artificial intelligence (AI) | Competivation
Development of AI technologies

Development of AI technologies

There is a large discrepancy between the current importance of the topic of artificial intelligence and the AI expertise of most people. This widespread know-how gap ranges from students and teachers to managers and politicians. It therefore seems important to look at the development and current status of AI technologies, which were created almost 70 years ago, something that many people are unaware of.

This new blog post is the continuation of our series on competitive advantage with knowledge-based artificial intelligence. In it, I outline the roots of AI technologies and explain the hype and disillusionment with large language models.

 

Training offensive based on the AI Act

The European Union’s AI Act requires companies to provide their employees with practical know-how on how AI works and how it can be used, as well as the opportunities and limitations of the technology. This EU Regulation 2024/1689 came into force in Germany on February 2, 2025.1 Specific training modules may be necessary for individual user groups, such as IT, legal, HR and operational units, which must be tailored to the existing level of knowledge. Furthermore, it seems sensible to adapt the teaching of AI know-how to the respective situation of the company. One place to start is with knowledge of the development of artificial intelligence (AI) and its various technologies

 

From symbolic AI and neural networks to „AI winters“

Artificial intelligence has experienced a series of ups and downs in its long development history. In the computer sciences of the 1950s, two approaches emerged in the attempt to develop machines that mimic human intelligence:2

  • Symbolic AI is based on programmable rules and a systematic logic with the aim of representing knowledge and deriving conclusions. The aim is to represent a real-world problem programming symbols and their relationships.
  • Inspired by the networking of the brain, neural networks aim to simulate learning processes by using connections between artificial neurons. This method relies on data-driven machine learning to find patterns and correlations.

The Dartmouth summer research project initiated by scientists such as John McCarthy and Marvin Minsky in 1956, which focused on symbolic AI, is regarded as the birth of artificial intelligence. This forms the basis for expert systems that attempt to translate rules and decision-making chains into computer code . However, its advocates underestimated the complexity of the brain, which led to the first „AI winter“ in the 1970s.

The first neural network was designed by psychologist Frank Rosenblatt, who was not present at Dartmouth, in 1956. Inspired by Rosenblatt’s work, physiologist, cognitive psychologist and computer scientist Geoffrey Hinton developed a multilayer neural network and an algorithm at the University of Toronto in 1986 that enabled the system to learn from its calculation errors. This method of backpropagation led to a refinement of the answers. It was the breakthrough for neural networks. However, the computing power was not sufficient for large amounts of data and a second „AI winter“ occurred before the turn of the millennium.

 

Deep learning

An improvement in hardware was achieved with the super-fast chips of the graphics processing units (GPU), which the US semiconductor manufacturer Nvidia initially developed for video games and later used to train multi-layer neural networks. Improved methods of image recognition that used small errors to recognize patterns (Convolutional Neural Network CNN) were then decisive. In 2015, Hinton and his colleagues coined the term deep learning for deeper models with more neuron layers.

 

Transformer architecture

In 2013, important impulses for Natural Language Processing (NLP) came from a Google team that trained a neural network in such a way that the proximity of words within a space reflects their semantic relationship. The team taught its word embedding system (word2vec) to predict the missing word in a sentence. A further development published in 2017 was called the Google Transformer architecture. The basic principle is to find out which words are most important in a sentence (self-attention) and thus „transform“ a text into a summary.

In 2019, OpenAI published its GPT-2 model, which had been trained on 40 gigabytes (eight million websites) with 1.5 billion parameters and should therefore be able to predict the most likely next word in a sequence. GPT stands for Generative Pre-Trained Transformer. On November 30, 2022, OpenAI launched its chatbot ChatGPT to the public. Following a prompt, the chatbot produces longer texts from different fields of knowledge, but is prone to errors (hallucinates). Foundation models that form a training foundation for specific applications and are based on Internet content are known as large language models (LLMs).

This development of artificial intelligence is summarized in the following figure. AI is a generic term for various technologies that describes an extension of aspects of learning and intelligence by a machine.

Lernprozess Innovationsstrategie

With an AI that combines neural networks with the Monte Carlo Tree Research (MCTR) method, DeepMind, a company acquired by Google, has not only managed to defeat one of the world’s best players in the Asian board game Go since 2015, but also to predict the folding of 200 million proteins. This shows that an AI that works according to the principle of reinforcement learning both increases productivity and expands specific knowledge and the resulting skills. When generative AI (GenAI) is applied, this results in new perspectives for knowledge work in companies. With the help of knowledge-specific (domain-specific) GenAI, the European economy, with its high proportion of highly specialized companies, has new opportunities to differentiate itself from the competition.

