133 million new jobs through robotics and AI? What is really behind the controversial forecast – and what it means for her

Is your job in danger? This is how you get fit for change on the job market with the right strategies – a comprehensive analysis of the transformation of the labor market: the forecast and its classification

What does the much -discussed forecast of the World Economic Forum say about 133 million new jobs?

In 2018, the World Economic Forum (WEF) published its report "The Future of Jobs", which contained a far -reaching and much -discussed forecast. The core of the statement was that 75 million jobs would be displaced by technological change by 2022, but at the same time 133 million new roles would arise. This would lead to a net profit of 58 million jobs. This transformation was located in the context of the "fourth industrial revolution" (4IR), driven by key technologies such as fast mobile Internet, artificial intelligence (AI), big data analysis and cloud technology.

A central finding of the report was the changing division of labor between man and machine. While 71 % of people were still working in 2018, the report predicted a decline in this share to 58 % by 2022, with the expectation that more current work tasks would be carried out by 2025. The view of the 2018 report was more positive than that of the previous report from 2016. This was justified by the fact that companies had now developed a better understanding of the opportunities that offer new technologies. The report saw itself as a "call to action" of governments, companies and individuals to make this transformation wisely in order to avoid tightening the gaps in competence and growing social inequality.

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How did these forecasts develop and changed in later reports from the World Economic Forum?

The initially optimistic forecast of the WEF has changed significantly in the following years and has become more complex. The development of the predictions shows a significant departure from a purely technology -driven view towards one that takes more attention to macroeconomic and social framework conditions.

The "Future of Jobs Report 2023" drew a much more sober picture for the period until 2027. He predicted the creation of 69 million new jobs, which, however, faced the destruction of 83 million positions. This would lead to a net loss of 14 million jobs or 2 % of the overall employment at that time. This change from a forecast net profit to a net loss marks a significant re -evaluation of the situation.

With the "Future of Jobs Report 2025", which looks at the period until 2030, the WEF returned to an optimistic assessment, albeit with changed premises. This report forecast the creation of 170 million new jobs with a loss of 92 million, which would correspond to a net profit of 78 million positions.

However, the change in the drivers of change is crucial. While the 2018 report focused almost exclusively on the technological revolution, later reports name a wider range of influencing factors. Technology, especially AI and Big Data, remains a central driver. However, the green transformation, macroeconomic factors such as increasing cost of living and slow economic growth, ESG standards (environment, social and corporate management) as well as demographic shifts are given as great or even more important.

This development of the forecasts reveals an important finding: The initial assumption that technological progress virtually automatically leads to net growth in jobs was refuted by reality. The reports show that the potential of the technology for creating jobs depends heavily on the economic and political framework. The report from 2025 identifies slow economic growth as a main driver for the annihilation of jobs, while investments in the green transformation are seen as an essential engine for creating new spots. So the promise of technology is not absolutely, it is conditionally. A positive result is not an inevitable result of innovation, but depends on a healthy and supportive macroeconomic environment.

Change in labor market: How technology and green transformation create jobs

Development of net forecasts for WEF's jobs. The table illustrates the change of forecasts from a purely technology -driven optimism towards a more complex view that includes economic and ecological factors.

The labor market is changing, driven by the influences of technology and green transformation. In the period from 2018 to 2022, technological developments such as artificial intelligence, big data and cloud technologies created 133 million new jobs, while 75 million jobs were replaced, which led to a net increase of 58 million. From 2023 to 2027, however, 69 million jobs will arise, but 83 million will be lost, which is due to technological changes, economic pressure and increasing living costs and results in net acceptance of 14 million jobs. For the period from 2025 to 2030, a strong increase in employment with 170 million new positions compared to 92 million, with technology, green transformation, ESG criteria and macroeconomic factors are the main drivers of change, which leads to a net increase of 78 million jobs.

What methodology are these numbers based on and what are the criticisms of this approach?

The prominent numbers of WEF are based on the "Future of Jobs Survey", a survey aimed at managers in the areas of personnel, strategy and management of large, multinational companies. For example, 313 global companies were interviewed for the 2018 report, which together represent over 15 million employees in 20 economies, which in turn make up 70 % of the global gross domestic product.

It is crucial to understand that the often quoted numbers such as "75 million displaced" and "133 million new" jobs are the result of an extrapolation. The companies surveyed predicted a decline of 984,000 jobs and an increase of 1.74 million for their own workforce. These internal company trends were subsequently extrapolated to the global non-agricultural employment population in large companies, whereby data from the International Labor Organization (ILO) served as the basis. The methodology explicitly excludes small and medium -sized companies (SMEs) and the informal sector, which is a significant restriction, as these make up a large part of the global employment.

