de Souza Soares, P., Holtermann, P., AMD wants to end Nvidia’s monopoly. In: Handelsblatt, January 7, 2026, pp. 18-19

[8] Knees, C., Disinformation as a business risk. In: Handelsblatt, January 7, 2026, pp. 20-21

[9] Merten, M., Companies sinking in AI junk. In: Handelsblatt, January 9, 2026, pp. 20-21

[10] Frankopan, P., „What does Europe have besides handbags and champagne?“ (Interview). In: Handelsblatt, December 19/20/21, 2025, pp. 12-13

[11] Fröndhoff, B., et al., Billions for restructuring. In: Handelsblatt, November 26, 2025, pp. 1, 4-5

[12] Servatius, H.G., Disruption of management education for AI-based realignments. In: Competivation Blog, October 10, 2025

[13] Servatius, H.G., Development and change in strategic management. In: Competivation Blog, September 19, 2025

[14] Servatius, H.G., Triple strategic realignment. In: Competivation Blog, June 7, 2024

[15] Servatius, H.G., Strategic leadership with contextual and relationship-oriented intelligence. In: Competivation Blog, March 14, 2023

[16] Huchzermeier, D. et al., Economy in reform gridlock. In: Handelsblatt, February 2/3/4, 2026, pp. 1, 6-7

[17] Servatius, H.G., Resilience-oriented strategic management. In: Competivation Blog, March 15, 2024

[18] Meiritz, A., „We will certainly remember a no.“ In: Handelsblatt, January 22, 2026, p. 1, 4-5

[19] Koch, M., Can an alliance of middle powers slow Trump down? In: Handelsblatt, January 22, 2026, p. 5

[20] Bomke, L., Germany’s AI hope. In: Handelsblatt, December 2, 2025, p. 1

[21] Czikszentmihalyi, M., Creativity – Flow and the psychology of discovery and invention, Harper Collins 1996

[22] Dweck, C., Mindset – The new psychology of success, Random House 2006

[23] Nadella, S., Hit Refresh – The quest to rediscover Microsoft’s soul and imagine a better future for everyone, Harper Collins 2017

[24] Murphy, M.C., Cultures of growth – How the new science of mindset can transform individuals, teams and organizations, Simon & Schuster 2024

[25] Herwig, M., Linnhoff, C., New A 45 bridge opened. In: Rheinische Post, December 23, 2025, p. A6

[26] Fukuyama, F., „Trumpism is a cry against modernity“ (interview). In: Handelsblatt, December 5/6/7, 2025, pp. 12-13

[27] Tüngler, M., „Friedrich Merz still has it in his hands“ (interview). In: Handelsblatt, December 11, 2025, pp. 22-23

[28] Grünewald, S., We crisis acrobats – Psychogram of an unsettled society, Kiepenheuer & Witsch 2025

[29] Delhaes, D., Architect of a German research breakthrough. In: Handelsblatt, December 30, 2025, p. 13

[30] Andritzky, J., The country needs visions – For long-term policies with the courage to face the future, Herder 2026

[31] Bomke, L., Revolutionizing the trade with AI. In: Handelsblatt, January 7, 2026, p. 26

[32] Simon, H.A., The sciences of the artificial, 3rd ed., MIT Press 1996

[33] Mitchell, W.J., City of bits – Space, place, and the infobahn, MIT Press 1995

[34] Servatius, H.G., AI and the future of management education. In: Competivation Blog, April 9, 2025

[35] Seckler, C., et al., Design sciences across industries – Building bridges for advancing impactful business research. In: Schmalenbach Journal of Business Research, December 9, 2025

[36] Banze, S., Freisinger, G.M., The Munich code. In: Manager Magazin, February 2026, pp. 30-36

Disruption of management education for AI-based realignments

10.10.25

One of the major challenges facing Europe is the AI-based strategic and organizational realignment of companies. An important prerequisite for success in this task is the improvement of management education within the framework of lifelong learning. In this context, university teaching and human resources development are seeking a new balance between traditional and innovative approaches. Using ten influencing factors, I outline the spectrum of existing characteristics. This presents opportunities for education providers to differentiate themselves in an increasingly competitive international market. Exciting questions arise as to who will continue to drive forward the long-standing disruption of management education and in what form.