 

Nobel Prizes for AI researchers

The 2024 Nobel Prizes in Physics went to John Hopfield and Geoffrey Hinton, who conduct research into machine learning and artificial neural networks. One half of the 2024 Nobel Prize in Chemistry went to Demis Hassabis and John Jumper, who work at Google subsidiary DeepMind, for their AI-based prediction of complex protein structures. This makes it clear that there are serious changes in technology and innovation management in the creation of new knowledge.

 

Hype and disillusionment with large language models

Large language models (LLMs) are currently going through a hype cycle. The technological trigger was the chatbot ChatGPT developed by Open AI in November 2022. Large language models based on the Transformer technology presented by Google researchers in 2017 had been around for some time. But ChatGPT reached the masses and had 100 million users after two months.

The peak of exaggerated expectations was demonstrated by a gigantic investment bubble in the race for AI supremacy by large digital companies and start-ups.

The valley of disappointment manifested itself in the unfulfilled management illusion that the high investments would also lead to profitable applications and the resulting stock market illusion.3

Lernprozess Innovationsstrategie

One possible path to enlightenment could come from cost-effective small language models with industry-, company- and process-specific applications.

Whether, when and how exactly a plateau in productivity will be reached with knowledge-based AI is not yet entirely clear. However, we assume that this will result in opportunities for the European economy. AI providers should exploit these opportunities together with users.

 

Advantages of small and specific AI models

Large language models strive to cover as many areas as possible and are primarily trained with data from the internet. This is not only time-, cost- and energy-intensive, but the marginal benefit of additional data decreases. For specialized tasks, the performance of large language models can even deteriorate over time.(4)

Small language models do not have these disadvantages. Their training is based on industry-, company- and process-specific data. The Berlin start-up Xayn, for example, specializes in law firms and legal departments. There are several approaches to training, e.g.

– a Retrieval Augmented Generation (RAG): This involves the coupling of a large language model to internal databases
– Continuous pre-training in the form of domain-specific models and
– training of own models with complete control over the data used.

Start-ups from the USA, such as Databricks, offer their customers the joint development of company-specific AI models. The costs for training these customized models are significantly lower than those for training GPT-4, for example, which amount to almost 80 million dollars. The training is based on individual company data.5 One potential risk is becoming dependent on service providers. The alternative is therefore to empower your own employees. The basis for this is an AI human reource strategy for the company.

When it comes to industry-specific AI solutions, cooperation between established companies and start-ups can be successful.6 The Berlin start-up Linetweet, for example, focuses on AI tools for store management in the retail sector. Linetweet already won the optician chain Fielmann in 2019 with a tool for digital appointment scheduling. This tool was developed further together. Today, Store AI automatically adjusts the schedules in Fielmann stores based on company-specific data, thereby increasing store productivity. So far, Linetweet is wholly owned by the two founders. This example shows the potential of combining the industry-specific knowledge of established companies with the AI expertise of start-ups.

At present, many companies are still focusing on individual processes in their AI applications. The really big breakthrough of AI will probably only come with an integrated view of strategies and business processes.7 These are the topics of our next blog posts.

 

Conclusion

  • The basis for generative AI with large language models is formed by neural networks, the development of which began many decades ago
  • Nobel Prize-winning AI researchers have changed technology and innovation management
  • After the hype phase triggered by ChatGPT, a certain disillusionment is emerging in large language models
  • Knowledge-based AI has a number of advantages that European companies should explore.

 

Literature

[1] Obmann, C., What bosses and employees need to know about AI now. In: Handelsblatt, February 17, 2025, p. 32-33

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

[3] Holtermann, F., Holzki, L., de Souza Soares, A.P., The great crisis of meaning. In: Handelsblatt, August 9/10/11, 2024, pp. 46-51

[4] Bomke, L., Holzki, L., Which AI counts for the economy. In: Handelsblatt, September 23, 2024, p. 20-23

[5] Bomke, L., Kerkmann, C., Scheuer, S., Corporate AI becomes affordable. In: Handelsblatt, May 3 / 4 / 5, 2024, p. 30

[6] Bomke, L., Retailers reorganize their stores with AI. In: Handelsblatt, January 2, 2025, p. 32

[7] Servatius, H.G., Competitive advantages with a knowledge-specific AI. In: Competivation Blog, February 11, 2025

Learning from successful digital companies

Learning from successful digital companies

Six of the seven most valuable companies in the world are leaders in artificial intelligence (AI) technologies. Start-ups are also providing important impetus for the current topic of generative AI. This raises the question of what established companies can learn from these digital champions. The search for answers to this question leads us to a paradigm shift in strategic management that has not been understood by established companies for a long time. Closely linked to this is a change in personnel management and culture.