There is well -founded criticism of this methodological approach:

First, the reports are accused of an overly overly optimism and a narrative bias. Critics argue that WEF's stories tend to support the organization's goals to promote global cooperation, which can lead to a positive representation. The fluctuating between the dark warnings from 2016, the strong optimism of 2018 and the more complex image of later years indicates a pattern of over -correction instead of a stable, consistent analysis.

Second, the focus on a “net profit” in jobs is criticized as misleading. This approach compared to the "Gambler's Fallacy" (playing player) ignores the massive hurdles during the transition. He incorrectly suggests that a repressed employee can easily switch to one of the new roles. However, enormous qualification gaps – a cashier is not neglected overnight as a devops – -geographical inequalities as well as differences in work quality and payment. The net number obscures the immense human and social costs of the transition.

Third, the forecasts are based on questionable assumptions. The reports imply that cost reductions by AI will lead to a multiplication of "Mensch + Ki" roles that compensate for the loss of jobs in entire teams. Critics consider this assumption to be unrealistic, especially since the forecast growth in sectors such as the green economy and the care sector, which are underfunded or politically controversial in many large economies, is to take place.

Finally, failure of earlier forecasts in question the credibility of the assumptions. The prediction of the WEF from 2018 that a massive “retraining revolution” would take place by 2022 did not have been true to the expected extent. The efforts often remained inadequate, underfunded and encountered logistical hurdles, which makes the feasibility of the assumptions on which the job forecasts are based.

The change in the professional landscape: Winner and Loser of Automation

Which specific professional fields and roles are displaced by AI and automation?

The transformation of the labor market through AI and automation leads to a significant polarization, in which certain professions are exposed to a high risk of displacement. Above all, activities based on routines are affected both in the commercial area (white collar) and in production (blue collar). The most endangered demographic groups are office workers, employees with low digital competence and older workers.

A consistent list of professions is mentioned across various WEF reports, the demand of which is falling sharply. This includes:

• Data input staff (Data Entry Clerks)
• Clerk in accounting and payroll (Accounting, Bookkeeping and Payroll Clerks)
• Administrative and secretarial forces (administrative and executive Secretaries)
• Assembly and factory workers (in certain industries)
• Cashier and switch staff (Cashiers and Ticket Clerks)
• Bank employee at the counter (Bank Tellers)
• Postal service employee (Postal Service Clerks).

Recent reports, such as the "Future of Jobs Report 2025", are expanding this list with further professions from the knowledge work area. Graphic designers and lawyers are now counted among the shrinking professional fields. This is explicitly attributed to the progressive skills of generative AI, which is increasingly able to take on demanding cognitive tasks.

Which new and growing professions arise in the course of this technological revolution?

In parallel to the displacement of routine tasks, there is a high demand for new and developing professional fields. These growth areas are not exclusively technical in nature, but also include roles that specifically require human skills.

Technology -oriented professions are at the center of growth. The fastest growing roles are consistently:

• KI and Machine Learning specialists
• Big data specialists
• Experts in process automation
• Analysts for information security
• Software and application developers
• Robotics engineers
• FinTech engineers.

At the same time, the demand for occupations that are based on pronounced "human" skills is increasing. These include:

• Sales and marketing experts
• Specialists for personnel and corporate culture (People and Culture Specialists)
• Experts in organizational development
• Innovation manager
• Customer supervisor.

Another rapidly growing sector is the green economy. Later reports emphasize the strong growth of professions such as:

• Engineers for renewable energies
• Engineers for solar energy systems
• Sustainability managers.

The educational and care sector also records robust growth. Professions such as doctors, nurses and teachers are expected to increase, driven by demographic developments such as the aging of society and the fact that these activities are difficult to automate.

It is important to distinguish between the percentage fastest growth and the greatest growth in absolute numbers. While Tech professions are growing fastest in percentage, the greatest absolute growth in frontline professions such as land workers, delivery drivers and construction workers are expected.

Future of work: these professions are gaining and losing importance

Consolidated overview of growing and shrinking professional fields. The table summarizes the forecasts from different reports and shows the winners and losers of the transformation of the labor market.

