Strategy 5.0 | 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

Development and change in strategic management

19.09.25

The task of strategic management is to shape corporate development and overcome challenges. New opportunities and threats mean that board members and managing directors are constantly faced with the need to learn. Improved didactics in executive education and training should take this change in strategic management into account. In the current phase of upheaval, the focus is on AI-based strategic and organizational realignments. We refer to the combination of these fields of action, which are changing the labor market and requiring new leadership skills, as innostrategizing.

In this blog post, I explain the stages of development of strategic management and the paradigm shift that is shaping the evolution of the field.

AI is also changing the job market for young professionals

The increasing importance of artificial intelligence (AI) is leading to a decline in demand for clearly structured, repetitive fields of activity^, even for young professionals^.Many of these tasks are already being performed faster, more cost-effectively, and with sufficient quality by AI. At the same time, new tasks are emerging, e.g., in AI training and the use of AI tools. In addition to AI skills, other qualifications are becoming more important. These include, for example, the ability to work on interdisciplinary projects. As this change affects all areas of management, innovative education providers are realigning their bachelor’s programs. In addition, the requirements for managers are also changing.

New requirements for managers

In the past, completing an MBA program increased the likelihood of a successful management career. For example, 18 percent of the board members of German listed companies have a Master of Business Administration (MBA), 88 percent of whom obtained their degree abroad. An important motivation for pursuing an MBA program is the desire to develop further and improve one’s own strategic skills. For business economists and especially for graduates of technical degree programs, MBA programs at renowned universities act as career accelerators. For universities in Germany and abroad that offer MBA programs, it is important to note that the challenges facing companies and thus also the field of strategic management have been undergoing fundamental changes for some time. Innovative education providers are equipping their students to cope with the complexity associated with these changes. The negative effects of US ’s policies on the country’s education system are an opportunity for Europe that universities should take advantage of.

Of particular importance here is an understanding of the changes in strategic management over the course of its development.

Stages of development in strategic management

We have divided the development of strategic management since the 1960s into the following stages, which characterize the respective focus:³

Market- and finance-oriented (Strategy 1.0)
technology- and innovation-oriented (Strategy 2.0)⁴
sustainability-oriented (Strategy 3.0)⁵ and
resilience-oriented to overcome the current multi-crisis (Strategy 4.0)⁶ .

Parallel to the momentum of these stages, the importance of a connective design is increasing. By this we mean

to plan and implement
objects, systems, or problem solutions
carried out jointly by actors from different disciplines, levels, or organizations.

We consider such connective design to be the fifth stage of development in strategic management (Strategy 5.0). This stage connects and expands on the previous stages.⁷

An important foundation for connective design was laid by Nobel Prize winner Herbert Simon (1978) in his book The Sciences of the Artificial, which has shaped design theory.⁸ Even though this groundbreaking work is little known in Germany, hidden champions have been practicing this management approach for decades, which deals with questions such as how to connect new customer needs and technologies.

University teaching on strategic management still focuses primarily on the first stage of development, which is market- and finance-oriented. The second and third stages have given rise to the independent disciplines of technology, innovation and entrepreneurship, and sustainability. However, the integrative aspect of connecting the stages is usually neglected. In addition, there are the specialist areas of human resource management, organization, IT management, and change management, which are also often not linked to strategic management.

Connective design

Although the ability to create connections is rarely taught at universities, it has always been and continues to be relevant at all strategic levels. This is illustrated by the following tasks:

Designing business portfolios with the aim of permanently increasing company value (Strategy 1.0)
designing the innovation system of companies by connecting relevant fields of action and innovation ecosystems (Strategy 2.0)
designing the sustainability system of companies and GreenTech ecosystems, as well as jointly overcoming conflicts of interest between economics, ecology, and social issues (Strategy 3.0)
the design of resilient systems by connecting the levels of government, companies, and individuals, e.g., to overcome geopolitical crises (Strategy 4.0)
designing connections between the stages of development, e.g., in the areas of sustainability innovation and climate resilience (Strategy 5.0).

In addition to these stages of development and a unifying perspective, the change of strategic management is characterized by a paradigm shift.

Paradigm shift in strategic management

The term paradigm describes a fundamental pattern that serves as a guide in a particular field. In science, a paradigm forms a framework for theories, concepts, and practices. A paradigm shift is a transition from an older to a new fundamental pattern. The science historian Thomas Kuhn uses the term to describe scientific revolutions.⁹ One of the critics of this idea is the philosopher Stephen Toulmin. For him, a scientific paradigm is a loosely connected bundle of individual theories that must prove themselves in an evolutionary process.¹⁰ The paradigm shift in strategic management has a rather evolutionary character.

Since the 1990s, this paradigm shift has been taking place from top-down-oriented analyses to a growing dynamic, complexity, and uncertainty emanating from successful digital companies and a changed geopolitical landscape.¹¹ Analysis in the old paradigm aims to break down problems. The following figure summarizes the factors that characterize the evolutionary paradigm shift.