In this blog post, I report on my experiences in university teaching and consulting on the topic of personnel development in the context of realignments.

Competitive advantages through AI qualifications

German companies owe their competitive advantages in the past century to a large extent to their globally recognized engineers. Qualifications in the development and application of artificial intelligence (AI) may be similarly important for the future. The conclusion would be that the part of the world that trains and educates its managers and employees best in AI will be the most successful. This opens up new opportunities for cooperation between companies and education providers. Management didactics for AI-based strategic and organizational realignments play an important role here.¹ The first step is to clarify basic terms.

Realignment and transformation

The terms realignment and transformation have one thing in common and one difference. The commonality concerns the effects of change. These are profound in both realignments and transformations. In contrast, when the effects are minor, we speak of improvement. What differs, however, are the forms of change.

By the term realignment (or, better, „innoalignment“) of companies, we mean an innovative approach to coordinating important system elements, e.g., customers, business model, strategy, technologies, employees, organization, and culture. It is important to view these system elements in context. Realignments usually unfold their profound effects over a longer, not clearly defined period of time in many parallel learning steps. For example, digital change has been progressing for decades in ever new waves. This evolutionary character distinguishes realignments from time-limited transformations, in which change takes place in a big leap that often triggers defensive reactions.² In this respect, transformation projects are similar to restructuring projects, which, however, focus on reactive crisis management.

The widely used term „transformation“ leaves unanswered the question of what happens afterwards, for example, if the environment continues to develop dynamically or if the hoped-for success of the transformation fails to materialize. Perhaps it is precisely this illusory nature of the term that is one reason for its unreflective use. It is therefore to be welcomed that renowned scientists such as sociologist Armin Nassehi are now taking a critical look at the topic of transformation.³

It seems that many who use the term transformation actually mean a process of realignment that is not limited in time, or at least hope that a new stable equilibrium will eventually be established after a successful transformation. Realistic management education, on the other hand, should be based on clearly defined conceptual foundations.

In the case of AI-based realignments, which are characterized by a high degree of uncertainty, a realistic understanding of change is an important prerequisite for success. It is also important to avoid mistakes.

Mistakes in AI initiatives

Various studies show that only a small proportion of AI initiatives are successful. There are many reasons for this.⁴ Often^,expectations are exaggerated and there is a lack of well-prepared data. Other possible reasons include unclear guidelines and the uncoordinated use of too many AI tools. However, the main cause probably lies in the area of human resources, ranging from overburdened managers and a lack of training opportunities to employees‘ concerns about their jobs. Companies should therefore attach greater importance to management education when undertaking AI-based realignments. Management didactics is thus increasingly becoming a field of innovation.

Management didactics as a field of innovation

The term „management didactics“ refers to the theory and practice of teaching and learning in the field of management. Important drivers of innovation include fundamental changes in the world of work, which require new management concepts, and the use of AI-supported learning technologies.

Since the term, derived from ancient Greek, was coined by the educator and theologian Comenius in the early 17th century, didactics has evolved in many ways. Nevertheless, management didactics is a field of innovation whose importance has long been underestimated by many. This is evident, among other things, in the fact that university teachers are not required to have any training in didactics, but usually acquire the relevant skills themselves in the course of their work. Such learning by doing naturally leads to varying results.

Management education in search of a balance

In light of new challenges and learning technologies, management education and training are changing. There are a number of influencing factors, each of which can have a wide range of characteristics. One example is the documentation of teaching content, which is already less common in traditional textbooks and more prevalent in microlearning. In light of these influencing factors, management didactics is searching for a new balance between traditional and innovative approaches. This opens up a wide range of possibilities for lifelong learning, which is becoming increasingly important. Educational providers such as universities and publishers are faced with the task of reorienting themselves in this jungle and opening up interesting career paths for learners.

From my practical experience as a professor and consultant, ten influencing factors with a wide range of characteristics have emerged, which I will explain below.