The future of work shows significant changes: In the areas of technology and data, professions such as AI and Machine Learning specialists, big data specialists, software developers and information security analysts are gaining in importance, while simple activities such as data input and IT support technicians are declining. In the field of economy and management, sustainability managers, innovation managers, experts in process automation as well as sales and marketing experts are increasingly in demand, while administrative and secretarial forces as well as accounting and payroll accounting are lost in relevance. In the green economy, engineers for renewable energies, specialists for electric vehicles and environmental engineers are increasing, at the same time professions are disappearing in the fossil energy industry. In the nursing and educational sector, nursing staff, doctors, teachers and consultants for social work become more important, whereby no professions lose their importance. In the office and administration area, bank employees, postal service employees, cashiers, graphic designers and lawyers are in particular declining, while in the craft and the production, agricultural workers, delivery drivers and construction workers grow in absolute figures, while assembly and factory workers are less in demand by automation.

Which overarching trends, such as the green transformation, also influence the development and decline in jobs?

The dynamics on the job market are not determined solely by automation. A number of macrotrends interact and form the professional landscape of the future.

The green transformation, i.e. investments in climate protection and adaptation to climate change, is considered one of the greatest net job engines. This trend drives the demand for engineers for renewable energies and environmental protection as well as sustainability specialists.

Economic framework conditions have an equally strong but often opposite effect. Slow economic growth and rising living costs are classified as net shredders of jobs and can sometimes destroy the profits created by technology and green transformation.

The technology adoption itself is a double-edged sword. The expansion of digital access is expected to create the most jobs (19 million), but also many (9 million). Ki and Big Data follow the second largest driver, with 11 million created and 9 million repressed positions.

Demographic shifts also play a crucial role. An aging population in countries with high incomes drives demand in the health and care sector. At the same time, a growing employable population in countries with low incomes leads to an increased need for workers in the education sector.

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The gap in competence: Which skills are required in the future

What is the “competence gap” (Skills Gap) and how big is this challenge?

The "competence gap" or "skills gap" describes the discrepancy between the skills that employers need for their vacancies, and the actually existing qualifications of the available workers. This gap is one of the central challenges of the current labor market transformation.

The extent of this challenge is enormous. The 2018 WEF report predicted that by 2022, 54 % of all employees would need considerable retraining and further education measures (reskilling and upsky). Later reports confirm and tighten this assessment: The "Future of Jobs Report 2025" notes that the core competencies of 44 % of employees will change over the next five years, and by 2030 almost 40 % of the skills required for a job will be outdated.

This statistical reality is reflected in the perception of corporate leaders. In the United States, 70 % of managers indicate that there is a critical gap in competence in their organization that has a negative impact on innovation and growth. Almost 40 % of these managers believe that this gap even worsens.

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Which specific technical and digital skills are most urgently needed?

On the side of the technical skills, also called "Hard Skills", there is a clear hierarchy of demand. At the forefront there are competencies that are directly connected to the driving technologies of the fourth industrial revolution.

At the top of the most sought -after skills are constant AI and big data. The ability to deal with large amounts of data and use or develop AI systems is considered crucial. Further core competencies of digitization are closely linked to this: basic technological competence (technological literacy), network and cyber security, software and application development, data analysis and cloud computing are also extremely in demand.

Interestingly, project management is often mentioned as one of the most important technical skills. This underlines the need to combine technical implementation competence with strategic business planning and successfully control complex digitization projects.

Why are “human” skills such as analytical thinking, creativity and resilience considered even more important?

At a time when machines take on more and more technical tasks, a paradox is created: Technical skills are the fastest growing but cognitive and social-emotional skills are often classified as the most important. This can be explained by the economic logic of scarcity and benefits. Since AI routine tasks – be they technical or cognitive in nature – makes it available in abundance and low costs, the skills that are used exclusively to carry out these tasks lose value.

At the same time, tasks remain difficult to automate the tasks that require new problem solving, strategic thinking, ethical judgment and complex interpersonal interactions. When machines take on the "what" and "how" many activities, the human role shifts to the "why" and the "what next". This requires the ability to define problems, to interpret the results of the AI creatively, convince interest groups and to manage complex human teams. This is exactly what the so -called "human" skills are essential for.

An “automation bonus” for non-automatic skills is created. The economic value and the demand for these unique human skills increase disproportionately. The most important of these skills are:

• Analytical and creative thinking: These are consistently at the top of the skills that are most requested by employers.
• Adaptability: resilience, flexibility and agility are of the utmost importance, since employees have to be able to find their way around in a constantly changing environment.
• Leadership and social competence: leadership skills, social influence, emotional intelligence as well as curiosity and lifelong learning are also crucial, since AI can hardly replicate these skills.