The transition from the old to the new paradigm is changing the influence of various schools of strategy. The analysis-oriented positioning school has lost importance. A combination of other schools of strategy, such as the entrepreneurial school and the learning school, has become more relevant.¹²

Another important change concerns the mindset of managers. While a rather static self-image dominates in many established companies, the culture of successful digital companies is characterized by a dynamic self-image (growth mindset), which often begins to develop in childhood.¹³

The focus of the old strategy paradigm is on increasing company value. The new paradigm focuses more on business model innovation through stakeholder ecosystems. Artificial intelligence (AI) now plays an important role in managing the complexity associated with this.¹⁴

Strategy processes and projects have also changed. The old paradigm was dominated by a separation between strategy development and implementation by distinct organizational units. This separation encourages the emergence of silo cultures. The new paradigm is characterized by interdisciplinary projects using agile methods such as design thinking and Scrum. A common feature of these projects is the iterative approach in learning loops.¹⁵

The internal organization also differs accordingly. In the old paradigm, responsibility for strategic management lies at the management level. The new paradigm is characterized by more decentralized, self-similar (fractal) processes and structures. Strategy units with different tasks are connected to each other and to a central office.¹⁶

Currently, an important change is emanating from the political framework conditions. The old paradigm is based on the idea that prosperity arises from a rule-based world order. This idea is increasingly being called into question. Due to growing political threats, the framework conditions for strategies have become much more uncertain. A current example is the tariff crisis initiated by the US government. In this situation, the world seems to lack a reliable compass.¹⁷

AI-based strategic and organizational realignments

In summary, it can be said that the change in strategic management is characterized by the following two determinants:

Development in stages with an increasingly important connective perspective, and
an evolutionary paradigm shift.

Characteristic of the early approaches to strategic management according to the old paradigm are top-own-oriented analyses based on problem decomposition. These approaches determined the first stage of development and the beginning of the second stage. In contrast, the new paradigm focuses on growing dynamics, complexity, and uncertainty.

If one is looking for a term to describe current strategic management, the neologism „innoalignment“ comes to mind. By this we mean the connection of AI-based strategic and organizational realignments. The strategic realignments are aimed at making companies more resilient, digital, and sustainable.¹⁸ In organizational realignments, AI-supported performance management measures the success of leaner structures, networked processes and projects, and an innovative AI platform architecture.¹⁹ There are still few examples of such innoalignment. This makes it all the more important for application-oriented research and teaching to focus more on this topic. The further development of management didactics plays a central role in this.

Key players in management didactics

In recent decades, various players have shaped didactics in management education. Their approaches have specific advantages and disadvantages. In view of new challenges, innovative education providers are currently developing didactic concepts that focus on AI-supported solutions to complex management problems. ²⁰

The prevailing management didactics at universities in Central Europe have long been function- and industry-specific subject concepts. The focus of business administration functional teaching (e.g., finance) and technical industry teaching (e.g., mechanical engineering) is on training specialists who work in hierarchies with clearly defined organizational units. This approach encourages the emergence of interface problems that are difficult for companies to overcome due to a rigid culture.

In the USA, Harvard Business School transferred the case study method from legal education to management education in 1920. The basic idea is that lecturers condense interesting practical examples into case studies, which form the focus of teaching. The promise of benefit here is to learn from actors who have attempted to solve a specific problem. This didactic approach differs fundamentally from subject-based learning. One disadvantage of the case study method is that the rapid transfer of a known solution often does not do justice to the complexity of new tasks.

The major strategy consultancies, which are influenced by the teaching methods used at business schools, have supplemented the case study approach with a specific form of further training for their consultants. This on-the-job training focuses on teaching the ability to identify problems, structure them, and solve them analytically. The final step is to sell the solutions by having experienced consultants convince decision-makers. A common criticism of this classic approach by consultants is that they leave their clients to implement the solutions on their own. This is where performance management, which emerged in the 1980s, comes in with the formulation of objectives and key results.

Successful digital companies and their venture capitalists rely less on external consultants and more often work on interdisciplinary projects themselves using agile methods such as design thinking or Scrum. In this iterative approach, the actors apply the concept of learning loops, which is well known in organizational development. The lean startup method is also based on this approach.

Since all of these approaches have specific strengths and weaknesses, innovative education providers build on what is already known and develop it further. The result is project-based learning that focuses on AI-supported, collaborative design of solutions for complex management problems.²¹ Such action-oriented learning can begin with simple problems and then move on to individual learning steps addressing current challenges for which there are no known solutions yet. The new education providers have recognized that this approach is best mastered by a heterogeneous teaching staff in which academics work together with practitioners who have different backgrounds and experience. An interesting question is how organizations can promote the further development of a dynamic self-image. The role model function of leadership plays an important role here.

This change in strategic management, combined with innovative didactics, opens up an opportunity for Europe that the „old continent“ should seize.

