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

Is your job at risk? Here's how to prepare yourself for the changing job market with the right strategies – A comprehensive analysis of the labor market transformation: The forecast and its classification

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

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 message was that while technological change would displace 75 million jobs by 2022, it would simultaneously create 133 million new roles. This would result in a net gain of 58 million jobs. This transformation was situated within the context of the "Fourth Industrial Revolution" (4IR), driven by key technologies such as high-speed mobile internet, artificial intelligence (AI), big data analytics, and cloud technology.

A key finding of the report was the changing division of labor between humans and machines. While in 2018, 71% of working hours were still performed by humans, the report predicted a decline in this share to 58% by 2022, with the expectation that by 2025 machines would be performing more current job tasks than humans. The outlook of the 2018 report was noticeably more positive than that of its predecessor from 2016. This was attributed to the fact that companies had since developed a better understanding of the opportunities offered by new technologies. The report was conceived as a “call to action” for governments, businesses, and individuals to manage this transformation wisely in order to avoid a widening skills gap and growing social inequality.

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How have these forecasts evolved and changed in later reports from the World Economic Forum?

The WEF's initially optimistic forecast has changed considerably in subsequent years and become more complex. The evolution of the predictions shows a clear shift away from a purely technology-driven perspective towards one that takes macroeconomic and social conditions more strongly into account.

The "Future of Jobs Report 2023" painted a much more sobering picture for the period up to 2027. It predicted the creation of 69 million new jobs, but this would be offset by the loss of 83 million jobs. This would result in a net loss of 14 million jobs, or 2% of total employment at the time. This reversal from a projected net gain to a net loss marks a significant reassessment of the situation.

With its "Future of Jobs Report 2025," which covers the period up to 2030, the WEF returned to a more optimistic outlook, albeit with revised assumptions. This report forecasts the creation of 170 million new jobs while 92 million are lost, resulting in a net gain of 78 million jobs.

Crucially, the drivers of change have shifted. While the 2018 report focused almost exclusively on the technological revolution, subsequent reports identify a broader range of influencing factors. Technology, particularly AI and big data, remains a key driver. However, the green transformation, macroeconomic factors such as rising living costs and slow economic growth, ESG (environmental, social, and governance) standards, and demographic shifts are now considered equally or even more important.

This evolution of forecasts reveals an important insight: the initial assumption that technological progress would almost automatically lead to a net increase in jobs has been refuted by reality. The reports show that technology's potential to create jobs is highly dependent on the economic and political framework. For example, the 2025 report identifies slow economic growth as a major driver of job losses, while investments in the green transition are seen as a key engine for creating new jobs. The promise of technology is therefore not absolute, but conditional. A positive outcome is not an inevitable result of innovation, but depends on a healthy and supportive macroeconomic environment.

The changing job market: How technology and green transformation are creating jobs

Development of the WEF's net job forecasts. The table illustrates the shift in forecasts from purely technology-driven optimism to a more complex perspective that incorporates economic and environmental factors.

The labor market is undergoing a transformation, driven by the impacts of technology and the green transition. Between 2018 and 2022, technological developments such as artificial intelligence, big data, and cloud technologies created 133 million new jobs while displacing 75 million, resulting in a net increase of 58 million. However, from 2023 to 2027, 69 million jobs are expected to be created, but 83 million will be lost, due to technological changes, economic pressures, and rising living costs, resulting in a net decrease of 14 million jobs. For the period from 2025 to 2030, a significant increase in employment is projected, with 170 million new jobs compared to 92 million displaced. Technology, the green transition, ESG criteria, and macroeconomic factors are the main drivers of this change, leading to a net increase of 78 million jobs.

What methodology are these figures based on, and what criticisms exist of this approach?

The prominent figures from the WEF are based on the "Future of Jobs Survey," a survey of executives in human resources, strategy, and management at large, multinational companies. For the 2018 report, for example, 313 global companies were surveyed, representing over 15 million employees in 20 economies, which in turn account for 70% of global GDP.

