AI is changing the world of work
- The pace is new
Classification of various studies by McKinsey & Co. against the background of current developments
The world of work is undergoing fundamental change. Digitalisation, automation and artificial intelligence (AI) are driving a transformation that is not only making processes more efficient, but also transforming entire job profiles. Studies such as "The Future of Employment" by Frey and Osborne (2013), the categorisation for Germany by the BMAS (2015) and the McKinsey analysis "A Future That Works" (2017) clearly show that technological progress is inevitable - and with it the need for strategic reorientation in companies.
As the annual figures show, these developments were predicted over 10 years ago. Anyone who is surprised now is therefore a little too late. Only the pace of change has only multiplied with the era of artificial intelligence that began in 2022. Against this backdrop, the figures and timeframes used in the studies at the time certainly need to be reassessed.
Frey and Osborne analysed the automatability of 702 professions based on technical criteria such as perception, creative intelligence and social intelligence.
The result: around 47% of jobs in the USA are potentially automatable from a technical perspective.
Routine tasks in administration, production and logistics are particularly affected. Less affected are professions with a high creative, social or complex problem-orientated component, such as teachers, managers or healthcare professions.
In 2015, the Federal Ministry of Labour and Social Affairs commissioned a study on the transferability of the Frey/Osborne results to the German labour market.
The result: due to structural differences (e.g. higher level of education, different industry structure), the proportion of potentially automatable jobs in Germany is around 42%.
At the same time, the study shows that not every technically possible automation will be economically viable or socially accepted. In addition, new task profiles and job profiles are emerging as a result of the change.
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The later McKinsey analysis "A Future That Works" relativises the radical view of Frey/Osborne: only 5% of all occupations can be fully automated. However, up to 60% of activities within a job can be replaced by automation. It is therefore not a question of job losses in their entirety, but of a far-reaching change in the content of work.
McKinsey emphasises the need for further training, restructuring and proactive management of change. Companies that adapt at an early stage secure competitive advantages. Those who persevere risk being left behind.
Developments in recent years - particularly in the field of generic AI systems such as GPT, Claude or Gemini - are fundamentally changing the perspective on automation and digitalisation. While earlier studies limited automation primarily to repetitive, rule-based tasks, it is now becoming clear that creative, communicative and knowledge-based activities can also be automated - at least in part.
Generative AI can write texts, perform complex analyses, write code, carry out legal research or assist with medical diagnoses. This significantly shifts the boundary between "non-automatable" and "partially automatable". Activities that were considered protected just a few years ago are now affected by structural change - including professions in journalism, marketing, law and even management.
At the same time, however, generic AI also opens up enormous opportunities to increase productivity, individualisation and scalability. The decisive factor will be how companies integrate these tools into existing processes - and how quickly they do so.
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Conclusion
The transformation of the world of work is not a dystopian future scenario, but is real and already underway. Developments in the field of generic AI are adding to the momentum - and pushing the boundaries of what is feasible. Decision-makers have a responsibility to actively shape this change. If you don't move with the times, you move with the times. Now is the time to set the course - for competitiveness, innovative strength and a sustainable working world of tomorrow.
Recommendations for decision-makers
- Establish a technology radar: Monitoring relevant technological developments in your own industry environment.
- Accelerate skills development: Strategically embed further training in digital skills.
- Analyse automation potential: Identify areas with high automation potential through RPA/AI.
- Integrate generative AI in a targeted manner: Initiate pilot projects to test potential in your own company.
- Establish change management: Managing change as a strategic project, including communication and employee involvement.
- Utilise cooperations: Collaboration with specialised technology providers for pilot projects and know-how transfer.