The gap in competence is therefore not just a lack of technical skills. It is a division of the competence market: the value of routine skills breaks down, while the value of non-routine, deeply human skills skyrocket. The most effective strategies for personnel development will therefore not only teach programming, but also combine this with training in critical thinking and creativity.

Sustainable in the job: the balance of soft skills and tech know-how

Key skills for the future world of work. The table shows the dual importance of technical and human skills and orders them according to their importance perceived by employers.

To be future-proof in the job means to find the right balance between soft skills and technical know-how. In the first place, human skills such as analytical and creative thinking are. Densified by technical knowledge in the areas of artificial intelligence, big data and basic technological skills. Resilience, flexibility and agility are also important as further human skills. On the technical side, networks, cyber security and data analysis are becoming increasingly important. Curiosity, lifelong learning as well as leadership and social influence are also among the decisive human abilities. This is supplemented by technical expertise in software and application development as well as project management.

Strategies for coping with change: retraining, further education and new working models

Which strategies pursue companies to prepare their workforce for the future?

In view of the extensive gap in competence, companies develop proactive strategies to prepare their workforce for the future. These strategies go beyond simple training measures and aim at a fundamental realignment of personnel development.

A central approach is strategic personnel planning. Companies analyze their current skills in comparison with future requirements and develop targeted programs for retraining (reskilling) and further training (upsky). The goal is to build a "sustainable competence architecture", which makes the workforce resistant to future shocks.

The strategic focus shifts from the pure replacement of workers through technology to augmentation, i.e. the targeted strengthening of human skills through technological tools. This manifests itself in the concept of human-machine collaboration, in which the strengths of both sides are combined.

Investments in further training are a concrete expression of this strategy. 60 % of companies actively invest in training programs for their employees, with a focus on AI, digital skills and leadership skills. At the same time, companies promote internal mobility by creating clear career paths to keep talents within the organization and to develop them further.

Innovative companies also integrate learning directly into everyday work. The proven practices include training executives into coaches who lead their employees, as well as the use of peer-to-peer learning models, in which experienced colleagues pass on their knowledge.

What are successful retraining initiatives in practice? A look at the programs by Amazon, AT&T and Siemens.

Some globally leading companies have already started extensive and far -reaching initiatives to qualify their employees, which can serve as case studies for successful strategies.

With its “Upskilling 2025” initiative, Amazon has provided a budget of $ 1.2 billion to train hundreds of thousands of employees. The core programs include the "Amazon Technical Academy", which trains employees without a technical background, the "Machine Learning University" for advanced and the "Career Choice" program that takes over tuition fees. The results are measurable: 75 % of the participants recorded a career ascent, and their salary rose on average by 8.6 %.

With his “Future Ready” program, AT&T invested around $ 1 billion in retraining his workforce. The company found that half of its employees did not have the skills necessary for the future, and deliberately chose an internal qualification offensive instead of mass discounts and new settings. The program focuses on areas such as data science and cyber security and uses online platforms as well as personalized career portals to offer employees flexible learning opportunities.

Siemens follows an approach in which digital transformation and employee qualification go hand in hand. The company uses cloud technologies such as Amazon Web Services (AWS) for comprehensive modernization, from data infrastructure to the use of generative AI. An outstanding example is the Siemens electronics work in Erlangen. There, an industrial 4.0 solution was implemented, which reduced the operational time for machine learning by 80 %. At the same time, the workforce in production was trained in real-time data analysis and the Internet of Things (IoT). This shows how upskilling can be embedded directly into the operational transformation.

What role does the state play? An analysis of the German Qualification Chances Act.

In addition to entrepreneurial initiatives, state framework conditions also play a crucial role in coping with structural change. The German Qualification Chances Act is an example of proactive state politics.

The law aims to support companies in further training their employees, especially in professional fields that are affected by technological or structural changes. It offers significant financial incentives: The Federal Employment Agency can cover up to 100 % of the further training costs and also subsidize up to 75 % of the employee's work fees during the qualification measure. The amount of funding depends on the size of the company, with smaller companies being supported more.

The aim of the law is to strengthen the competitiveness of the German economy, to secure the jobs of the employees and to actively counteract the shortage of skilled workers in future fields such as UX design, data science and product management.

Could more radical approaches such as the four-day week or an unconditional basic income (BGE) be part of the solution?