It is crucial to understand that the often-cited figures such as "75 million jobs lost" and "133 million new jobs" are the result of extrapolation. The surveyed companies projected a decline of 984,000 jobs and an increase of 1.74 million within their own workforce. These internal trends were then extrapolated to the global non-agricultural workforce in large companies, using data from the International Labour Organization (ILO). The methodology explicitly excludes small and medium-sized enterprises (SMEs) and the informal sector, which is a significant limitation given that these constitute a large share of global employment.

There is well-founded criticism of this methodological approach:

First, the reports are accused of a tendency toward over-optimism and narrative bias. Critics argue that the WEF's narratives tend to support the organization's goals of promoting global cooperation, which can lead to an overly positive portrayal. The fluctuation between the dire warnings of 2016, the strong optimism of 2018, and the more complex picture of later years suggests a pattern of overcorrection rather than a stable, consistent analysis.

Secondly, the focus on a “net gain” in jobs is criticized as misleading. This approach, often compared to the “gambler’s fallacy,” ignores the massive hurdles involved in the transition. It falsely suggests that a displaced worker can easily move into one of the new roles. However, it overlooks enormous skills gaps—a cashier doesn’t become a DevOps engineer overnight—geographical inequalities, and disparities in job quality and pay. The net gain obscures the immense human and social costs of the transition.

Third, the forecasts are based on questionable assumptions. The reports imply that cost reductions through AI will lead to a proliferation of "human + AI" roles, offsetting the loss of jobs across entire teams. Critics consider this assumption unrealistic, particularly since the projected growth is expected to occur in sectors such as the green economy and healthcare, which are underfunded or politically contested in many major economies.

Finally, the failure of previous forecasts calls into question the credibility of the assumptions. The WEF's 2018 prediction that a massive "retraining revolution" would take place by 2022 has not materialized to the expected extent. Efforts have often been inadequate, underfunded, and encountered logistical hurdles, casting doubt on the feasibility of the assumptions upon which the job forecasts are based.

The changing professional landscape: Winners and losers of automation

Which specific professions and roles will be displaced by AI and automation?

The transformation of the labor market through AI and automation is leading to a significant polarization, with certain professions facing a high risk of displacement. This particularly affects routine-based jobs, both in white-collar and blue-collar sectors. The most vulnerable demographic groups are office workers, employees with low digital skills, and older workers.

Across various WEF reports, a consistent list is cited of professions whose demand is declining sharply. These include:

• Data Entry Clerks
• Accounting, Bookkeeping and Payroll Clerks
• Administrative and Executive Secretaries
• Assembly and factory workers (in certain industries)
• Cashiers and Ticket Clerks
• Bank teller at the counter (Bank Tellers)
• Postal Service Clerks.

More recent reports, such as the "Future of Jobs Report 2025," expand this list to include further knowledge-based professions. Graphic designers and paralegals are now also counted among the shrinking job categories. This is explicitly attributed to the advancing capabilities of generative AI, which is increasingly able to take on demanding cognitive tasks.

What new and growing professions are emerging as a result of this technological revolution?

Alongside 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 require specifically human skills.

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

• AI and machine learning specialists
• Big data specialists
• Experts in process automation
• Information security analysts
• Software and application developers
• Robotics engineers
• FinTech engineers.

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

• Sales and marketing professionals
• People and Culture Specialists
• Experts in organizational development
• Innovation Manager
• Customer service representative.

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

• Renewable energy engineers
• Solar energy engineers
• Sustainability manager.

The education and care sectors are also experiencing robust growth. Occupations such as doctors, nurses, and teachers are expected to increase, driven by demographic trends like the aging population and the fact that these jobs are difficult to automate.

It is important to distinguish between the fastest percentage growth and the largest growth in absolute numbers. While tech jobs are growing the fastest in percentage terms, the largest absolute growth is expected in frontline jobs such as farmworkers, delivery drivers, and construction workers.

The future of work: These professions are gaining and losing importance

A consolidated overview of growing and shrinking occupational fields. The table summarizes forecasts from various reports and shows the winners and losers of the labor market transformation.

