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Will the future of artificial intelligence be dominated by specialists, or will generalists gain the upper hand? This question not only raises philosophical and technological considerations, but also fundamentally affects the planning and strategy of companies worldwide. In part 1, ‘The Evolution of Machine Learning Positions,’ we demonstrate the steady specialisation in machine learning professions. Accordingly, the technology-driven AI industry must follow the same logic – but that is only half the story.
The book ‘Range’ and the author David Epstein's view are of great interest in this context, which challenges our previous world view of specialisation and the 10,000-hour rule (a general rule of thumb for achieving mastery in a skill). Even though a very deep technical understanding is crucial for some areas of AI, the question arises: will this specialisation alone be enough to meet future challenges? Let's now delve into the world of AI, where every answer raises new questions and the boundaries between specialists and generalists are increasingly blurring.
Expertise in AI: necessity and benefit
The need for specialised AI knowledge is undeniable and growing steadily – we already discussed this in the first part ‘The Evolution of Machine Learning Positions’. In further development, we see a similar expansion and specialisation in other areas of artificial intelligence. The following areas will increasingly be looking for specialists:
AI in personalised medicine
In the healthcare industry, AI-supported diagnostic systems and robot-assisted surgical techniques are significantly expanding the possibilities for patient care. Highly specialised applications improve the precision and efficiency of medical interventions and treatments, resulting in better patient care and safety.
Ethical AI and regulation
As AI technologies become more widespread, the need to consider ethical considerations and regulations is particularly increasing. Specialists are needed who can develop and implement AI applications within the legal framework. As intelligence increases, so does the public's mistrust of the technology. This is because even today, ‘big tech’ does not know what is happening inside the AI.
Reinforcement of human roles through AI
Instead of replacing jobs, AI is increasingly being used to complement human tasks. In areas such as finance, law and education, AI automates repetitive tasks and enables professionals to focus on more complex problem solving and decision-making. This fact is also a pro-argument for generalists.
But specialised AI roles are primarily about combining in-depth technical understanding with industry-specific knowledge. This development can also be seen in SAP experts, who specialise in specific industries such as automotive or healthcare. This development is different from the evolution of machine learning positions, where the development of the technology itself drives specialisation. We will discuss this point in more detail later.
Why generalists are still important in companies
While specialisation in AI is increasing, generalists still play a crucial role in companies. Their broad skills and ability to combine different disciplines are in demand in an increasingly complex working world. Here are the reasons why versatility, in addition to specialisation, is an advantage in modern working life.
Adaptability and versatility
Generalists are characterised by their ability to quickly adapt to new challenges and to integrate information from different areas. These skills are particularly invaluable in ‘wicked environments’ – i.e. environments characterised by constant change and unclear data. After all, we live in a VUCA world (an acronym for volatility, uncertainty, complexity, ambiguity). In such situations, generalists can use their versatile skills to develop and implement innovative solutions.
Broad knowledge and creativity
In his book ‘Range’, David Epstein argues that in a world increasingly characterised by technological advances and rapid change, people with a broad range of knowledge and the ability to apply that knowledge creatively are particularly successful. Generalists who are able to combine their knowledge from different areas and create new connections are often the innovators and problem solvers that drive companies forward. Epstein uses the example of Roger Federer, who attributes his success in tennis to a range of different sporting activities in his youth.
Generalist vs. Specialist: Which Is Better?
Resilience to automation
As the article ‘The Future of Work in an AI-Driven World’ describes, many specialised tasks that operate within fixed rules and repeatable patterns are susceptible to automation by AI.Chess is a classic example in the field of artificial intelligence. Generalists, on the other hand, who are able to think beyond these patterns and apply their skills flexibly, have a significant advantage. They are less easily replaced by automated systems because their work often requires a higher degree of interpretation, critical thinking, and adaptability.
Bridge builders and team players
An older Forbes article highlights further strengths of generalists, using the example of Elon Musk – the ability to link different areas of knowledge and work across teams. The ability to act as a link between different teams and disciplines makes generalists valuable members of any organisation. They foster the communication and collaboration that are central to the successful implementation of AI systems and strategies.