 

In this blog post, I explain an approach that has contributed to the success of digital companies. In the USA, this approach is known as the „geeky leadership style“.

 

Increasing the enterprise value of the „big six“

The seven most valuable companies in the world (as of 27.06.2024) include Microsoft, Apple, Nvidia, Alphabet, Amazon and Meta. These „big six“ from the USA are benefiting to varying degrees from the current boom in generative artificial intelligence.1 Microsoft alone is currently worth 77% more than all 40 DAX companies combined.

However, the success of US companies is not only based on their digital expertise, but also on management innovations. This combination has led to an advantage over established companies. While the expertise in the different waves of digitalization is obvious, the new management approaches in the success phases of digital companies are far less transparent.

 

Causes of the success phases of digital companies

We therefore investigated the question of what the European economy can learn from successful digital companies. The result is a causal chain that begins with the connection between the first development stage of market- and finance-oriented strategic management and the second stage, which is determined by technology and innovation. This connection has had a theory-changing effect and led to a paradigm shift from a more mechanistic to a complexity-managing strategic management. This paradigm shift has shaped the culture in the success phases of digital companies. During these phases, an innovation-promoting personnel management and disruptive corporate culture has emerged, which represents a difficult barrier for established companies to overcome.

Lernprozess Innovationsstrategie

I would like to explain these three causes in the following sections. A better understanding of the causes can help established companies to better manage the digital transformation. However, a basic prerequisite for this is a willingness to learn and to question traditional cultural norms. The starting point is a system-oriented combination of Strategy 1.0 and Strategy 2.0.

 

System-oriented combination of Strategy 1.0 and 2.0

Since the early 1980s, the traditional market- and finance-oriented strategic management (Strategy 1.0) has been expanded to include a technology- and innovation-oriented second stage of development (Strategy 2.0).2 Successful digital companies have used this expansion to their advantage. On the one hand, their success is based on their lead in digital technologies. At least as important is the system-oriented integration of analysis-oriented strategic action and a culture that promotes innovation. In this way, they have succeeded in implementing a new integrated approach to designing innovation systems.3 This approach is not limited to their own company, but also includes start-up ecosystems.

In successful start-up ecosystems, four sectors work together in partnership. Politicians actively promote education, new technologies and innovations. Science successfully spins off start-ups. Venture capitalists and corporate venture management finance not only the founding but also the scaling of start-ups. Society also plays an important role by creating a positive climate for innovation and attractive framework conditions.

Lernprozess Innovationsstrategie

This interplay has led to the success story of Silicon Valley, which is several decades ahead of Europe.4 However, the example also shows the tension between the current AI boom and the exploding cost of living on the US West Coast.

The development of start-up ecosystems was stimulated by the design science5 and methods such as design thinking, which emerged in the 1960s.6 Design thinking supports the interdisciplinary learning process for designing digital business models. Innovative technologies act as enablers of new forms of problem solving and satisfying customer needs. The action research7 developed by psychology professor Kurt Lewin in the USA back in the 1940s provides the theoretical basis for learning loops that start from hypotheses, design something that can be tested with customers and whose results lead to possible changes in direction. In the early 1990s, agile software development methods such as Scrum were developed on this basis.8 Start-ups that use these concepts have become the most valuable companies in the world.

The example of Amazon shows that these companies also had to overcome critical phases. After the failure of a project to improve collaboration between functional areas, Jeff Bezos recognized the need for a change of direction. He introduced the „two-pizza principle“ for agile teams and implemented the concentration of project managers on a single project (single-threaded leaders). To enable agile teams to work as independently as possible, it was necessary to develop a modular IT architecture. This internal initiative formed the starting point for the founding of Amazon Web Services (AWS), today’s global market leader in the cloud business. 9

The theoretical basis for such activities was provided by a paradigm shift in strategic management that took place in the 1990s. I would like to briefly describe how I experienced this period.