The future of work is showing clear changes: In the technology and data sectors, professions such as AI and machine learning specialists, big data specialists, software developers, and information security analysts are gaining importance, while simpler tasks like data entry and IT support technicians are declining. In the business and management sector, sustainability managers, innovation managers, process automation experts, and sales and marketing experts are increasingly in demand, while administrative and secretarial staff, as well as accounting and payroll staff, are losing relevance. In the green economy, renewable energy engineers, electric vehicle specialists, and environmental engineers are on the rise, while jobs in the fossil fuel industry are shrinking. In the healthcare and education sectors, nurses, doctors, teachers, and social work counselors are becoming more important, although no professions are losing relevance. In the office and administration sector, bank employees, postal workers, cashiers, graphic designers, and legal assistants are particularly affected by the decline in demand, while in the skilled trades and manufacturing, the absolute numbers of agricultural workers, delivery drivers, and construction workers are growing, while assembly and factory workers are less in demand due to automation.

What overarching trends, such as the green transformation, also influence the creation and decline of jobs?

The dynamics of the labor market are not solely determined by automation. A number of macro trends interact with each other and shape the professional landscape of the future.

The green transformation, meaning investments in climate protection and adaptation to climate change, is considered one of the biggest net job creators. This trend is driving demand for renewable energy and environmental engineers, as well as sustainability specialists.

Economic conditions have an equally strong, but often opposing, effect. Slow economic growth and rising living costs are considered net job killers and can partially negate the gains created by technology and the green transformation.

Technology adoption itself is a double-edged sword. The expansion of digital access is expected to create the most jobs (19 million) by 2030, but also displace many (9 million). AI and big data follow as the second biggest driver, creating 11 million jobs and displacing 9 million.

Demographic shifts also play a crucial role. An aging population in high-income countries drives demand in the health and care sectors. At the same time, a growing working-age population in low-income countries leads to an increased need for labor in the education sector.

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The skills gap: Which skills will be in demand in the future?

What is meant by the “skills gap” and how big is this challenge?

The "skills gap" refers to the discrepancy between the skills employers require for their open positions and the actual qualifications of the available workforce. This gap is one of the central challenges of the current labor market transformation.

The scale of this challenge is enormous. As early as 2018, the WEF report predicted that by 2022, 54% of all workers would require significant retraining and upskilling. Subsequent reports confirm and intensify this assessment: The "Future of Jobs Report 2025" states that the core skills of 44% of workers will change in the next five years, and by 2030, almost 40% of the skills required for a job will be obsolete.

This statistical reality is reflected in the perceptions of business leaders. In the US, 70% of executives report that their organization has a critical skills gap that negatively impacts innovation and growth. Nearly 40% of these executives believe that this gap is worsening.

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What specific technical and digital skills are most urgently needed?

On the side of technical skills, also known as "hard skills," there is a clear hierarchy of demand. At the forefront are skills directly linked to the driving technologies of the Fourth Industrial Revolution.

AI and Big Data consistently rank among the most sought-after skills. The ability to handle large datasets and to use or develop AI systems is considered crucial. Closely related to this are other core competencies of digitalization: technological literacy, network and cybersecurity, software and application development, data analysis, and cloud computing are also in extremely high demand.

Interestingly, project management is also frequently cited as one of the most important technical skills. This underscores the need to combine technical implementation expertise with strategic business planning and to successfully manage complex digitization projects.

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

In an era where machines are increasingly taking over technical tasks, a paradox arises: While technical skills are the fastest-growing, cognitive and socio-emotional competencies are often considered most important by employers. This can be explained by the economic logic of scarcity and utility. Because AI makes routine tasks—whether technical or cognitive—available in abundance and at low cost, the skills that serve solely to perform these tasks lose value.

At the same time, tasks requiring novel problem-solving, strategic thinking, ethical judgment, and complex interpersonal interactions remain difficult to automate. As machines take over the "what" and "how" of many activities, the human role shifts to the "why" and "what next." This requires the ability to define problems, creatively interpret AI results, persuade stakeholders, and lead complex human teams. It is precisely for these so-called "human" skills that these skills are essential.