Change as an opportunity for Europe

Strategic management started as an import from the US, with its first stage of development spreading across Europe since the 1970s. Europe has been overtaken in many areas by the waves of digitalization, which have mainly originated from US companies. At the same time, changing geopolitical conditions are increasing Europe’s dependence on the US and China. It therefore seems high time for Europe to refocus on its strengths. Politicians have begun to rethink their approach, placing greater emphasis on competitiveness once again. One opportunity of global significance is the combination of digitalization and sustainability (digital green tech), where Europe should strive to take a leading role.²² The basis for this is an improvement in education systems.

The outlined change in strategic management creates a framework for joint programs between politics, science, business, and society in specific growth areas, such as the realignment of power grids with AI.²³ This depends on the ability to design solutions for complex management problems. Overall, this change represents an opportunity for Europe if it succeeds in becoming more resilient in crisis management through a joint effort.

Advanced didactics in management play a central role in this. These methods must also address the question of what causes the basic patterns of error that Germany has made in the past, for example in digitization and the energy transition. An important insight is that such basic patterns of error are the fragmented interests of individual actors or groups. The theory and practice of connective design can help to overcome this basic pattern of error.

Conclusion

The development of strategic management has proceeded in stages, with the importance of a connective perspective increasing
Parallel to this, there has been an evolutionary paradigm shift with a change in a number of factors
These two determinants shape innostrategizing, which combines AI-based strategic and organizational realignments
To this end, innovative education providers are further developing management didactics
Europe should see this increasingly apparent change as an opportunity.

Literature

[1] Bomke, L., Müller, A., Telser, F., AI displaces career starters. In: Handelsblatt, August 12, 2025, pp. 16-17

[2] Westkämper, A., On the board thanks to an MBA – that’s what matters. In: Handelsblatt, July 18/19/20, 2025, pp. 54-55

[3] Servatius, H.G., Strategy 5.0 for overcoming new challenges. In: Competivation Blog, June 28, 2022

[4] Servatius, H.G., Evolution of strategic management. In: Competivation Blog, June 28, 2024

[5] Servatius, H.G., Sustainability-oriented strategic management. In: Competivation Blog, August 15, 2024

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

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

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

[9] Kuhn, T.S., The structure of scientific revolutions, Suhrkamp 1996

[10] Toulmin, S.E., Critique of collective reason, Suhrkamp 1983

[11] Servatius, H.G., Learning from successful digital companies. In: Competivation Blog, July 12, 2024

[12] Mintzberg, H., Ahlstrand, B., Lampel, J., Strategy safari: A journey through the wilderness of strategic management, Carl Ueberreuter 1999

[13] Dweck, C., Self-image – How our thinking causes success or failure, 7th edition, Piper 2017

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

[15] Servatius, H.G., GenAI-based strategic learning loops as a connecting process pattern. In: Competivation Blog, November 1, 2024

[16] Servatius, H.G., Fractal organization of strategy 5.0 labs. In: Competivation Blog, March 28, 2023

[17] Riecke, T., The struggle for a new world order. In: Handelsblatt, August 8/9/10, 2025, pp. 24-25

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

[19] Servatius, H.G., Process-oriented AI for increased productivity. In: Competivation Blog, March 12, 2025

[20] Servatius, H.G., AI and the future of management education. In: Competivation Blog, April 9, 2024

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

[22] Servatius, H.G., Achieving success in digital greentech with a Strategy 5.0. In: Fesidis, B., Röß, S.A. Rummel, S. (Eds.), (Towards a Climate-Neutral Company through Digitalization and Sustainability), SpringerGabler 2023, pp. 72-94

[23] Stratmann, K., Build less, digitize more. In: Handelsblatt, August 12, 2025, pp. 20-21

Competitive advantages with knowledge-based AI

11.02.25

In the past, there has probably never been a battle for competitive advantage that has been as dynamic as the current race in the field of artificial intelligence (AI). New opportunities are arising for Europe with knowledge-specific (domain-specific) AI. These opportunities build on the traditional strengths of the „old continent“. In order to catch up, it seems necessary to take a closer look at the topic of knowledge and its long history of development.

As part of our series on AI as a tool for strategies, this new blog post follows on from my explanation of strategic learning loops. First of all, it deals with the combination of knowledge management and AI technologies in the context of the fifth development stage of connective strategic management.

Battle for leadership in AI

The five companies with the highest market capitalization worldwide (as of December 2024) are Apple, Nvidia, Microsoft, Amazon and Alphabet. Artificial intelligence is an important value driver. Apple is worth 3.7 trillion euros. All 40 DAX companies together are only worth 1.9 trillion euros.¹

At the end of January 2025, the Chinese start-up Deepseek surprised the global public with a new AI language model that is said to be able to compete with the best models from Western tech giants, but requires less computing power and costs less. The news triggered a slide in US technology stocks. In the meantime, share price losses amounted to one trillion US dollars. The company, founded by Liang Wenfeng in 2023, relies on open source, i.e. the software is freely available to others. It is also said to have been trained without high-tech chips. This raises the question of whether the billions invested by US companies are really necessary. Deepseek’s good price-performance ratio is probably the result of a combination of different approaches, e.g. the composition of many small expert models, of which only the relevant ones are activated.²For European AI providers with less capital strength, this development may represent an opportunity.

