Artificial intelligence (AI) is currently developing into a powerful tool for strategic management that accelerates, strengthens and changes learning processes. This applies to the corporate level as well as to the level of functional areas and business processes. Pioneering companies are using knowledge-specific AI in the various phases of strategic processes and achieving competitive advantages with innovative, AI-based business models. Generative AI has the character of a wake-up call.

In our series of blog posts on artificial intelligence, this article deals with the role of AI in strategic management. In it, I explain the increasing importance of AI in strategy processes.

Generative AI as a wake-up call

The use of artificial intelligence in strategic management is not new. Since the turn of the millennium, US digital companies such as Amazon have been using AI-based personalization as part of their innovative business models.¹ Surprisingly, many users of these business models are not aware of the contribution of AI.

In our book The Internet of Things and Artificial Intelligence as Game Changers, published in 2020, we described the strategy process for new IoT- and AI-based business models² and discussed relevant business model patterns.³ At that time, however, interest in the topic was still limited in Germany.

The real wake-up call that shook the general public awake came in November 2022, when OpenAI released its ChatGPT dialog program. This action triggered a hype around generative AI and large language models, which was followed by a certain disillusionment.⁴

Many companies are now asking themselves what role artificial intelligence can play in their strategy processes.

AI-supported strategy processes at corporate level

A study by the Massachusetts Institute of Technology (MIT) concludes that artificial intelligence accelerates and strengthens learning processes.⁵ Such augmented learning builds on existing learning capabilities. An important field of application are the various phases of innovative strategy processes that help companies to gain a new form of competitive advantage.

It starts with an AI audit to analyze the company’s initial strategic situation and its use of AI. This is followed by AI-supported strategic foresight, which enables faster and more efficient early detection. Knowledge-based AI is also a means of realigning business models. Another phase is the design of an AI-oriented stakeholder ecosystem. When selecting partners, it is important to find the right balance between cooperation and competition.

Innovative AI platform architectures form the basis for relevant applications, and companies generally need partners to implement them. Strategies are implemented with the help of agile, AI-supported performance management. This involves close coordination between the corporate level and the level of connected business processes.

Strategic learning loops, which take the form of rapid iterations, play a decisive role in agile strategy processes. This turns the analysis of the initial strategic situation into a dynamic process.

AI audit to analyze the initial strategic situation

A study by the German Economic Institute (IW) concludes that AI could contribute 330 billion euros to gross value added nationwide. One in five companies already uses AI. However, most applications are rather selective, e.g. in the form of chatbots for customer inquiries. Surprisingly, 66% of companies say that AI is not relevant to their business model. 36 percent consider integration into existing systems to be difficult. 47% complain about the lack of employee expertise. NRW Minister President Hendrik Wüst nevertheless believes that AI could be the driving force behind an economic upturn.⁶

To achieve this goal, companies should carry out an AI audit and use a SWOT analysis, for example, to gain an overview of their initial strategic situation.⁷ Interestingly, results of such an analysis of strengths, weaknesses, opportunities and threats are similar. One strength of companies is that they have a lot of specific knowledge that has the potential to be enhanced by AI. This is often offset by weaknesses in the systematic anchoring of AI in strategies and processes. The potential of AI lies both in increasing productivity and in innovation benefits through new products, services and business models. On the other hand, there are many threats from competitors, foreign stakeholder ecosystems and misuse of the power inherent in artificial intelligence.⁸

On this basis, the next step is to prepare even better for future developments with the help of AI-supported strategic foresight.

AI-supported strategic foresight

The term strategic foresight, coined in the 1980s, has a long history, during which methods such as scenario analysis, which are still widely used today, were developed. The Gamechanger Radar developed by us makes it possible to prepare for far-reaching changes.⁹ With AI-supported strategic foresight, pioneering companies are now writing a new chapter in foresight. This chapter assumes a change in the way people search for information on the internet.

For example, Google has developed the new search function „Overview with AI“, which provides summarized texts on topics. An example is shown in the following illustration. The topic I entered is: „Applying Complexity Theory in Management“. The answer that Google provides is surprisingly good. It describes the paradigm shift in strategic management that has taken place in recent decades more comprehensively and better than many individual publications on this topic.