This creates an "automation premium" for skills that cannot be automated. The economic value and demand for these uniquely human competencies increase disproportionately. The most important of these skills are:

• Analytical and creative thinking: These consistently rank at the top of the skills most sought after by employers.
• Adaptability: Resilience, flexibility and agility are of utmost importance, as employees must be able to adapt to a constantly changing environment.
• Leadership and social skills: Leadership skills, social influence, emotional intelligence, curiosity and lifelong learning are also crucial, as AI can hardly replicate these abilities.

The skills gap is therefore not just a lack of technical skills. It's a division in the skills market: the value of routine skills is plummeting, while the value of non-routine, deeply human skills is skyrocketing. The most effective talent development strategies will therefore not only teach programming, but also combine it with training in critical thinking and creativity.

Future-proof in your job: The balance between 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 ranks them according to their perceived importance by employers.

Being future-proof in your job means finding the right balance between soft skills and technical know-how. First and foremost are human skills such as analytical and creative thinking. These are closely followed by technical knowledge in the areas of artificial intelligence, big data, and fundamental technological competencies. Resilience, flexibility, and agility are also important as further human skills. On the technical side, networks, cybersecurity, and data analysis are becoming increasingly important. Curiosity, lifelong learning, leadership, and social influence are also crucial human skills. This is complemented by technical expertise in software and application development as well as project management.

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

What strategies do companies pursue to prepare their workforce for the future?

In light of the widening skills gap, companies are developing proactive strategies to prepare their workforce for the future. These strategies go beyond simple training measures and aim for a fundamental realignment of personnel development.

A key approach is strategic workforce planning. Companies analyze their current skills in comparison to future requirements and develop targeted retraining (reskilling) and upskilling programs. The goal is to build a "sustainable skills architecture" that makes the workforce resilient to future shocks.

The strategic focus is shifting from simply replacing workers with technology to augmentation, i.e., the targeted strengthening of human capabilities through technological tools. This manifests itself in the concept of human-machine collaboration, which combines the strengths of both sides.

Investments in professional development 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 competencies. At the same time, companies promote internal mobility by creating clear career paths to retain and develop talent within the organization.

Innovative companies are also integrating learning directly into everyday work. Proven practices include training managers to become coaches who guide their employees, and using peer-to-peer learning models where experienced colleagues share their knowledge.

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

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

Amazon has allocated a $1.2 billion budget for its "Upskilling 2025" initiative to retrain hundreds of thousands of employees. Key programs include the "Amazon Technical Academy," which trains employees without a technical background to become software developers; the "Machine Learning University" for advanced learners; and the "Career Choice" program, which covers tuition fees. The results are measurable: 75% of participants experienced career advancement, and their salaries increased by an average of 8.6%.

AT&T invested approximately $1 billion in its "Future Ready" program to retrain its workforce. The company found that half of its employees lacked the skills needed for the future and consciously opted for an internal skills development initiative instead of mass layoffs and new hires. The program focuses on areas such as data science and cybersecurity and utilizes online platforms and personalized career portals to offer employees flexible learning opportunities.

Siemens is pursuing an approach where digital transformation and employee training go hand in hand. The company is leveraging cloud technologies like Amazon Web Services (AWS) for comprehensive modernization, from data infrastructure to the use of generative AI. A prime example is the Siemens electronics plant in Erlangen. There, an Industry 4.0 solution was implemented that reduced machine learning usage time by 80%. Simultaneously, the manufacturing workforce received on-site training in real-time data analytics and the Internet of Things (IoT). This demonstrates how upskilling can be directly integrated into operational transformation.

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

Besides entrepreneurial initiatives, governmental frameworks also play a crucial role in managing structural change. The German Qualification Opportunities Act is an example of proactive government policy.

The law aims to support companies in providing further training for their employees, particularly in professions affected by technological or structural changes. It offers significant financial incentives: the Federal Employment Agency can cover up to 100% of the training costs and additionally subsidize up to 75% of the employee's wages during the training program. The level of funding depends on the company size, with smaller companies receiving greater support.

The aim of the law is to strengthen the competitiveness of the German economy, to secure the jobs of 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 a universal basic income (UBI) be part of the solution?