Competitive advantages with AI from Europe

When it comes to artificial intelligence, Europe faces the task of catching up and reducing its dependence on large tech companies. It is also important to secure critical infrastructures and protect the intellectual property of organizations based here. This is particularly important for the many hidden champions and their outstanding expertise in specialist areas. After the hype and some disillusionment with large language models, new opportunities are now emerging for an AI strategy that builds on the strengths of the European economy. Knowledge-specific (domain-specific) artificial intelligence plays an important role in this , providing competitive advantages for many small and medium-sized companies. The Heidelberg-based start-up AlephAlpha has developed a new approach to this.

The advantage for companies is that they can design and operate language models with their own knowledge. Today’s models are based on the transformer architecture and a tokenizer that recognizes language patterns. For this purpose, large volumes of text are analyzed and broken down into individual components (text segmentation). AlephAlpha’s T-Free approach and its AI model Pharia work differently. T-Free stands for tokenizer-free and continuously processes groups of three adjacent characters. This makes it easier to adapt to other languages and terminologies. Together with the semiconductor manufacturer AMD and the Finnish start-up SiloAI, which was acquired by AMD, AlephAlpha has found a way to train industry- and company-specific terms („languages“) with significantly improved performance using T-Free. The approach also helps to increase AI sovereignty^(3).

A consortium of companies, universities and supercomputing centers is currently developing an AI for Europe. Peter Sarlin from SiloAI sees the new Open Europe LLM project as a „moonshot“. Participants from Germany include AlephAlpha and the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS). Both the code and the research will be published as open source. The European Commission is to provide up to 54 million euros over the next three years. In international comparison, this sum is relatively small. However, a European AI that becomes a public good will significantly increase sovereignty^(4).

According to experts, Europe has the opportunity to gain a competitive edge in artificial intelligence if it succeeds in combining the following four success factors:⁵

Improved cooperation between politics, science, business and society
a focus on knowledge-specific AI applications
pooling resources to overcome disadvantages of scale and
the creation of trustworthy AI as a differentiating feature.

Such a combination requires connective strategic AI management. While French President Emmanuel Macron wants to invest 150 billion euros in European AI start-ups, the topic of artificial intelligence is unfortunately barely mentioned in the German parliamentary election campaign.

In the following, I would like to outline how knowledge management can be successfully combined with AI technologies.

Combining knowledge management with AI technologies

The collaboration between humans and AI works in a similar way to pole vaulting. The pole is a tool that enhances the jumper’s abilities if they master the tool.

The use of AI changes people’s knowledge work in the following three dimensions:

Time savings through automation of routine activities
Expanding skills in processing both data-intensive and unstructured tasks and
individualized learning for the further development of human skills.

The potential of AI as a tool lies in the interaction of these dimensions.

With the combination of knowledge management and AI technologies, a new way of achieving competitive advantages is now emerging. The starting point is the activation of companies‘ specific knowledge and skills. Added to this is the use of the potential of AI to expand competencies and thus to differentiate themselves from the competition. The third and decisive point is the systematic improvement of skills that combine knowledge and AI. This requires targeted training and further education.

For Jeanette zu Fürstenberg, Head of Europe at the US investment company General Catalyst, the opportunities for the European economy lie in combining the big data and knowledge of established companies with AI technologies^(6).

In the following, I would like to explain the connection between knowledge management and artificial intelligence and discuss the implications for strategic management in this and the next blog posts.

Knowledge for innovative business models has a long history of development from ancient Greece to today’s knowledge society. In the 1990s, the conviction prevailed that the creation of new knowledge is an important source of competitive advantage. However, this hype surrounding knowledge management was followed by disillusionment. At the same time, US start-ups succeeded in linking knowledge-based value creation and value enhancement with digital business models.

The development of AI technologies has progressed from symbolic AI and neural networks to generative AI (GenAI). In 2024, four AI researchers were awarded Nobel Prizes. But the hype surrounding large language models is turning into disillusionment. Small language models have a number of advantages. They are cheaper and easier to adapt to specific applications. Here, too, the question arises as to how the dangers of AI can be contained.

The combination of these two topics leads to the realization that knowledge-specific AI is an important process and design element in strategies. A distinction can be made between the corporate strategy level and the functional strategy level. AI is a new tool for supporting strategy processes and the collaboration of strategy teams. In addition, AI enables the design of innovative products, services and business models. At the functional level, AI makes important contributions to increasing the productivity of connected business processes. In addition, AI-supported, agile performance management leads to better complexity management than traditional approaches.