Generalists can view complex problems from different perspectives and develop creative, holistic solutions. They demonstrate flexible ways of thinking that not only overcome technical challenges, but also understand and shape the ethical, social and corporate aspects of the technology.
Balancing specialisation with flexibility
As we have already touched on, specialisation in the modern world of work with AI is not only about in-depth technical knowledge, but also about the importance of specific industry knowledge. Specialists who are familiar with an industry are extremely valuable. Not only for the industry, but also for themselves.
Advantage of industry knowledge: In complex fields such as financial technology, biomedicine or renewable energies, specialists cannot avoid industry-specific knowledge. This in-depth knowledge enables them to develop solutions that are not only technically feasible but also commercially viable. Or, in general, to create new solutions – an ability that is more attributed to generalists due to their broader world view.
Flexibility and adaptability: Although specialisation means gaining expertise in a defined area, today's fast-paced market requires a certain degree of flexibility. Specialists must be able to apply their expertise in new and evolving contexts. Again, this means familiarising themselves with related technologies and methods or managing interdisciplinary projects.
Integrating technology and industry knowledge: In many industries, integrating advanced technology with industry-specific knowledge enables companies to develop (leading) innovations. For example, specialists in the healthcare sector use AI to develop personalised therapeutic approaches based on specific medical data and clinical studies.
Companies are well advised to create an environment that promotes not only the vertical but also the horizontal development of skills (see Fig. 1). This means that specialists should be encouraged to think outside the box and develop skills that go beyond their immediate area of expertise. Such skills include project management, leadership, interdisciplinary work and sales knowledge. All these things contribute to creating a resilient and adaptable team, or even a ‘high performance team’.
The balance between in-depth expertise and broader industry knowledge will be the measure of all things in future roles. What's more, in our VUCA world, these combined skills often mean the difference between progress and stagnation.
Lateral thinking in AI specialisation
Let's take it one step further. In the future, it will be essential to integrate cross-disciplinary perspectives into the development of artificial intelligence. As highlighted in the 2015 Forbes article ‘The One Trait That Elon Musk, Benjamin Franklin, and Marie Curie Have In Common’, lateral thinking – the ability to combine and apply knowledge from different disciplines – is a fundamental factor in groundbreaking innovation and progress. We've known this for a long time, but in the context of artificial intelligence, it is becoming extremely apparent once again.
Interdisciplinary knowledge as a driver of innovation
Historical innovators such as Elon Musk, Benjamin Franklin and Marie Curie have shown that the key to extraordinary breakthroughs often lies in the ability to link different fields of knowledge. The same applies to art. For AI specialists, this means that a deep understanding of other fields such as ethics, biology or energy is fundamental to developing innovative solutions that go beyond conventional approaches.
On side effects and epiphenomena
By applying interdisciplinary knowledge, AI developers can find new approaches to designing algorithms and solving problems. For example, an understanding of neurological processes can help to design more effective neural networks, while philosophical considerations can provide important ethical guidelines for the application of AI.
Strategies for promoting lateral thinking
Companies can encourage lateral thinking by ensuring that their teams are diverse and investing in training programmes. Interdisciplinary workshops and projects help to increase creative exchange and innovative capacity. Here, too, I would like to refer you to our article ‘What is the secret of high-performance teams in IT?’ which is well worth reading.
‘Expert Generalist’
It may also be time for a new model – Orit Gadiesh, CEO of the consulting firm Bain & Company, calls it an ‘expert generalist’ and explains it as follows:
‘An expert generalist is someone who has the ability and curiosity to gain expertise in and a command of many different disciplines, industries, skills, abilities, countries and topics, and then:
- To recognise patterns and connect the dots across multiple areas.
- To dig deeper to focus and perfect thinking.’
Conclusion
Both specialists and generalists can play to their strengths and weaknesses in an AI universe. However, specialists who know the overlaps with neighbouring fields or expand their expertise at the boundaries or in another relevant area – for example, with dedicated industry knowledge – have particularly good cards.