The first two factors are the management environment and the teaching content.

Management environment and teaching content

Compared to today, the management environment used to be relatively static, and the need for lifelong learning was only beginning to emerge. This has changed fundamentally. The result of the highly dynamic environment is a shortening of the half-life of knowledge and skills.

One example is generative artificial intelligence. At the end of 2022, the introduction of the text robot ChatGPT by OpenAI triggered extreme hype.⁵ This has since been followed by a certain disillusionment, and experts are warning of an AI illusion.⁶ On the other hand, domain-specific AI, which specifically combines AI tools with applications, has gained in importance.⁷ These dynamic circumstances necessitate agile adaptation of teaching content. It is important to realistically assess trends and train learners to think critically.

In addition to the dynamics of teaching content, the balance between theory and practice plays an important role. The focus of traditional management studies is on theoretical foundations and concepts that are primarily taught by academically oriented instructors. In recent decades, the importance and influence of management consultants as bridge builders between theory and practice has increased. In a process of additional qualification, external consultants and in-house consultants improve their practical management skills on the job in the form of problem-oriented learning.⁸ Universities would be well advised to integrate this complexity-managing form of continuing education more strongly into their teaching.

This has important implications for the development and documentation of teaching content.

Development and documentation

In the past, the development of pioneering teaching content has been carried out primarily by individual professors who have worked on subject-specific management topics together with their doctoral students. This has resulted in a modular system of distinct elements of specialist knowledge for learners. This traditional concept needs to be supplemented. A new approach is the development of connecting skills by interdisciplinary teams consisting of both more theoretically and more practically oriented members.

Domain-specific AI provides an exciting example of this approach. The teaching content combines AI fundamentals with applications in individual management fields.⁹ Students learn from practitioners how to successfully implement a strategic and organizational realignment of companies.

In the past, teaching content has mainly been documented in the form of subject-specific textbooks, articles, case studies, and lecture notes. Some classic textbooks have been published in new editions over decades and have become very comprehensive. However, such „tomes“ are being written less and less and read even less. It is difficult for learners to distinguish which content in a work of several hundred pages is still relevant and which is not. However, the future documentation of teaching content is still more like a field of experimentation.

AI also plays an important role here. The goal is multimodal microlearning that is interactive and individualized. Content providers and new learning technologies such as learning experience platforms (LXP) are working together to design this microlearning^.

This is accompanied by a change in the theory and practice of management didactics.¹⁰

Theory and practice of management didactics

For a long time, management didactics theory has focused on the passive reception of knowledge. The proportion of creative design, e.g., of new business models, was surprisingly low, even though subjects such as innovation management should focus on this skill. Here, too, a process of change is currently taking place. The focus is shifting toward a more active, self-directed process. Systemic-constructivist pedagogy provides a basis for this.¹¹

The new generation of learners is growing up with AI tools, and intelligent input (prompting) is currently becoming a core competency. AI not only speeds up routine tasks, but also improves the search for creative solutions.¹² Teachers and their organizations have the task of creating suitable learning environments for this.

There are major differences in the practice of management didactics between Germany and the USA, for example. While the focus in Germany is on learning concepts and applying them in exercises, management teaching in the USA has been dominated by case studies for decades. Management consultancies have provided important impetus for problem solving in projects. Collaborative learning places greater emphasis on bottom-up learning processes through internal company teams, in which trainers take on a coaching role.¹³

Innovative education providers are working to incorporate AI-supported project-based learning into education at an early stage. In this way, they are improving the career opportunities of learners and differentiating themselves from traditional universities.

This is changing the business models and scaling options of education providers.

Business models and scaling opportunities

The traditional „business model“ of leading universities is based on the principle of academic freedom, which is enshrined in Germany’s Basic Law. According to this principle, professors have the right to design their teaching content individually. At private business schools that operate at multiple locations, teaching content increasingly comes from module coordinators, who make the materials available to less experienced lecturers as inspiration. In team teaching, interdisciplinary teams with different backgrounds develop the teaching content. These changed business models can be geared toward both cost reduction and the realization of price premiums.