Since knowledge management forms a basis for the use of AI technologies, I would first like to outline the development of the topic of knowledge from ancient Greece to the knowledge society.

From ancient Greece to the knowledge society

In ancient Greece in the 3rd century BC, the philosopher Plato and his student Aristotle discussed the question of whether deductive or empirical theories of knowledge lead to the acquisition of knowledge.

At the beginning of the modern era, Rene Descartes (1596-1650) propagated a separation between the subject of knowledge and the object of knowledge. This so-called Cartesian division was to occupy science for a long time to come.

The German philosopher Immanuel Kant (1724-1804) attempted a synthesis. Logical thinking and experience work together.

At the beginning of the twentieth century, American pragmatism, with representatives such as William James, was concerned with the relationship between knowledge and action.

In 1969, Peter Drucker coined the concept of a knowledge society characterized by knowledge work and knowledge workers^(7.

The work of Chris Argyris and Donald Schön on single loop and double loop learning,⁸which formed the basis for the concept of a learning organization, has been of great practical relevance since the late 1970s.

Surprisingly, the topic of knowledge did not play a decisive role in the resource-oriented view of strategic management that emerged in the early 1990s.

From today’s perspective, we define knowledge as a resource and the result of learning processes that people create in exchange with teams, organizations and artificial intelligence.

Creation of new knowledge as a source of competitive advantage

The concept of implicit or tacit knowledge, which the natural scientist and philosopher Michael Polanyi coined back in the 1950s, is important for the creation of new knowledge.⁹In the case of tacit knowledge, someone knows how to do something, but their knowledge is implicit in their skills. It is difficult to document verbally or in writing in the form of explicit knowledge.

In the mid-1990s, Japanese scientists Nonaka and Takeuchi described how new knowledge as a source of competitive advantage arises from the following four forms of knowledge exchange:⁽¹⁰

From implicit to implicit (socialization)
from implicit to explicit (externalization)
from explicit to explicit (combination) and
from explicit to implicit (internalization).

These forms of knowledge exchange are crucial to the success of hidden champions. The combination of knowledge, skills and action has a long tradition there. The creation of new knowledge, the development of skills and their implementation in practical action often take place in learning processes in which – similar to sport – demonstration and imitation play an important role. These learning processes can be documented and scaled using videos, for example.

This provides new impulses for the application of knowledge-specific artificial intelligence

Hype and disillusionment in knowledge management

In the second half of the 1990s, knowledge management experienced a hype phase, which was followed by disillusionment. The hype was mainly triggered by the book The Knowledge Creating Company by Nonaka and Takeuchi, which deals with knowledge management in Japanese companies.

Ultimately, however, the importance of tacit knowledge has not really been understood „in the West“. The focus of companies and consultants has been on extracting and synthesizing existing explicit knowledge („if HP knew what HP knows…“). This proved to be difficult and costly and contributed to disillusionment in the 2000s.

A pragmatic approach that linked knowledge, skills and action did not play a major role in the publications of the time.

Knowledge-based value creation, value enhancement and AI-based business models

Knowledge-based value creation, value enhancement and the connection with digital business models are the subject of our book WissensWert ⁽KnowledgeValue⁾, published in 2001.¹¹Work on this began in the mid-1990s, inspired by the increasing importance of knowledge management. It followed on from the „reengineering wave“ and IT-based innovations in routine processes. Our initial hypothesis was that knowledge-based value creation and value enhancement with knowledge open up new opportunities for achieving competitive advantages.

At the same time, new digital business models have emerged with internet technologies, initially in online retail (electronic business). Following the collapse of the new economy, start-ups such as Amazon, Google and Facebook have achieved leading market positions and have become the most valuable companies in the world.

Europe has become heavily dependent on digital business models. Looking back, it is astonishing how little people here have noticed that business model innovations based on AI applications have emerged since the turn of the millennium.

In the early 2000s, then Princeton computer science professor Fei-Fei Li began building the largest database in AI research (Computervision, later ImageNet). One user was the online bookseller Amazon. Founded in 1994, the company is regarded as the inventor of AI-based personal product recommendations.¹²Since 2003, Amazon has been using the item-to-item collaborative filtering method for this purpose.

Another AI user was Facebook with a social network that uses machine learning to bring people together („matching“) who have things in common. Machine learning models sort personalized advertising according to the highest probability of success, thus establishing innovative business models such as Google’s search engine and its RankBrain algorithm. Spotify’s music streaming business model, Netflix’s video streaming and the short video platform of the Chinese Bytedance subsidiary TikTok are also based on the AI-based principle of personal recommendations.

This means that many people have been in daily contact with AI applications since the turn of the millennium without realizing it. Europe is currently facing the challenge of making better use of the new opportunities offered by AI than in the past.

Conclusion

In view of the extreme competitive dynamics in artificial intelligence, Europe must catch up and reduce its dependency
One way to do this is to combine company-specific knowledge with innovative AI technologies such as the tokenizer-free approach
The success of today’s tech giants since the turn of the millennium is based on the creative application of
Knowledge-specific artificial intelligence could build on and continue the success story of the European hidden champions.

