The fifth stage of connective strategic management (Strategy 5.0) continues to evolve. Currently, the focus is on the connectivity between human and artificial intelligence. To better understand this connective intelligence, it helps to examine the forms of connectivity in strategic management more closely. This yields important implications for teaching and research regarding the changing workplace in the AI era.
In this new blog post, I summarize various forms of connectivity in the fifth stage of strategic management and draw conclusions regarding connective intelligence.
Digital Industrial Engineering as an Opportunity
A central theme of this year’s Hannover Messe was artificial intelligence (AI). Both established companies and startups presented innovative solutions. For example, the Limburg-based software manufacturer German Edge Cloud (GEC) presented the AI agent Digital Industrial Engineer, which serves as a sparring partner for engineers and thus makes the interaction between humans and machines more efficient. Many human capabilities are based on experience. This tacit knowledge must be combined with AI. GEC sees this as an important opportunity for differentiation. The agent utilizes this undocumented knowledge for learning processes. It is the task of managers to show appreciation for the human knowledge carriers.1
An important concept in mechanical engineering is Physical AI. This refers to the integration of hardware and artificial intelligence. One aspect of this is the ability of machines not only to process data but also to perceive their environment. At the Hannover Messe, Siemens presented its AI agent “Eigen,” which is designed to make engineers’ work 50 percent more efficient. This is achieved through a combination of automated processes and human oversight. In doing so, Siemens draws on its comprehensive and deep domain knowledge, which providers of basic AI models generally do not possess.2
Connective Intelligence Instead of Robomobbing
Artificial intelligence (AI) can trigger a modern “Luddite movement” among employees who fear losing their jobs. The term “robomobbing” is currently gaining traction to describe acts of sabotage against robots. Analyses by the industry association Bitkom show that 23 percent of respondents perceive AI as a threat. Resistance to AI is understandable if companies fail to communicate and act credibly.3
A crucial factor here is equipping employees to work with AI. So far, only a rough outline of how human and artificial intelligence will interact in the future is visible. In this context of connective intelligence, not only is AI evolving at a rapid pace, but the human skills in demand are also changing. This raises the question of what lessons can be drawn for the future of work from various forms of connectivity.
Lessons from Forms of Connectivity in Strategic Management
In response to new challenges facing companies, strategic management has gone through various stages over the past decades, which continue to evolve dynamically.4 These stages focus on the following areas:
- Market and financial orientation (Strategy 1.0)
- technology and innovation orientation (Strategy 2.0)
- sustainability orientation (Strategy 3.0) and
- resilience orientation, including necessary restructuring (Strategy 4.0).
In the current fifth stage of connective strategic management, the difficulty lies in the complexity of the challenges. Many companies must navigate strategic and organizational realignments while becoming more resilient, digital, and sustainable.5 The lesson for an emerging connective intelligence is that this requires an integrated perspective. Companies that possess such a perspective have significant advantages over others.
An effective corporate innovation system consists of various interconnected areas of activity. These areas—such as research, innovation marketing, and a culture that fosters innovation—require specific competencies. Successful innovation managers possess the ability to design connections.6 The lesson for connective intelligence is that this ability can be learned, yet it is neglected in our education system, which is oriented toward distinct disciplines. This leads to the recommendation that management theory and research be oriented more toward a transdisciplinary and design-oriented approach.7
In strategic management, there has been a paradigm shift from mechanistic to one that manages complexity.8 Digital champions recognized the potential of the theory of complex, evolutionary systems early on and applied it in management.9 Startups have thus become the most valuable companies in the world. The implication for connective intelligence is to learn from the positive experiences of these companies and to adapt the newer complexity theory to the AI era. This, too, requires design-oriented management research in real-world laboratories of change.
Perhaps the greatest challenge is improving cooperation among stakeholders from politics, academia, business, and society—for example, in the context of digital change. In recent decades, global competition among innovation ecosystems has intensified. These have become key value drivers for companies and regions.10 Germany has rested on the laurels of past successes for too long and must now catch up. The lesson for connective intelligence is that the relevant sectors must improve their ability to act jointly and in a dialogue-oriented manner in order to achieve competitive advantages.11 The following figure summarizes these forms of connectivity and lessons for connective intelligence.
For some time now, research has been exploring what approaches to connective intelligence might look like. The term, coined by Derrick de Kerckhove, initially focused on interaction with the Internet.12
In projects on connective strategic management, we have found that people with contextual and relationship-oriented intelligence are key. These skills can be developed.13 They also play an important role when working with AI. Both upskilling and deskilling are possible in this context.
Upskilling and Deskilling Through AI
In a work environment accelerated by AI, there is an expansion of employees’ skills (upskilling) but also a reduction (deskilling). Therefore, it is crucial to promote upskilling and limit deskilling.
The aspects of upskilling mentioned in many case studies are:
- Time savings from routine tasks taken over by AI
- Using AI as a tool to improve one’s own skills
- stimulation of human creativity through content specifically generated by AI, as well as
- a stronger focus on relational intelligence and empathy, which are used in a complementary manner to AI.