The late Harvard professor Clayton Christensen pointed out as early as 2010 the disruptive potential of business model innovations in management education, which is becoming increasingly apparent.¹⁴

The classic model of university scaling is based on the worldwide distribution of books and articles by management gurus, case studies, and the reputation of educational providers. This reputation enables the high prices of MBA programs and, in some cases, company-specific executive education. With the emergence of Massive Open Online Courses (MOOCs) in the 2010s, new opportunities have arisen for learning platforms to reach a large number of learners at relatively low cost.

It is foreseeable that in the coming years, most companies will need to provide their employees with continuing education tailored to their specific needs in the field of AI.¹⁵ Companies should not underestimate the social explosiveness of this issue if they have to lay off a large number of employees.

Other factors to consider are the channels and trusteeship of management education.

Channels and trusteeship

While traditional classroom learning offers little opportunity for interaction among large numbers of students, small groups are relatively cost-intensive. Online learning has a number of advantages, but often lacks the experiential quality of face-to-face learning through dialogue. Innovative education providers are therefore trying to combine the advantages of different channels with new formats. Blended learning, combining face-to-face and online events, is now widespread. One form of learning with a long tradition that could be experiencing a renaissance is work-integrated learning. Its roots lie in the dual vocational training system common in the skilled trades. A new interpretation is based on the use of digital technologies such as virtual reality (VR).

For several years now, companies have been using VR glasses for training and further education in the workplace. Business schools such as Insead are also experimenting with VR glasses in management education.¹⁶

Publicly funded universities are facing increasing competition in management education from private education providers, which charge higher prices but are also considered more individual and flexible. In addition to full-time courses, these often offer dual programs in which students spend part of their working time at a partner company. Dual study programs are very popular, but in my opinion, they have not yet reached their full potential. One area for improvement is the targeted coordination between learning at the business school and gaining relevant, practical experience in the company.

That is why we offer students individually tailored tasks for action-oriented learning with their practical partners. It becomes exciting when, for example, in a project learning situation on the topic of AI^, the students‘ supervisors also contribute their experience.¹⁷

AI-supported disruption of management education

The outline of these factors with their different characteristics shows the potential for AI-supported disruption in management education.¹⁸ Artificial intelligence provides both new content and new tools for management education and training, with far-reaching implications for the business models of universities, companies, publishers, and providers of learning technologies.

The term business model disruption refers to a business model with a new value proposition for customers and barriers that are difficult for established competitors to overcome.¹⁹ Traditional universities, for example, are struggling to implement changed degree programs with AI as a basic and cross-disciplinary subject, as well as innovative content and learning technologies. The biggest barrier here is the pronounced reluctance of those responsible to change, which makes realignment difficult.

The topic of AI opens up opportunities for disruption in management education on a scale that cannot yet be fully predicted.²⁰ One possible path is through AI-supported improvement of the individual quality of teaching. The following figure summarizes a few points on this topic.

The basic idea is that AI does not replace teachers, but rather supports them in improving the quality of their work. An important guiding principle is that the individual teaching style recognized by AI is retained. Based on specified criteria, AI then performs a strengths and weaknesses analysis using teaching samples. A first starting point is the development of strengths, e.g., the use of illustrative examples, and a reduction of weaknesses, e.g., too much frontal teaching.

One area with potential for optimization among many teachers is the goal of improving students‘ career opportunities in the AI age. An important contribution to this is to teach content that combines technological application experience with social skills.²¹ The actual future skills require a combination of the expertise of various specialists, which AI can support. This is particularly true for complex tasks involving interaction with customers and employees, e.g., in the context of challenging sales talks or conflicts within teams.

One task that has long overwhelmed many teachers is the continuous and targeted updating of their teaching content in the form of examples, questions, and short case studies. AI can also help to overcome this challenge and contribute to the design of new teaching content.

Another point is the use of AI chatbots to help learners explore the teaching content step by step. Google, for example, is pursuing this goal with its Gemini AI model and Guided Learning software, which aims to promote curiosity and critical thinking.²² The vision for the future is that every person will have a personal AI tutor who adapts to their individual learning needs.