 

Paradigm shift from mechanistic to complexity management

After about a decade in strategy consulting, I had the impression that the existing, relatively mechanistic strategy concepts were not sufficient to cope with the complexity of innovation and sustainability issues. In my search for better solutions, I came across evolutionary and complexity theories and completed an external habilitation at the University of Stuttgart in 1991 on the seemingly necessary paradigm shift in strategic management.10

Lernprozess Innovationsstrategie

Complexity-based strategic management is based on three theoretical foundations that have influenced each other. Firstly, evolutionary theories have emerged in various disciplines. They view the dynamic sequence of imbalances as a balance between chaos and order, the outcome of which depends on the initial conditions.11 Important impetus then came from the Santa Fe Institute, founded in the USA in 1984, and the theory of complex adaptive systems developed there. This deals with the creation of suitable framework conditions for a more self-organized interaction of competent actors at the „edge of chaos“ based on simple rules.12 The theory of complex interactive relationship processes makes a contribution to the application in organizations. The focus here is on local, non-linear interactions between actors, from the course of which patterns emerge that are difficult to predict. 13

These relatively abstract-sounding ideas were difficult to convey to established companies in the 1990s. As a result, even the large consulting firms did not jump on the bandwagon. Nevertheless, the theories have found their way into practice. This path led from Stanford University to Google. As digitalization progressed, the importance of evolutionary and complexity theories increased significantly.

In 1995, the book Competing on the Edge was published by future Google manager Shona Brown and Stanford professor Kathleen Eisenhardt, who attempt to apply complexity theories to strategic management.14 They divide their recommendations for action into the fields of chaos edge, time harmony and timing. The focus here is on overcoming complexity by finding the right balance. In the field of chaos edge, the recommendations for action are as follows:

  • Using professional improvisation to find the middle ground between too much structure and too much confusion and
  • utilize synergies between businesses through joint adaptation in order to find the balance between too much cooperation and too much selfishness.

The recommendations in the Time Harmony field are:

  •  Deriving benefits from the future and the past through targeted renewal and
  •  Carry out experiments to shape tomorrow with experience today.

The last recommendation concerns the timing. It reads:

  •  Set the tempo to synchronize transitions and find your own rhythm.

These recommendations for action have shaped the HR management and culture of Google and other digital companies.

 

Innovation-promoting personnel management and disruptive corporate culture

A specific leadership style has developed in digital companies, which is referred to as the „geeky leadership style“ in the USA. The term geek is undergoing a positive change in meaning. This form of personnel management is culturally influential. It is characterized by the following four cultural norms: 15

  •  A specific scientific approach (Science)
  •  Personal responsibility (ownership)
  •  a high speed of iterations (Speed) and
  •  Openness.

The disruptive nature of such a culture lies in the fact that it is difficult for established companies to develop due to behavioral barriers. I would like to explain this briefly.

Lernprozess Innovationsstrategie

The scientific approach based on action learning and design theory is geared towards data-based, adaptive design. Digital companies such as Google used these findings early on and developed infrastructures for testing hypotheses. The test results then form the starting point for intensive, fact-based argumentation by the stakeholders. In contrast, decisions in established companies are based more on the convictions and power of managers and the opinions of experts. The cultural change to a more scientifically oriented approach can therefore trigger resistance in established companies because those responsible fear a loss of importance. Personnel development at universities and in practice should create a conscious counterbalance here.

Digital start-ups are characterized by the personal responsibility of managers with a higher degree of autonomy, empowerment of agile teams and less coordination effort. Established companies, on the other hand, often struggle with increasing bureaucratization, where many are allowed to have a say and demonstrate their power by exercising a veto. Microsoft was also faced with the challenge of regaining a culture of ownership, which it has succeeded in doing under the leadership of Satya Nadella. Bayer’s attempt to reduce bureaucracy with the help of the humanocracy concept developed by management guru Gary Hamel is the subject of much public debate.16 It remains to be seen how successful this attempt will be.

One root of the „geeky leadership style“ is the agile manifesto written in 2001, which emphasizes the speed of rapid iterations. Established hardware-oriented companies often find it difficult to link this approach, which originated in software development, with their existing product innovation process. In view of the increasing importance of software in the automotive industry, for example, hybrid approaches that combine existing skills with digital expertise are becoming increasingly important. One indicator of success here is that companies achieve their set time targets and do not fall victim to the 90 percent syndrome, in which the players realize too late that they are missing their targets.

Characteristics of the cultural norm of openness are the sharing of information, receptiveness to other arguments, the willingness to re-evaluate situations and change one’s own direction. The opposite of openness is widespread defensive behavior patterns, which Harvard professor Chris Argyris has described as a characteristic of established companies since the 1980s.17 The negative consequence is often that the community punishes those who violate prevailing norms. An extreme form of defensive behavior is the tacit toleration of unethical or punishable activities. On the other hand, a culture characterized by openness can be recognized, for example, by the fact that young employees are allowed to openly contradict their boss in an internal meeting without having to expect sanctions.