Potential deskilling is not addressed as extensively. Causes for this may include:
- A superficial understanding of a topic driven by convenience (offloading)
- Loss of skills in tasks taken over by AI, e.g., reading, understanding, critical evaluation, and writing
- a decline in self-generated new knowledge coupled with a loss of problem-solving skills, as well as
- neglected oversight of the results produced by AI.
Research on connective intelligence—which seeks to answer the question of what the best connections between human and artificial intelligence are depending on the situation—is still in a relatively early phase.
Approaches to Connective Intelligence
Ethan Mollick, an innovation researcher teaching at the Wharton School in Philadelphia, offers several recommendations for connecting human and artificial intelligence.
- Try to incorporate AI. In doing so, it is important to recognize what AI is good at and what it is not. This ability grows with experience
- Stay in the control loop as a human. Human expertise and judgment are essential for correcting the incorrect results generated by AI
- Treat the AI as a clearly defined persona representing a specific type. In this context, the AI is like a fast-working intern who wants to please and tends to twist the truth
- Assume that AI is evolving dynamically. Newer AI technologies, such as agent systems, have specific strengths but also pose threats
- View AI as a connection engine. In this way, AI is capable of developing new ideas from the combination of existing knowledge
- Education and training should view working with AI as a new professional competency. In this context, good prompting is just one of the skills within the framework of evolving connective intelligence.14
These approaches to enhanced intelligence give rise to specific performance patterns in the AI era.
Performance Patterns in the AI Era
In recent decades, a wealth of leadership theories has emerged, the practical relevance of which is highly dependent on time and context. Of particular importance for human-resource management within the framework of connective strategic management is the concept of connective leadership. Its recommendation for highly competitive and power-oriented managers is to expand their own leadership styles and behavioral preferences.15 However, the topic of artificial intelligence does not feature in this concept.
The development of AI technologies has proceeded in waves. There have been repeated phases of disillusionment, such as currently with generative AI using large language models.16 In most cases, the answer to the question of which next wave of AI (NextAI) will be successful is marked by great uncertainty.
In the AI era, it is therefore crucial to link the dynamically evolving performance in artificial intelligence with the ability to design connections. A conceptual foundation for this is the design of trustworthy high-performance systems.17 Depending on the characteristics of the two dimensions mentioned, the following performance patterns emerge:
- Losers in the AI era
- holistic non-technicians
- AI specialists and
- winners with connective intelligence.
It can be assumed that both holistic non-technicians and AI specialists should further develop their mindset.
The disadvantage of holistic non-technicians is that, while they possess strong skills in integrative design, they are unable to leverage the new opportunities offered by AI or work with AI technologies that are not very powerful. AI specialists lack the integrated perspective required to understand the complex interrelationships of an AI application and its potential consequences. Therefore, in the AI era, leaders must train their connective intelligence. In our research and teaching accompanying projects, we examine the success factors of these winning types. The looming disruption of management education is serious.18
The Significance of Strategy 5.0 for Management Education
I asked AI what the significance of Strategy 5.0 is for management education. The answer summarizes some of my recent publications and highlights the following points:
- Expansion of the competency profile of executives and employees
- Integration of new technologies with AI as a partner
- Change of teaching methods and research approaches, as well as
- Focus on the intersection of digitalization, sustainability, and resilience.
I find this result surprisingly good because it not only identifies important aspects but also demonstrates a certain creativity.
You can continue the question-and-answer game for as long as you like, receiving a wealth of in-depth information and examples from the AI. Of course, you can also “activate” your own intelligence and explore the implications for existing management education.
In my view, many bachelor’s and master’s programs in business administration (BA) have the following weaknesses:
- Business administration is understood as an unconnected collection of functional business disciplines (e.g., marketing) and interdisciplinary fields (e.g., innovation)
- Entrepreneurship and venture capital are not required courses
- The curriculum neglects the fundamentals and applications of artificial intelligence in practical exercises
- Strategy courses—if offered at all—are typically introduced relatively late and address the initial stage of market- and finance-oriented strategic management (Strategy 1.0) in a more or less uncritical manner
- Universities do not, at least not in depth, teach the fundamentals of the theory of complex, evolutionary systems and their relevance, e.g., for agile project management
- When research approaches are taught, empiricism dominates, while design-oriented research is “uncharted territory” for many business administration professors.
One possible way to reorient the “Introduction to Business Administration” course would be to place connective strategic management at the beginning of a degree program and, using practical examples, provide an overall picture of the challenges. Since all students now work with AI in one form or another, they can contribute their own important experiences to practical exercises on the topic of connective intelligence. We have been pursuing this approach for several years in bachelor’s and master’s programs, in executive education, and in the supervision of theses.
Given the speed at which the world of work is changing due to AI, it is not surprising that the topics of Strategy 5.0 and connective intelligence are also evolving rapidly. This opens up new opportunities for the German education system. However, education providers who cling to outdated models run the risk of being left behind. The same fate threatens organizations that neglect continuing education.