In addition to individualizing learning, e.g., in complex projects, collaboration between teachers is becoming increasingly important. AI supports team teaching by helping to connect related topics, e.g., in interdisciplinary tasks.

AI tools that exploit efficiency potential in routine activities create more scope for these qualitative improvements. One example is support in the evaluation of exam performance, which is very time-consuming.

Overall, these and other points are leading to a next generation of management education in which teachers and learners work together with the support of AI. This can help to further develop the mindset in the respective stages of life. The question now is how to successfully implement AI-supported management education in practice.

Implementing concepts for AI-supported management education

Many universities and companies do not seem to be aware of how much management education and training is changing in the age of AI. Although there is no shortage of literature on the theoretical foundations of such a realignment,²³ there is a lack of concepts for practical implementation.

One gets the impression that most players are still in individual experimental phases, which they are going through with limited commitment and caution. Large AI providers are acting differently, offering their customers free training programs in order to retain them. This approach has the advantage that it is at least based on clear objectives. However, it is necessary for universities and companies to implement their own concepts for AI-supported management education.

A hallmark of successful concepts is the connective design of the following three dimensions:

The joint development and continuous updating of relevant interdisciplinary teaching content
The implementation of innovative management teaching methods
Individual adaptation to different target groups, from school pupils and students to professionals and executives.

Many players will not be able to develop and implement these concepts on their own. One foreseeable consequence of the disruption of management education is that private education providers offering outdated teaching content and methods, for example, will eventually disappear from the market.

The greatest pressure to act in the change of educational ecosystems is likely to be felt by companies that need to retrain a large number of their employees in AI-supported realignments. These companies will increasingly seek to collaborate with innovative partners. Therefore, the realization that every disruption presents an opportunity also applies here. Learning ecosystems around the world that understand AI as a game changer are taking advantage of this opportunity.

AI as a game changer for learning ecosystems

In our book on AI as a game changer, published in 2020, we identified shortcomings in German digital policy.²⁴ These shortcomings still exist today in education policy, which should create conditions that promote innovation for the development and application of AI. Important inspiration comes from AI initiatives in other parts of the world. For example, the world’s first AI-only university has been established in the future city of Masdar near Abu Dhabi in the United Arab Emirates. In Saudi Arabia, 86 percent of bachelor’s degree programs now have an AI focus.²⁵

In the global competition for the best AI support for management education, learning ecosystems in which various partners work together are likely to play a leading role. Possible partners include educational institutions, AI and LearnTech providers, as well as companies and public organizations as customers. New forms of cooperation are emerging in the US, China, the Gulf states, and Europe, but the outcome of the race is still open.

Studies on the use of AI assume that for many companies, the initial focus will be on increasing productivity. In addition, AI opens up a wide range of opportunities for business model innovation.²⁶ Both approaches have a major impact on the future of work. In addition to job cuts in routine tasks, new job profiles are emerging, e.g., in the monitoring of AI results.²⁷ Education providers must adapt to this. An important success factor is motivating learners to work with AI.

Combining the best of both worlds

In addition to my work as an honorary professor at the University of Stuttgart, I have long been an external lecturer in dual degree programs at private business schools. Universities and business schools have different strengths that actually complement each other perfectly. In the case of business schools, these include

teachers with experience outside the education sector
students who work in the field alongside their studies, and
companies as practical partners that need to manage an AI-supported realignment.

The strengths of universities, on the other hand, are

a broad spectrum of relevant subject areas such as business administration, computer science, and engineering
professors and doctoral students with a focus on research, as well as
funding from the public sector and third-party sources.

It would therefore seem logical for these two types of providers to cooperate on the subject of AI.

In practice, however, this has been the exception rather than the rule. One possible approach would be joint projects in which actors from both „camps“ work together with clearly defined goals. Currently, however, this is hindered by a pronounced distance between public and private business schools.

One promising way to combine the best of both worlds is to make the solution of complex problems in companies the subject of final theses. One example is the use of AI in sales and the cooperation with start-ups such as the Berlin-based company Parloa. In this context, teachers and students jointly train the application of problem-solving methods in projects, a skill that is becoming increasingly important in the age of AI. At the same time, this creates an application-oriented alternative to the strongly empirical management research that has become dominant in recent decades but is often of relatively little relevance to practitioners.