The profound changes on the labor market also raise questions about fundamental redesign of work and social security. Two intensely discussed models are the four-day week and the unconditional basic income (BGE). These approaches can be understood as two different, but potentially complementary answers to the challenges of automation.

The four-day week aims to improve the quality of the existing work by passing on productivity gains to employees in the form of time. Large international pilot studies with 141 companies and over 2,800 employees have shown impressive results. The companies reported stable or even increased sales (sometimes by up to 35 %), while the employees reported a drastic decline in burnout (up to 70 %), stress and anxiety as well as improved mental health and sleep quality. Personnel fluctuation fell, and over 90 % of the participating companies kept the model after the test phase. The success is based on the "100-80-100" model (100 % wages, 80 % time, 100 % productivity), which is achieved by redesigning work processes and reducing unnecessary meetings.

The unconditional basic income (BGE), on the other hand, aims to create social security outside of gainful employment by decoupling a basic income of employment. It primarily addresses the problem of those who could be displaced by the labor market or are in precarious employment relationships. The results from worldwide pilot projects are mixed and heavily dependent on the context. Positive effects such as lower nutritional uncertainty, improved health, higher school visit rates and an increase in start -ups were observed in Kenya and India. The pilot project in Stockton, California, showed positive psychological effects without negative effects on work motivation. Other studies, such as the early experiments in the United States in the 1970s or the Finnish experiment, showed a slight reduction in work incentives or no significant change in the employment rate, but an improvement in well -being. A significant restriction of many of these studies is their limited duration and its small scope, which makes it difficult to transfer to a permanent, universal system.

These two models are not mutually exclusive. Rather, they could address different facets of the same transformation. A future strategy could establish the four-day week as a standard for full-time employment in order to improve the quality of life of the employed. At the same time, a BGE could serve as a social foundation for those who are in the transition in the gig economy or whose jobs have been completely replaced by automation. This would enable a more resistant and fairer social answer to change than any of these measures on their own.

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Socio -economic consequences: inequality, regional disparities and work quality

Is artificial intelligence intensifying income and assets or can it reduce it?

The question of how AI affects inequality is one of the most urgent socio -economic debates from the present, and research provides nuanced and partly contradictory results.

On the one hand, there are arguments that AI could reduce wage inequality. In contrast to previous waves of automation, which primarily concerned low-qualified routine work, the current AI wave aims at highly paid "White-Collar" professions. Studies at the level of tasks show that the low-qualified employees often experience the greatest productivity increases by AI tools within a profession (e.g. in customer service or in software development). This could potentially strengthen the middle class wages and reduce the wage scissors.

On the other hand, the arguments for an increase in the total accuracy outweigh. First, the productivity benefits of the AI could primarily benefit highly paid knowledge workers who have access and skills to use these tools, while low earners in service and craft professions remain. Second, the AI-controlled automation tends to lead to a shift in the income shares from work to capital. Since less human work is required for the same production, the owners benefit from capital (e.g. shareholders) disproportionately, which exacerbates inequality in favor of the already wealthy.

A working paper of the International Monetary Fund (IMF) brings together these two aspects and affects a decisive distinction: AI could easily reduce walness inequality (by suppressing high earners), but drastically increasing the asset's inequality. The mechanism behind it is that the same highly paid employees who experience wage pressure are also the largest capital owners. You therefore benefit most from the rising capital yields caused by automation. In addition, high wage premiums for people with sought-after AI skills – a PWC study found a bonus of 56 % – gap between those with and without these skills.

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How does the technological transformation affect the regional differences in Europe and the USA?

The technological transformation also has a strong geographical dimension and threatens to tighten existing regional inequalities.

Growth and new jobs are increasingly concentrating in urban centers and capitals. These regions have a higher density of knowledge -intensive and long -distance work -capable (telescope) jobs. In the EU, the main strokes of the city recorded the strongest employment growth. In the United States, McKinsey has already predicted that urban areas would experience net growth in workplaces, while rural districts could be confronted with a decades of loss of job.

This trend leads to a self -reinforcing spiral: cities attract employers, specialists and investments with their dynamic labor markets and their good infrastructure, while rural areas have to struggle with the loss of jobs and a weaker infrastructure. The regional disparities in the EU have increased since the great recession, a trend that could still be exacerbated by pandemic and progressive automation, since poorer regions often have a lower quota of long -distance jobs. Tech centers will in future secure their economic strength less through job growth than by increasing productivity, which continues to concentrate economic power.