The profound changes in the labor market also raise questions about more fundamental redesigns of work and social security. Two models that are being intensively discussed are the four-day week and the unconditional basic income (UBI). 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 existing work by passing on productivity gains to employees in the form of additional time. Large international pilot studies involving 141 companies and over 2,800 employees have shown impressive results. Companies reported stable or even increased revenues (in some cases by up to 35%), while employees reported a dramatic reduction in burnout (up to 70%), stress, and anxiety, as well as improved mental health and sleep quality. Employee turnover decreased, and over 90% of participating companies retained the model after the trial period. The success is based on the "100-80-100" model (100% pay, 80% time, 100% productivity), achieved by redesigning workflows and reducing unnecessary meetings.

In contrast, a universal basic income (UBI) aims to create social security outside of paid employment by decoupling a basic income from employment. It primarily addresses the problem of those who could be displaced from the labor market or who are in precarious employment. The results from pilot projects worldwide are mixed and highly context-dependent. Positive effects such as reduced food insecurity, improved health, higher school attendance rates, and an increase in entrepreneurship have been observed in Kenya and India. The pilot project in Stockton, California, showed positive psychological effects without negative impacts on work motivation. Other studies, such as the early experiments in the US 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 major limitation of many of these studies is their limited duration and small scale, which makes it difficult to extrapolate them 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 the standard for full-time employment to improve the quality of life for workers. At the same time, a basic income could serve as a social safety net for those in transition, those working in the gig economy, or those whose jobs have been completely replaced by automation. This would enable a more resilient and equitable societal response to change than either of these measures alone.

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Socioeconomic consequences: inequality, regional disparities and job quality

Will artificial intelligence exacerbate income and wealth inequality, or can it reduce it?

The question of how AI affects inequality is one of the most pressing socio-economic debates of our time, and research on this topic provides nuanced and sometimes contradictory results.

On the one hand, there are arguments that AI could reduce wage inequality. Unlike previous waves of automation, which primarily affected low-skilled routine work, the current wave of AI is strongly targeting highly paid white-collar jobs. Task-level studies show that often the lower-skilled employees within a profession (e.g., in customer service or software development) experience the greatest productivity gains from AI tools. This could potentially strengthen middle-class wages and narrow the gender pay gap.

On the other hand, the arguments for an increase in overall inequality outweigh the arguments for it. First, the productivity gains of AI could primarily benefit highly paid knowledge workers who have access to and the skills to use these tools, while low-wage earners in service and craft jobs are left behind. Second, AI-driven automation tends to shift the share of income from labor to capital. Since less human labor is needed for the same output, the owners of capital (e.g., shareholders) benefit disproportionately, exacerbating inequality in favor of the already wealthy.

A working paper from the International Monetary Fund (IMF) brings these two aspects together and makes a crucial distinction: AI might slightly reduce wage inequality (by displacing high earners), but it could drastically increase wealth inequality. The underlying mechanism is that the same highly paid workers experiencing wage pressure are also the largest owners of capital. They therefore benefit most from the increased returns on capital generated by automation. Furthermore, high wage premiums for individuals with in-demand AI skills—a PwC study found a premium of 56%—widen the gap between those with and without these skills.

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

Technological transformation also has a strong geographical dimension and threatens to exacerbate existing regional inequalities.

Growth and new jobs are increasingly concentrated in urban centers and capital cities. These regions have a higher density of knowledge-intensive and teleworkable jobs. In the EU, capital city regions have recorded the strongest employment growth. In the US, McKinsey has already predicted that urban areas would experience net job growth, while rural districts could face decades of job losses.

This trend is creating a self-reinforcing spiral: cities, with their dynamic labor markets and excellent infrastructure, attract employers, skilled workers, and investment, while rural areas struggle with job losses and weaker infrastructure. Regional disparities in the EU have increased since the Great Recession, a trend that could be exacerbated by the pandemic and advancing automation, as poorer regions often have a lower rate of remotely workable jobs. Tech hubs will secure their economic power less through job growth and more through productivity gains, further concentrating economic power.

Does automation improve the quality of work by eliminating monotonous tasks, or does it lead to more monitoring and stress?

The impact of AI on the daily work experience is ambivalent and depends heavily on the type of implementation.