Conclusion

Training and further education play an important role in the AI-supported strategic and organizational realignment of companies
A number of influencing factors come together in the design of innovative management didactics
The disruptive potential of AI can contribute to market consolidation in management education
When implementing AI-supported management education, innovative learning ecosystems are changing the rules of competition.

Literature

[1] Servatius, H.G., Development and change in strategic management. In: Competivation Blog, September 19, 2025

[2] Servatius, H.G., Triple strategic realignment. In: Competivation Blog, June 7, 2024

[3] Nassehi, A., Critique of the grand gesture – Thinking differently about social transformation, 2nd edition, C.H.Beck 2024

[4] Merten, M., How can AI really add value? In: Handelsblatt, September 2, 2025, pp. 24-25

[5] Bomke, L., Holtermann, F., Scheuer, S., Between hype and disillusionment. In: Handelsblatt, August 29/30/31, 2025, pp. 48-53

[6] Dörner, A., Holtermann, F., Wiebe, F., The AI illusion. In: Handelsblatt, August 22/23/24, 2025, pp. 1, 4-7

[7] Servatius, H.G., Development of AI technologies. In: Competivation Blog, February 19, 2025

[8] Servatius, H.G., Learning to design solutions for complex management problems. In: Competivation Blog, July 15, 2025

[9] Servatius, H.G., Competitive advantages with knowledge-specific AI. In: Competivation Blog, February 11, 2025

[10] Floor, N., This is learning experience design – What it is, how it works, and why it matters, Pearson Education 2023

[11] Reich, K., Systemic-constructivist pedagogy – Introduction to the fundamentals of interactionist-constructivist pedagogy, Beltz 2010

[12] Servatius, H.G., AI as a tool for strategic management. In: Competivation Blog, May 1, 2025

[13] Hernandez, N., Collaborative learning – How to upskill from within and turn L&D into your competitive advantage, Kogan Page 2023

[14] Christensen, C.M., Horn, M.B., Johnson, C.W., Disrupting class – How disruptive innovation will change the way the world learns, McGraw-Hill 2010

[15] Servatius, H.G., Generative AI and Mass Customized Action Learning. In: Competivation Blog, August 28, 2023

[16] Stern, I., Epstein, A., Landau, D., Making VR a Reality in Business Classrooms. In: Harvard Business Review, November 8, 2021

[17] Jones, D., Modern PBL – Project-based learning in the digital age, Teacher Goals 2024

[18] Servatius, H.G., AI and the future of management education. In: Competivation Blog, April 9, 2025

[19] Servatius, H.G., How to recognize a disruptive business model. In: Competivation Blog, July 27, 2016

[20] Servatius, H.G., Disruption in management education. In: Competivation Blog, May 29, 2019

[21] von Schwanewede, S., Securing your own job value in the age of AI. In: Handelsblatt, September 16, 2025, pp. 22-23

[22] Scheuer, S., Chatbot Gemini to help with learning. In: Handelsblatt, September 8, 2025, p. 24

[23] de Witt, C., Gloerfeld, C., Wrede, S.E. (eds.), Artificial intelligence in education, Springer 2023

[24] Kaufmann, T., Servatius, H.G., The internet of things and artificial intelligence as game changers – Paths to Management 4.0 and a Digital Architecture, SpringerVieweg 2020, p. 203 ff.

[25] Witsch, K., Bomke, L., Rogg, I., The new tech superpowers. In: Handelsblatt, October 2/3/4/5, 2025, pp. 42-48

[26] Hübner, G., Artificial intelligence as a growth driver. In: Handelsblatt, November 28, 2025, p. 28

[27] Burkhardt, K., Efficiency and new tasks. In: Handelsblatt, September 25, 2025, p. 30

Learning to design solutions for complex management problems

15.07.25

In light of major challenges such as the resilient, digital, and ecological realignment of companies, industries, and regions, the proportion of problem-oriented learning in education and training must increase. The ability to design solutions for complex management problems is crucial for success. Management theory for complex evolutionary systems provides an important foundation for this. The application of this approach, which has developed over the last few decades, has hardly been taught at business schools to date. One practical approach is the DSCMP method, which we have tested in many projects. DSCMP stands for Designing Solutions for Complex Management Problems.

