connective strategic management | Competivation
Design-oriented innovation research on AI applications

Design-oriented innovation research on AI applications

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.

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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.

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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:

  1. Action research, pioneered by social psychologist Kurt Lewin. Lewin advocated for a connection between theory and practice to solve real-world problems.
  2. 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.
  3. 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.
  4. 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.
  5. Action Design Research (ADR), described by various scholars, which combines Action Research and Design Science Research.
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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.

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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.

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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.

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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

Designing trustworthy high-performance systems

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

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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.

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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.

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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.

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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

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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.

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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.

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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:

  1. Truthfulness (clear identification of problems)
  2. focus (successful national projects)
  3. participation (making one’s own contribution clear)
  4. fairness (unreasonable demands must be perceived as fair)
  5. culture of debate (dealing more productively with changes in perspective) and
  6. 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.

Lernprozess Innovationsstrategie

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

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