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:

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

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.