This example leads us to an approach on how established companies can reduce the cultural distance to the digital world.

 

Adapting and exemplifying cultural norms

Managers of established companies have the task of finding an individual approach to the cultural norms of successful digital companies. Knowledge of the theoretical principles outlined above can be helpful in this regard. However, success in the digital world does not mean that these norms can be transferred 1:1 to an established company. They need to be adapted to the specific situation and framework conditions of the respective company. Once there is a consensus regarding this situational adaptation, it is important for managers to exemplify changed cultural norms. Appropriate personnel development and promotion policies then play an important role. The idea of a rapid, comprehensive digital transformation is therefore unrealistic. A successful digital realignment in established companies is more likely to be a specific, longer-term process.18

 

Collaboration with start-ups as an underutilized opportunity

One way for established organizations to learn from successful digital companies is to work more closely with start-ups. Unfortunately, too little use is made of this opportunity. A study by the German Start-up Monitor concluded that cooperation between corporations and SMEs and young companies fell by ten percent between 2020 and 2023. Verena Pausder, head of the start-up association, sees this backward trend as an alarm signal and is promoting a revival of the partner culture.19 The current topic of generative artificial intelligence in particular offers a variety of approaches to this. There are a number of initiatives, such as the „Hinterland of Things“ conference, which has been taking place in East Westphalia since 2018 and brings together various players. But overall, there is still considerable potential for expansion in the design of start-up ecosystems.

 

Conclusion

  • Many of the world’s most valuable companies have evolved from start-ups to digital champions in a relatively short space of time
  • To answer the question of what established companies can learn from this, we have analyzed the development of strategic management
  • In contrast to established companies, digital companies have actively driven a paradigm shift in strategic management from mechanistic to complexity management during their success phases
  • A change in personnel management and culture has played an important role here
  • Managers in established companies are faced with the task of exemplifying cultural norms that are adapted to the situation
  • They should make greater use of the opportunity to work together with start-ups

 

Literature

[1] Sommer, U., AI sparks price fireworks. In: Handelsblatt, December 27, 2023, p.1, 4, 6

[2] Servatius, H.G., Evolution of strategic management. In: Competivation Blog, 28.06.2024

[3] Servatius, H.G., Gestaltung des Innovationssystems von Unternehmen. In: Servatius, H.G., Piller, F.T. (eds.), Der Innovationsmanager – Wertsteigerung durch ein ganzheitliches Innovationsmanagement, Symposion 2014, pp. 21-64

[4] Keese, C., Silicon Valley – What is coming to us from the most powerful valley in the world, Knauer 2014

[5] Simon, H.A., The Sciences of the Artificial, 2nd ed., MIT Press 1981 (1st ed.1969)

[6] Kelly T., Kelly, D., Creative Confidence – Unleashing the Creative Potential within us all, William Collins 2013

[7] Marrow, A.J., Kurt Lewin – Life and Work, Ernst Klett 1977

[8] Sutherland, J.J., The Scrum Fieldbook – A Master Class on Accelerating Perfomance, Getting Results, and Defining the Future, Currency 2019

[9] Bryar, C., Carr, B., Working Backwards – Insights, Stories, and Secrets from Inside Amazon, Macmillan 2021

[10] Servatius, H.G., Vom strategischen Management zur evolutionären Führung – Auf dem Weg zu einem ganzheitlichen Denken und Handeln, Poeschel 1991

[11] Beinhocker, E.D., Die Entstehung des Wohlstands – Wie Evolution die Wirtschaft antreibt, mi-Fachverlag 2007

[12] Lewin, R., Die Komplexitätstheorie – Wissenschaft nach der Chaos-Forschung, Hoffmann und Campe 1993

[13] Stacey R., Tools and Techniques of Leadership and Management – Meeting the Challenge of Complexity, Routledge 2012

[14] Brown, S.L., Eisenhardt, K.M., Competing on the Edge – Strategy as Structured Chaos, Harvard Business Review Press 1998

[15] McAfee, A., The Geek Way – The Radical Mindset That Drives Extraordinary Results, Macmillan 2023

[16] Hamel, G., Zanini, M., Humanocracy – Creating Organizations as Amazing as the People Inside Them, Harvard Business Review Press 2020

[17] Argyris, C., Overcoming Organizational Defences – Facilitating Organizational Learning, Allyn and Bacon 1990

[18] Servatius, HG, Triple strategic realignment. In: Competivation Blog, 07.06.2024

[19] Müller, A., Schimroszik, N., Mittelstand moves away from start-ups. In: Handelsblatt, June 13, 2024, p.22

 

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