Literature

[1] Sommer U., USA dominates like never before. In: Handelsblatt, December 27/28/29, 2024, p.1, 6-8

[2] Gusbeth, S. et al, Sputnik moment. In: Handelsblatt, January 31, February 1-2, 2025, pp. 50-55

[3] Holzki, L., Up to 400 percent more efficient. In: Handelsblatt, January 22, 2025, p. 23

[4] Holzki, L., 54 million for a European AI. In: Handelsblatt, February 4, 2025, p. 18-19

[5] Bomke, L., Knees, L., Wo Europa Chnacen im KI-Rennen hat. In: Handelsblatt, February 10, 2025, p. 20-21

[6] zu Fürstenberg, J., „We need much more capital that also takes risks“ (Interview), In: Handelsblatt, January 31, February 1-2, 2025, pp. 32-33

[7] Drucker, P.F., The Age of Disconinuity – Guidelines to our Changing Society Butterworth-Heinemann 1969

[8] Argyris, L., Schön, D.A., Organizational Learning – A Theory of Action Perspective, Addison Wesley 1978

[9] Polanyi, M., Implicit Knowledge, Suhrkamp 1985

[10] Nonaka, I., Takeuchi, H., The Knowledge-Creating Company – How Japanese Companies Create the Dynamics of Innovation, Oxford University Press 1995

[11] Palass, B., Servatius, H.G., WissensWert – Mit Knowledge Management erfolgreich im E-Business, Schäffer-Poeschel 2001

[12] Meckel, M., Steinacker, L., Alles überall auf einmal – Wie Künstliche Intelligenz unsere Welt verändert und was wir dabei gewinnen können, Rowohlt 2024

Management Education 5.0 for Dialog-based Action

13.01.24

For some time now, there have been various terms with the version number 5.0, such as Society 5.0, Industry 5.0, Education 5.0 and Strategy 5.0. We are concerned with the question of what contemporary training and executive education for the fifth development stage of connective strategic management could look like (Management Education for Strategy 5.0). An important basis for this is the improvement of dialog-based action.

In this blog post, I outline a new way of teaching relevant learning content for today’s working world.

Society 5.0, Industry 5.0 and Education 5.0

The concept of a networked Society 5.0, which originated in Japan, and the term Industry 5.0, coined by the European Union, describe a fifth stage in the development of society and industrial production. Industry 5.0 builds on the Industry 4.0 approach developed in Germany in 2011. The version number 5.0 emphasizes the increasing importance of human-machine interaction and a more resilient industry.

The focus of the still relatively new concept of Education 5.0 is the individual design of interdisciplinary learning processes. This fifth stage of development is characterized by the following features: ¹

Personalized learning
cooperation and solidarity
development of skills relevant for the 21st century
flexibility and accessibility
data-based decision-making
security and protection of privacy
high-speed networks
well-being
adaptability and
game-based learning (gamification).

In recent years, my research has focused on the fundamentals and characteristics of the fifth development stage of connective strategic management.² Such a Strategy 5.0 requires new approaches to management education and training. In this respect, there is a connection between Management Education for Strategy 5.0 and the concepts of Society 5.0, Industry 5.0 and Education 5.0.

We distinguish between three levels when it comes to improved training and executive education for today’s working world.

Three-level model for improved training and executive educaton

Traditional training and executive education for the world of work is organized according to specific subject areas. These include natural sciences, engineering and health sciences, computer science,business administration, economics, law, political science and psychology. These subject areas have been joined by new cross-cutting topics such as digitalization, sustainability and resilience, which are becoming increasingly important.³

Unfortunately, secondary schools have failed to teach young people the basics of these subjects. The result is unused apprenticeship years, e.g. for acquiring programming skills, and a certain lack of orientation when choosing a career. The acquisition of knowledge and skills for the working environment then only takes place in specialized Bachelor’s degree courses. Surprisingly, little has changed in this basic pattern over the last fifty years. At the beginning of the 1970s, for example, I studied to become an „Engineer 1.0“, in which the words „person“ and „customer“ did not appear.

Early professional specialization favours the emergence of a silo mentality within and between organizations, which makes change processes more difficult. In practice, the mantra-like calls for transformation often fail due to this compartmentalization and a lack of dialogue skills.

Our three-level model of improved training and executive education for the woring environment addresses these deficits. The three levels are:

Supplementing traditional Bachelor’s degree programs with an understanding of common foundations such as entrepreneurship
an expansion of professional specialization to include relevant cross-cutting topics such as sustainability and
imparting knowledge and skills for dialog-based action that helps managers to master the major challenges of the present and future.

The aim of this three-level model is to create a new content framework for Management Education 5.0.

At the first level of the common foundations, the main aim is to train interdisciplinary cooperation. The topic of entrepreneurship, for example, is well suited to this. Learners take on different roles when founding a start-up and thus improve their professional orientation by applying previously tested personal strengths.