Foresight users will learn to improve their prompting capabilities relatively quickly. In addition, AI-supported foresight platforms are currently emerging that simplify and accelerate the early recognition of new trends, which usually take the form of weak signals.

Of course, this development also poses a threat to Google’s traditional search engine business, which is linked to advertising. The start-up Perplexity, for example, is trying to take users away from Google with its user-friendly „answer engine“. It remains to be seen what effect this will have on the market leader’s profit driver¹⁰

Reasoning AI enables advantages for complex tasks such as strategic foresight. It is now offered by some AI developers. In reasoning, the AI breaks down possible queries into sub-problems and processes them step by step. Such slower thinking costs more computing power and electricity. Developers call the „reasoning“ of AI a chain of thought (CoT). Reasoning models achieve this through an additional training step that uses reinforcement learning to train detailed reasoning. Similar to an experienced employee, reasoning models analyze complex information step by step. To do this, they need a single precise prompt and a lot of context. However, the application of reasoning AI in strategic foresight is still at the experimental stage.¹¹

AI-based realignment of business models

Innovative business models for AI-based robotics are currently emerging. This represents an opportunity for Europe. Stanford professor and great „godmother of AI“ Fei-Fei Li has founded the start-up World Labs, which develops AI models for the spatial intelligence of robots that support machines. Google subsidiary DeepMind and digital giant Nvidia are also working on partner networks for AI-based human-like robots. Many of the partners come from Europe. In addition to well-known robotics companies, start-ups such as Anybotics (Switzerland) and Agile Robots, Neura Robotics and Quantum Systems from Germany are emerging here, although they do not have as much funding as their competitors from the USA (e.g. Figure AI and Covariant). For Europe, it is important to seize the opportunities arising from the combination of in-depth industry-specific knowledge and innovative AI models as quickly as possible.¹²

Two dimensions are relevant for an AI-based realignment of business models. These dimensions are productivity orientation and innovation orientation. Most companies start with an AI-based increase in productivity and use AI in routine processes to reduce personnel costs. In addition, many fields of application for AI-based innovations have now emerged. When both dimensions come together, we speak of AI-based ambidexterity. The term ambidexterity originally refers to the ability to use both hands in sport. Applied to management, ambidextrous leadership describes leadership that strikes a good balance between innovation and productivity.¹³

The specific applications of these two dimensions in industries and companies result in a wide variety of AI-based ambidexterity. The new business models are embedded in AI-oriented stakeholder ecosystems.

AI-oriented stakeholder ecosystems

German and European policymakers are planning to boost the performance of their AI ecosystem. In view of the changing geopolitical situation, the coalition agreement of the new German government provides for a strengthening of digital sovereignty. The digital policy of the European Union (EU) aims in the same direction. Five gigantic data centers are planned in order to catch up in the field of artificial intelligence. The Jülich and Stuttgart sites are candidates for such a gigafactory in Germany. When it comes to AI regulation, the EU wants to focus more on competitiveness and reduce bureaucracy. An EU action plan has been drafted to this end. It remains to be seen whether these measures will be enough to reduce dependence on the large cloud providers (hyperscalers) from the USA.¹⁴

There are also two dimensions to consider when designing a company’s AI-oriented stakeholder ecosystem.¹⁵ One dimension is the dependence on powerful AI providers. In order to reduce this dependency, the second dimension for companies is improving their own skills in the development and application of artificial intelligence. In the hype phase of basic AI models, dependence on US providers has increased. The opportunity for Europe now lies primarily in knowledge-specific AI models for various applications. Hybrid AI ecosystems are emerging by connecting these two dimensions. Such connectivity requires specific skills.

In view of the geopolitical uncertainties, companies are faced with the difficult task of finding the right partners when designing their AI ecosystem. The transitions between cooperation and competition are fluid. The term coopetition describes such a situation.¹⁶ However, the theoretical basis for a combination of cooperation and competition is still lacking in AI ecosystems. An important field of application is the selection and in-house development of innovative AI platform architectures.