From a positive perspective, AI can significantly improve the quality of work. By automating monotonous and repetitive tasks, employees can focus on more creative, strategic, and engaging activities. In some sectors, employees using AI report higher job satisfaction and greater enjoyment of their work. Furthermore, AI can improve workplace safety, particularly in physically demanding jobs.

The negative perspective, however, emphasizes the risks of alienation and increased control. AI enables a new level of employee monitoring, which can lead to increased work intensity, more stress, and a loss of autonomy. The pressure to be more productive in a compressed or AI-driven work environment can lead to burnout if not carefully managed. Consequently, employees also fear job loss, a loss of bargaining power regarding wages, and increased management control.

Historical context and outlook: The AI ​​revolution in comparison

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

To understand today's transformation, it's helpful to look at history. The AI ​​revolution exhibits both parallels and fundamental differences to the Industrial Revolution.

Among the parallels is that both revolutions are characterized by technological upheavals that reshape labor markets, displacing old professions and creating new ones. Both led to significant social disruptions, urbanization (or its digital equivalent), and intense debates about inequality and the distribution of productivity gains.

However, the differences are more significant:

• Muscle power vs. mental power: The Industrial Revolution primarily automated and expanded human muscle power (physical labor). The AI ​​revolution, on the other hand, automates and expands human cognition (thinking). This is a qualitative leap, not just a gradual change.
• Speed ​​and scale: The AI ​​revolution is happening far faster, compressing changes that previously took centuries into just a few decades. Societal and regulatory adaptation is struggling to keep pace.
• The nature of new jobs: During the Industrial Revolution, displaced farm laborers could move into factories, whose work was still based on human labor. Today, it is less clear whether displaced cognitive workers can easily transition into the new AI-related roles that often require a much higher level of abstract skills.
• The ultimate goal of technology: The machines of the Industrial Revolution were tools operated by humans. However, the stated goal of some leading AI developers is to create systems capable of performing all economically valuable tasks. This carries the risk of rendering human labor obsolete 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 within an industry do not automatically lead to higher wages for workers, especially when their bargaining power is weak. The real wages of many workers stagnated for decades, even as the economy grew.

Work quality and autonomy are crucial. The transition from artisanal to factory work meant a drastic deterioration in working and living conditions for many and was a major cause of social unrest. This is an important lesson for the current implementation of AI-driven management and monitoring systems.

Societal adaptation is a slow and painful process. Society eventually adapted to the Industrial Revolution—with new labor laws, education systems, and social programs—but this process was lengthy, conflict-ridden, and marked by suffering.

One of the most important lessons, however, is that the direction of technology is not a matter of fate, but of choice. Deliberate decisions can be made to develop technologies that augment human capabilities and create new, meaningful tasks, rather than simply automating and displacing existing jobs.

What key areas of action emerge for politics, businesses and every individual in order to successfully shape the change?

The analysis of the transformation of the labor market reveals clear areas of action for all stakeholders involved.

For politicians:

• Investments in education: Governments must invest massively in education and lifelong learning, integrating both AI competence and “human” skills such as critical thinking.
• Promoting transformation: You should create an environment that supports the transformation of the workforce, for example through policy instruments such as the German Qualification Opportunities Act.
• Strengthening social security: Social security systems must be strengthened and new models such as a basic income should be considered to support displaced workers and combat inequality.
• Regulation: Smart regulation is needed to ensure that AI is developed and used ethically, workers' rights are protected, and excessive surveillance is prevented.

For businesses:

• Active role in qualification: Companies must take an active role in the retraining and further education of their own workforce and focus on augmenting human skills rather than replacing them.
• Competency-based approach: You should pursue a competency-based approach to talent management that promotes internal career paths and mobility.
• Culture of learning: Creating a culture of continuous learning and psychological safety is crucial to making it easier for employees to adapt to change.

For each individual:

• Proactive lifelong learning: Each individual must take a proactive approach to their own lifelong learning and adopt an agile mindset.
• Building a skills portfolio: The best protection against automation is to build a portfolio that includes both technical skills and uniquely human competencies such as creativity, critical thinking and adaptability.

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