A connecting element at the second level of specialization are cross-cutting topics such as sustainability. For example, in a case study on the topic of Digital GreenTech, students apply their skills in the classic disciplines and at the same time improve their teamwork skills.

The third level is about developing a more dialog-based approach. Management Education 5.0 should possibly focus on this area. A current example of this is connecting strategies for generative artificial intelligence (AI). An opportunity for Europe lies in new forms of cooperation between the business, science, politics and society sectors in order to find the right balance between innovation and containment. This involves expanding the relevant capabilities for intersectoral programs.⁴

Connective strategic management

Over the past year, we have tested this three-level model in university teaching and executive education on the evolution of strategic management. In the courses, participants analyze the positive contributions and weaknesses of the five development stages of strategic management.⁵ The illustration shows our current version of the fundamentals and characteristics of a Strategy 5.0.

A new addition is the fundamental topic of dialog-based action, which I would like to discuss in more detail below.

Basic and supplementary dialog skills

The roots of the concept of dialog (dia-logos: flow of meaning) in the history of ideas go back to ancient times. Important impulses for modern dialog theory come from David Bohm, Ruth Cohn, Verena Kast, Brian Goodwin and William Isaacs. The view of the American physicist David Bohm is based on the idea that the participants in dialog processes generate a topic and that something new can emerge as a result.

He sees the exploration of new possibilities as the goal of dialog. This is the difference to debate or discussion, where the focus is on defending one’s own position. Dialogues are intended to counteract the fragmentation of reality through rational-analytical thinking and make deeper connections clear. Dialogue skills can be divided into the four basic skills (1-4) and six supplementary skills (A-F) shown in the diagram. Basic dialog skills are:⁶

Radical respect
suspending assumptions and assessments
speaking that comes from the heart and
generative listening.

I have assigned the supplementary dialog skills to these basic skills.⁷

Radical respect means recognizing the opinions of others as legitimate and of equal value. This is supported by a learning attitude, i.e. an inner attitude that is characterized by interest and curiosity in others. What is important here is openness towards the person in question and their possibly contrary positions.

The second fundamental skill of suspending assumptions and assesments means keeping one’s own mental models in abeyance. This leads to a slowing down of the dialog process, which makes it possible to bring the spirit of „thinking together“ to life.

The philosopher of religion Martin Buber understands „speaking from the heart“ to mean speaking when there is something to say and saying what needs to be said. The important thing here is to observe one’s own observations, i.e. a kind of self-perception on a meta-level. „Productive pleading“ means explaining one’s own thought process and not just presenting a thought result.

A fourth fundamental dialog competence is generative listening. This refers to recognizing your own contradictions and evasive manoeuvres. It helps to explore other positions by asking sincere, interested questions.

In my opinion, the stakeholder dialogues widely used in management and political practice have not really succeeded in translating these skills into concrete action in practice. The question therefore arises as to what improved dialog-based action could look like.

Dialogue-based action

In the fifth stage of development of connective strategic management, competition is increasingly taking place between innovative stakeholder ecosystems. Examples of the challenges to be overcome simultaneously include digital realignment in established companies, the fight against climate change and improved resilience in the event of polycrises. The winners in this new stage of development will be the stakeholder ecosystems that are best able to engage in dialog-based action.

Barriers between the expression of two behavioral dimensions must be overcome. These behavioral dimensions are:

Communication-oriented dialogs and
the translation into practical action.

In traditional stakeholder dialogs, there are often deficits in practical action.⁸

When communication-oriented dialog is not very pronounced, it is referred to as a discussion or debate. Management Education 5.0 should therefore train dialog skills.

The even greater difficulty often lies in translating this into practical action. In companies, the traditional behavioral pattern of power- and competition-oriented action, which is not very communication-oriented, usually dominates. This type of behavior should be replaced by dialog-based action.

Management Education 5.0 is therefore aimed at conveying the corresponding mindset. Innovative educational programs have the opportunity to test and further develop this basic concept in practice. The existing deficits in overcoming major challenges show that there is a need for dialog-based action in intersectoral programs, for example.

Testing new forms of learning

One example of the testing of new forms of learning is the development and expansion of the TUM campus in his home town of Heilbronn, initiated by Lidl owner Dieter Schwarz together with the Technical University of Munich (TUM). The Innovation Park Artificial Intelligence (Ipai), the largest AI ecosystem in Europe, is currently being created here.⁹ Alongside industrial companies such as Bosch and SAP and venture capital investors, the Schwarz Group is one of the partners of the AI start-up Aleph Alpha, founded by Jonas Andrulis. The aim is to realize a sovereign generative AI from Europe. The integration into the campus and the innovation park creates a bridge between science and business. Aleph Alpha makes its technology available to companies and administrations as a license, which then use it to implement specific applications.¹⁰ Future developments will show how successful this innovation ecosystem is in comparison to the major digital champions from the USA.