Innovative AI platform architectures

The chip manufacturer AMD and the Finnish start-up Silo AI, which belongs to AMD, are working together with the companies of the Swedish Wallenberg Group. The Nvidia competitor AMD has announced a partnership with 38 companies. These include AstraZeneca, Scania, Saab, Ericsson and IKEA. The collaboration is coordinated by the Wallenberg innovation network Combient. The aim is to scale company-specific AI models. While OpenAI trains its AI models on Nvidia chips, Silo AI uses chips from AMD. The role of Silo AI is to accelerate the deployment of AI models at companies that use AMD platforms. The infrastructure on which the work has begun plays an important role here, as a move is time-consuming. Silo AI uses multimodal AI agents, i.e. models that process images and audio files as well as speech.¹⁷

Established digital companies have been practising an organizational form with an IT platform at its center for some time now.¹⁸ With the increasing importance of artificial intelligence, this concept is becoming more and more relevant for established companies. Innovative AI platform architectures combine both the strategic and operational levels as well as centralized and decentralized organizational units. This enables all business processes and projects to have access to a common database. Due to their connecting role, AI platforms not only become a strategic building block, but also an important organizational design element. One question that is not easy to answer is how large the share of partners and the company’s own share should be in such an AI platform.

Innovative platform architectures also provide the infrastructure for AI-supported performance management.

AI-supported performance management

To answer the question of how artificial intelligence can improve performance management, it helps to take a look at the history of performance measurement. The Management by Objectives (MbO) developed by Peter Drucker and the goal-setting theory developed by organizational psychologist Edwin Locke provide important conceptual foundations. Back in the 1980s, Intel developed the agile Objectives and Key Results (OKR) method, which the venture capitalist Kleiner Perkins used at Google, for example.¹⁹ In Germany, the Balanced Scorecard method, which emerged from a best practice study by Robert Kaplan and David Norton, is much better known.²⁰ An AI-supported performance management system designed by Kleiner Perkins and the start-up Betterworks now aims to better connect strategy and motivation.

Although artificial intelligence is one of the top management issues for 2025, many companies neither formulate specific AI targets nor measure the results. A global BCG study, in which 1,800 managers were surveyed, found that only 24% of companies track their operational and financial AI targets. AI-supported performance management faces three challenges. These challenges are:²¹

1. Do not stall early trials
2. define appropriate key results for the success of an individual measure and, in addition
3. capture the longer-term effects resulting from the interaction of various measures.

The agile OKR method provides a conceptual basis for this, but requires adaptation. OKR pioneer Kleiner Perkins is one of the investors in performance management software provider Betterworks. The vision of the Palo Alto-based company, which was founded in 2013, is to further develop traditional performance management. AI plays an important role here as a co-pilot. Managers can thus invest time saved on routine tasks in better harmonization of strategic and operational projects. Important use cases are:²²

• Alignment of ambitious corporate goals and personal goals
• data-based, motivating feedback and
• the support of communication and learning processes.

The intended benefit, which contributes to the overall success, is

• a reduction in bias, more fairness and objectivity
• increased productivity and
• better personal relationships.

This brings performance management one step closer to the motivational concept already pursued by goal-setting theory.

With the increasing importance of artificial intelligence in strategic management, geopolitical expertise in working with stakeholders is becoming ever more important alongside practical skills in using AI as a tool. One basis for this is a strong future narrative.

A strong future narrative as a basis

In our 2020 book on the gamechanging potential of artificial intelligence, we took a critical look at European and German digital policy.²³ The new German government now faces the task of developing a strong future narrative that connects various policy areas.²⁴ One approach to such a much-needed grand narrative is the application of trustworthy AI both to increase productivity and to solve the innovation and environmental problems of organizations. At the heart of this is the new form of ambidexterity outlined earlier.