Challenges in Continuing Education and Leadership Development
Figures from the McKinsey HR Monitor show that German companies have cut their budgets for employee continuing education by 30 percent over the past two years. This places Germany at the bottom of the list in a European comparison. In the AI era, attempts to cut costs in the short term undermine competitiveness in the medium term, as employees need to acquire new skills. Furthermore, many companies face the challenge of choosing the right approach given a complex continuing education system. As a result, learning processes—for example, on AI topics—often take place not in traditional seminars but informally within the context of projects. This underscores the importance of customized training tailored to the specific situation, which, for instance, sharpens critical judgment in the application of AI while using AI as a tool.19
The growing importance of artificial intelligence is also changing the demands placed on executives. Above all, companies are looking for individuals who have experience with concrete AI projects and can demonstrate that they have successfully navigated the changes associated with these new technologies. Since the AI-based realignment of a company is a complex yet highly specific task that is also constantly evolving, it is essential to continually adapt and develop the skills acquired to the specific situation at hand. This requires a growth mindset with a passion for lifelong learning. Such a mindset supports both the use of AI as a tool for strategic management20 and the improvement of process-oriented AI to increase productivity.21
In leading companies, new AI-based roles and job profiles have emerged that open up excellent career opportunities. These include the AI Realignment Officer, the AI Solutions Architect, and the Forward Deployment Engineer. An AI Realignment Officer designs the AI-based strategic, operational, organizational, and cultural realignment of organizations. Job postings still use the outdated term “Transformation Officer,” even though the focus is not on a one-time transformation but on numerous, rapid adaptations to or anticipations of new developments. The AI Solution Architect also plays a key role. As an AI architect, their task is to determine which AI applications and processes take priority based on an AI strategy, what the required IT architecture looks like, and how to successfully integrate the relevant data. This requires many Forward Deployment Engineers who understand the complex problems of internal and external customers and develop tailor-made solutions “from the ground up” through iterative processes. A shared core competency of these “bridge-building roles” is the ability to design solutions that connect.
Conclusion
- AI agents learn from the experiential knowledge of engineers. This presents an opportunity for European industry if it succeeds in connecting human and artificial intelligence
- Lessons in this regard emerge from the various forms of connectivity within the framework of the fifth stage of development of a connecting strategic management
- Research that combines human and artificial intelligence is still in its infancy
- This has important implications for gaining a competitive edge in the AI era, where the key lies in combining the capabilities of artificial intelligence with the ability to foster cooperation
- This requires new approaches to executive education and development.
References
[1] Wittenbrink, J., “Sparring Partner for the Factory.” In: Handelsblatt, April 20, 2026, pp. 26–27
[2] Höpner, A., Siemens Introduces First AI Agent for Engineers. In: Handelsblatt, April 21, 2026, pp. 26–27
[3] Merten, M., Bomke, L., Man vs. Machine – How Bosses Help Their Employees Overcome Their Fear of AI. In: Handelsblatt, April 10, 11, and 12, 2026, pp. 54–55
[4] Servatius, H.G., Development and Transformation of Strategic Management. In: Competivation Blog, September 19, 2025
[5] Servatius, H.G., Triple Strategic Realignment. In: Competivation Blog, June 7, 2024
[6] Servatius, H.G., Designing and Empowering Innovation Systems. In: Competivation Blog, February 22, 2018
[7] Servatius, H.G., Creative Innovation Research on AI Applications. In: Competivation Blog, March 25, 2026
[8] Servatius, H.G., Learning from Successful Digital Companies. In: Competivation Blog, July 12, 2024
[9] Servatius, H.G., Learning to Design Solutions for Complex Management Problems. In: Competivation Blog, July 15, 2025
[10] Servatius, H.G., Designing Innovative Stakeholder Ecosystems. In: Competivation Blog, January 10, 2023
[11] Servatius, H.G., Management Education 5.0: Toward Dialogue-Based Action. In: Competivation Blog, January 13, 2024
[12] de Kerckhove, D., Connected Intelligence – The Arrival of the Web Society,
GB Gardners Books 1998
[13] Servatius, H.G., Strategic Leadership with Contextual and Relationship-Oriented Intelligence. In: Competivation Blog, March 14, 2023
[14] Mollick, E., Co-Intelligence – Living and Working with AI, Portfolio 2024
[15] Servatius, H.G., Human Resource Management in the Age of Connective Management. In: Competivation Blog, January 19, 2021
[16] Servatius, H.G., Development of AI Technologies. In: Competivation Blog, February 19, 2025
[17] Servatius, H.G., Designing Trustworthy High-Performance Systems. In: Competivation Blog, January 29, 2026
[18] Servatius, H.G., Disruption of Management Education for AI-Based Realignments. In: Competivation Blog, October 10, 2025
[19] Merten, M., Budgets for Continuing Education Cut by 30 Percent. In: Handelsblatt, May 13, 2026, p. 30
[20] Servatius, H.G., AI as a Tool for Strategic Management. In: Competivation Blog, May 1, 2025
[21] Servatius, H.G., Process-Oriented AI for Increased Productivity. In: Competivation Blog, March 12, 2025
[22] Obmann, C., Schimroszik, N., These new roles come with six-figure salaries. In: Handelsblatt, May 19, 2026, pp. 32–33