Does automation improve the quality of work through the elimination of monotonous tasks or leads you to more surveillance and stress?

The effects of AI on the daily work experience are ambivalent and strongly depend on the type of implementation.

From a positive perspective, AI can significantly improve the quality of work. By automating monotonics and repetitive tasks, employees can concentrate on more creative, strategic and appealing activities. In some sectors, employees who use AI report on greater job satisfaction and more pleasure in their work. In addition, AI can improve occupational safety, especially in physically exhausting activities.

However, the negative perspective emphasizes the risks of alienation and increased control. AI enables a new extent of employee surveillance, which can lead to an increased labor intensity, more stress and loss of autonomy. The pressure to be more productive in a compressed or AI-based working environment can lead to burnout if it is not carefully managed. Among workers therefore also have fears of loss of job, the loss of negotiation power in wages and increasing control through management.

Historical context and outlook: the AI revolution in comparison

What are the parallels and the fundamental differences between the current AI revolution and the industrial revolution?

To classify today's transformation, a look at history is helpful. The AI revolution has both parallels and fundamental differences to the industrial revolution.

One of the parallels includes that both revolutions are characterized by technological upheavals, redesign labor markets, displace old professions and create new ones. Both led to considerable social upheavals, urbanization (or their digital equivalent) and intensive debates about inequality and the distribution of productivity gains.

However, the differences are more serious:

• Muscle strength vs. mental force: The industrial revolution, automated and expanded primarily human muscle strength (physical work). The AI revolution, on the other hand, automatically and expand human cognition (thinking). This is a qualitative leap, not just a gradual change.
• Speed and extent: The AI revolution takes place much faster and compressed in a few decades. The social and regulatory adaptation has trouble keeping up at this pace.
• The nature of the new jobs: During the industrial revolution, repressed agricultural workers were able to switch to factories, whose work was still based on human work. It is less clear today whether repressed cognitive workers can easily switch to the new AI-related roles, which often require a much higher level of abstract skills.
• The end goal of technology: The machines of the industrial revolution were tools that were operated by humans. However, the declared goal of some leading AI developers is to create systems that can do all economically valuable tasks. This carries the risk of making human work superfluous in many areas – a danger that did not previously exist in this form.

What can we learn from history about the adaptability of society and the labor market?

The history of the industrial revolution offers valuable lessons for dealing with today's AI revolution.

The experience of textile workers in the early 19th century shows that massive increases in productivity in an industry do not automatically lead to higher wages for workers, especially if their negotiation power is weak. The real wages of many workers stagnated for decades, although the economy grew.

Work quality and autonomy are crucial. The transition from the factory work meant a drastic deterioration in working and living conditions for many and was a main cause of social unrest. This is an important teaching for today's implementation of AI-controlled management and monitoring systems.

The social adaptation is a slow and painful process. The company finally adapted to the industrial revolution – with new work laws, educational systems and social programs – but this process was lengthy, conflict and shaped by suffering.

One of the most important lessons, however, is that the direction of technology is not a fate, but a choice. Decisions can be consciously made to develop technologies that expand human skills and create new, meaningful tasks instead of just automating and displacing work.

Which central fields of action arise for politics, companies and each individual in order to successfully design the change?

The analysis of the transformation of the labor market results in clear fields of action for all actors involved.

For politics:

• Investments in education: Governments have to invest massively in education and lifelong learning and integrate both AI competence and "human" skills such as critical thinking.
• Promotion of the transformation: You should create an environment that supports the change in workers, for example through political instruments such as the German Qualification Chances Act.
• Strengthening social security: social security systems have to be strengthened and new models such as a BGE must be considered to support repressed employees and combat inequality.
• Regulation: A clever regulation is needed to ensure that AI is ethically developed and used, employee rights are protected and excessive monitoring is prevented.

For companies:

• Active role in qualification: Companies must take on an active role in retraining and further education their own workforce and focus on the expansion of human skills (augmentation) instead of being replaced.
• Competence -based approach: You should pursue a competence -based approach in talent management that promotes internal career paths and mobility.
• Culture of learning: The creation of a culture of continuous learning and psychological security is crucial to make it easier for employees to adapt to change.

For everyone:

• Proactive lifelong learning: Each individual has to pursue a proactive approach to their own lifelong learning and accept an agile way of thinking.
• Building a competence portfolio: The best security against automation is to build a portfolio that includes technical skills and unique human skills such as creativity, critical thinking and adaptability.

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