In this new blog post, I explain the theoretical foundations of the DSCMP method and a general framework that can be adapted to specific problem situations.

What students and practitioners find difficult

An exam question that most students find difficult is: Using an example, explain a concept and approach for AI-based strategic realignment. It makes little difference whether the question asks for a digital or ecological realignment. However, designing solutions for complex management problems is not only difficult for students, but also for experienced practitioners in companies. Apparently, neither group is familiar with suitable theoretical foundations or a practical approach that they could apply. I would therefore like to begin by explaining the theory.

Management theory of complex evolutionary systems

Management theory of complex evolutionary systems has emerged from the combination of three lines of development, the application of which in management enables better strategic realignments.¹ These paths, which I outline below, are:

The path from ecosystems to system theories
the application of evolutionary theories in economics, and
a transfer of the theory of complex adaptive systems to the solution of management problems.

The term ecosystem was defined by biologist Arthur Tansley (oikos, the house, and systema, the connected) in 1935, among others. The field of cybernetics, which has been shaped by Norbert Wiener and others since the 1940s, deals with the control of systems. In 1959, Stafford Beer defined the term management cybernetics.² Since the 1950s, a general systems theory has developed. According to this theory, open systems are in dynamic exchange with their environment. The sociological systems theory founded by Talcott Parsons regards actions as constitutive elements of social systems. In 1951, Parson developed the AGIL scheme (adaptation, goal attainment, integration and latency) for the structural and functional analysis of social systems.³ At the University of St. Gallen, Hans Ulrich has been developing the concept of system-oriented management theory since the 1960s.⁴ Since the 1990s, the terms economic, stakeholder, start-up, and AI ecosystems have gained importance.

Evolutionary theories (from evolvere, to develop) have a long history in various disciplines. Of particular importance for economics is the concept of spontaneous order developed in the 1960s by Friedrich August von Hayek, who later won the Nobel Prize in Economics (1974).⁵ This is the result of self-organizing processes that emerge over time and are based on rules that can change. An important basis for the concept of an evolutionary organizational theory of the Munich School around Werner Kirsch⁶ is the theory of communicative action developed by the philosopher Jürgen Habermas.⁷ An evolutionary process in management is characterized by a dynamic sequence of imbalances.

The interdisciplinary theory of complex adaptive systems was developed at the Santa Fe Institute in New Mexico (USA), which was co-founded in 1984 by Nobel Prize winner in physics Murray Gell-Mann (1969). Such a system absorbs information about its environment and its own interaction with this environment, recognizes patterns, and condenses them into competing models. The resulting actions feed back into these models. An important management recommendation is to create suitable conditions for more self-organized interaction between competent actors.⁸ Agile methods are based on this. The theory of complex responsive relationship processes emphasizes the importance of local, nonlinear interactions between actors, which give rise to patterns that are difficult to predict.⁹

The theory of complex evolutionary systems has initiated a paradigm shift in strategic management, which has been driven primarily by successful digital companies since the 1990s.¹⁰ The characteristics of this management theory, which is new to many established companies, are openness, dynamism, connectedness, non-linearity, emergence, path dependency, adaptivity, self-organization, and learning loops.

The question now is how this theoretical foundation can help teachers and learners in practice to design solutions for complex management problems.

Approach using the DSCMP method

The DSCMP method was developed as part of our consulting, teaching, and research activities. Consultants are usually called in when organizations are looking for support in solving complex management problems. However, consulting only leads to lasting success if managers and employees are successfully taught the relevant skills.

Designing solutions for complex management problems requires a conceptual framework that project teams can adapt to the situation at hand. The following figure shows the main phases of the iterative process used in the DSCMP method. The didactic challenge lies in teaching the ability to adapt this approach to new complex problems.