Such concepts are important steps on the way to Management Education 5.0 and it is to be hoped that other education providers will follow soon. The aim is to create a unifying learning experience that supports positive differentiation in the competition between stakeholder ecosystems.

Unfortunately, according to a survey of around 11,000 HR managers from 21 countries by the market research institute Trendence and the HR consultancy Emerging, none of the German universities are among those that best prepare their graduates for the job market.¹¹ The top places were taken by three universities from the USA: the California Institute of Technology, the Massachusetts Institute of Technology and Stanford University. Seven universities from Germany made it into the top 100 in the world. The leaders in this national ranking are the Technical University of Munich, the Humboldt University of Berlin and the Ludwig Maximilian University of Munich in 13th, 46th and 53rd place respectively. On the way to internationally successful management education, our country should therefore start a catch-up process as quickly as possible.

Dialogue-based action as the basis for the design of innovative stakeholder ecosystems

The Association of German Engineers (VDI) is an important driving force for dialog between the business, science, politics and society sectors. As a member of the VDI’s Technology in Dialogue Advisory Board, I am involved in dialog-based action as the basis for innovative stakeholder ecosystems. The VDI round table on the circular economy for plastics emerged from the work of the advisory board. One of the recommendations made by this group of experts is the establishment of a stakeholder platform for cross-circular cooperation and organizational processes.¹² This platform should not only formulate goals, but also implement coordinated measures and forms of cooperation between relevant stakeholders. As part of the Advisory Board’s further work, we aim to test platforms for intersectoral programs and analyse their success factors.

This model could serve as a blueprint for tackling other major challenges, such as the emergence of a hydrogen economy or the development of trustworthy generative AI. A representative survey by the VDI has shown that only 54 percent of the more than 1000 participants still consider our country to be competitive.¹³ VDI President Lutz Eckstein therefore invites us to formulate a positive target image for the future of our location in dialog with society.¹⁴ An important means of achieving this goal is competence in dialog-based action within the framework of joint, intersectoral programs of the relevant stakeholders. Surprisingly, there is still a considerable need for research with regard to successful cooperation between the economic, scientific, political and social sectors.

The quadruple helix approach, which describes the co-evolution of different sectors, provides a theoretical basis.¹⁵ The question of how Europe intends to implement this co-evolution in organizational terms remains largely unanswered.

Conclusion

A fifth stage of development is also emerging in management education, in which interdisciplinarity and connecting skills are becoming increasingly important
A model for improved training and executive education in the world of work emphasizes knowledge and skills for dialog-based action
A current example is generative AI with its opportunities and risks
A dialog-based approach combines dialog skills with a less power- and competition-oriented approach
The winners of the fifth development stage of connective strategic management will include stakeholder ecosystems that are better at dialog-based action than their competitors

Literature

[1] Ahmad, S. et al: Education 5.0 – Requirements, Enabling Technologies, and Future Directions, July 29, 2023

[2] Servatius, H.G.: Strategy 5.0 for mastering the new challenges.
In: Competivation Blog, 28.06.2022

[3] Servatius, H.G.: Generative AI and mass customized action learning.
In: Competivation Blog, 28.08.2023

[4] Suleyman, M.: The Coming Wave – AI, Power and the 21^st Century’s Greatest Dilemma, London 2023

[5] Servatius, H.G.: Leading strategically with contextual and relationship-oriented intelligence. In: Competivation Blog, 14.03.2023

[6] Isaacs, W.: Dialogue and the Art of Thinking Together – A Pioneering Approach to Communication in Business and in Life, New York 1999

[7] Hartkemeyer, M., J.F. and T.: Dialogische Intelligenz – Aus dem Käfig des Gedachten in den Kosmos gemeinsamer Denkens, 4th ed., Frankfurt 2022

[8] Künkel, P., Gerlach, S., Frieg, V.: Stakeholder-Dialoge erfolgreich gestalten – Kernkompetenzen für erfolgreiche Verhaltens- und Kooperationsprozesse, Wiesbaden 2016

[9] Hopp, D.: Dieter Schwarz – An exceptional phenomenon. In: Handelsblatt, December 15/16/17, 2023, p. 54-55

[10] Wohlfahrt, M.: Jonas Andrulis – Intelligent mission. In: Handelsblatt, December 15/16/17, 2023, pp. 56-57

[11] Telser, F.: These universities prepare students best for the job market. In: Handelsblatt, December 15/16/17, 2023, p. 79

[12] VDI (ed.): Rethinking the circular economy for plastics, November 2022

[13] VDI / VDE Technik+Innovation (ed.): How does Germany think about innovation and value creation? Düsseldorf / Berlin, May 2023

[14] Eckstein, L.: Where does Germany want to be in 2050? In: VDI News, December 15, 2023, p. 8-9

[15] Carayannis, E.G., Campbell, D.F.J.: Mode 3 Knowledge Production in Quadruple Helix Innovation Systems, New York 2012

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