Traditional ambidexterity strives for a balance between tapping innovation potential (exploration) and utilization of productivity (exploitation). With the help of AI, which should be trustworthy, it is now possible to simultaneously

• reduce labor costs by increasing productivity, counter the shortage of skilled workers²⁵ and
• to make greater use of qualified personnel for the digital and ecological realignment of organizations²⁶

In view of the changed geopolitical situation, there is a window of opportunity for AI made in Europe, which the „old continent“ should use to strive for global market leadership in the necessary sustainability innovations.²⁷ Due to the large number of crises to be overcome, this initially requires resilience-oriented strategic management.²⁸

Conclusion

• Strategy processes become more efficient through the use of artificial intelligence
• Knowledge-specific AI supports strategic foresight, the realignment of business models, the design of stakeholder ecosystems, innovative platform architectures and performance management
• Pioneering companies are working on AI-based ambidextry
• In view of the geopolitical challenges, choosing the right partners is crucial.

Literature

[1] Servatius, H.G., Competitive advantages with knowledge-specific AI. In: Competivation Blog, 11.02.2025

[2] Kaufmann, T., Servatius, H.G., Das Internet der Dinge und Künstliche Intelligenz als Game Changer – Wege zu einem Management 4.0 und einer digitalen Architektur, SpringerVieweg 2020, p. 56ff.

[3] Kaufmann, Servatius, op. cit. p. 34ff.

[4] Servatius, H.G., Development of AI technologies. In: Competivation Blog, 19.02.2025

[5] Alavi, M., Westerman, G., How GenAI Will Transform Knowledge Work. In: Harvard Business Review, November 7, 2023

[6] Höning, A., Kowalewski, R., Every fifth company in NRW uses AI. In: Rheinische Post, November 13, 2025, p. 1

[7] Servatius, H.G., Auditing the innovation system of a company. In: Competivation Blog, 19.03.2015

[8] Suleyman, M., Bhaskar, M., The Coming Wave – Technology, Power and the Twenty-First Century’s Greatest Dilemma, Crown 2013

[9] Servatius, H.G., Strategic foresight with a game changer radar. In: Competivation Blog, 27.01.2021

[10] Alvares de Souza Soares, P., Geldmaschine Google – Wie lange noch? In: Handelsblatt, April 25/26/27, 2025, p. 26-27

[11] Knees, L., Why users pay more for slow AI. In: Handelsblatt, March 31, 2025, pp. 24-25

[12] Holtermann, F., Schimroszik, N., The robots are coming! In: Handelsblatt, January 3/4/5, 2025, pp. 44-48

[13] O’Reilley, C., Tushman, M., Lead and Disrupt – How to Solve the Innovator’s Dilemma, Stanford Business Books 2016

[14] Bomke, L., et al, Europe wants to build its own AI factories. In: Handelsblatt, April 9, 2025, p. 6-7

[15] Servatius, H.G., Designing innovative stakeholder ecosystems. In: Competivation Blog, 10.01.2023

[16] Brandenburger, A.M., Nalebuff, B.J., Co-Opetition – A Revolutionary Mindset That Combines Competition and Co-Operation, Bantam 1996

[17] Holzki, L., AMD enters into partnership with the industry. In: Handelsblatt, January 30, 2025, p. 24

[18] Servatius, H.G., The resource platform with agile teams as a new organizational form. In: Competivation Blog, 12.01.2021

[19] Doerr, J., Measure What Matters – How Google, Bono and the Gates Foundation Rock the World with OKRs, Portfolio/Penguin 2018

[20] Kaplan, R.S., Norton, D.P., Balanced Scorecard – Translating Strategy into Action, Harvard Business School Press 1996

[21] Bomke, L., Höppner, A., Only a few companies measure their AI initiatives. In: Handelsblatt, January 16, 2025, p. 21

[22] Gouldsberry, M., The Pivotal Role of AI in Performance Management, January 11, 2025

[23] Kaufmann, Servatius, op. cit. p. 203ff.

[24] Servatius, H.G., On the way to a new economic policy narrative. In: Competivation Blog, 16.05.2022

[25] Servatius, H.G., Process-oriented AI to increase productivity. In: Competivation Blog, 12.03.2025

[26] Servatius, H.G., AI and the future of management education. In: Competivation Blog, 09.04.2025

[27] Servatius, H.G., Sustainability-oriented strategic management. In: Competivation Blog, 15.08.2024

[28] Servatius, H.G., Resilience-oriented strategic management. In: Competivation Blog, 15.03.2024