In the DSCMP method, the first phase involves forming interdisciplinary program and project teams that report to a management committee. The structure and composition of these teams may change as the work progresses.

In phase 2, the task is to bring together relevant information and different perspectives. The goal is to understand a complex problem and its causes and describe it as accurately as possible.

This is followed by the important step of designing creative solutions that bring everything together. Examples include the development of a hydrogen value chain and the conversion to climate-resilient cities. Communication with stakeholders from politics, science, and society is becoming increasingly important but is difficult to implement.¹¹ Such cooperation requires dialogue-based action based on a position of strength, e.g., with the help of new methods such as connective design.

Depending on the type of problem, possible approaches can be tested in pilot solutions, prototypes, or minimum viable products.

A variety of methods have been developed for testing the „pilots.“ It is also important to identify implementation difficulties and adapt the solutions in rapid learning loops.

The implementation of a promising solution begins with planning the scaling and further financing. The Objectives and Key Results (OKR) method has proven useful for formulating goals and key results.

The final phase is then the implementation of scaling within the framework of programs and projects, a review of success, and regular reflection on interim results.

The success of such an approach depends crucially on the skills and mindset of the teams and managers involved.

Implications for management education

Important implications for management education can be summarized in three points:¹²

Learning from complex current problems
interdisciplinary work on these problems in projects
the use of teachers with leadership experience.

The reality at most universities is far from this. However, this opens up a wide range of opportunities for innovative education providers. In our practical work, we use the DSCMP method in bachelor’s and master’s degree programs as well as directly in companies as part of customized management education. One focus of our research is on supporting problem-oriented project-based learning through artificial intelligence (AI). The benefit for all learners lies in improving their chances in a labor market that is currently undergoing dramatic change.

Artificial intelligence (AI) is changing the world of work

Dario Amodei, founder of the AI start-up Anthropic, predicts that AI will destroy half of all entry-level office jobs. Classic clerical work, which is characterized by repetitive, analytical, and administrative tasks, will be particularly affected. This makes it all the more important for job seekers to be able to demonstrate AI skills. Past experience with digitalization shows that jobs tend to change rather than disappear completely. It is therefore important to first learn how to use AI to perform time-consuming tasks more productively, thereby gaining more time to solve complex problems. In training and continuing education on the path to becoming a manager, the use of AI to manage complexity is crucial for success.¹³

Conclusion

Strategic realignments require a suitable theoretical foundation, innovative approaches, and problem-oriented learning
One such foundation is the management theory of complex evolutionary systems
A creative phase in the Designing Solutions for Complex Management Problems (DSCMP) approach is the connecting design
Innovative education providers play an important role in applying these foundations and approaches within the framework of problem-oriented learning.

Literature

[1] Servatius, H.G., Triple Strategic Realignment. In: Competivation Blog, June 7, 2024

[2] Beer, S., Cybernetics and Management, English Universities Press 1959

[3] Parsons, T., The Social System, The Free Press 1951

[4] Ulrich, H., The Enterprise as a Productive Social System, Haupt 1968

[5] von Hayek, F.A., The Constitution of Liberty, University of Chicago Press 1960

[6] Kirsch, W., Seidel, D., van Aaken, D., Evolutionary Organization Theory, Schäffer-Poeschel 2010

[7] Habermas, J., Theory of Communicative Action (2 volumes), Suhrkamp 1984

[8] Gell-Mann, M., The Quark and the Jaguar: From the Simple to the Complex, Piper 1994, p. 53

[9] Stacey, R.D., Complex Responsive Processes in Organizations – Learning and Knowledge Creation, Routledge 2001

[10] Servatius, H.G., Learning from Successful Digital Companies. In: Competivation Blog, July 12, 2024

[11] Servatius, H.G., Designing Innovative Stakeholder Ecosystems. In: Competivation Blog, January 10, 2023

[12] Servatius, H.G., AI and the Future of Management Education. In: Competivation Blog, April 9, 2025

[13] Knees, L., Maier-Brost, H., How secure is my job from AI? In: Handelsblatt, July 4/5/6, 2025, pp. 54-55

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