AI is changing the job market: Why 800,000 job losses are only half the story

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Published on: November 11, 2026 / Updated on: November 11, 2026 – Author: Konrad Wolfenstein

AI is changing the job market: Why 800,000 job losses are only half the story – Creative image on the topic, with AI: Xpert.Digital

More productivity, fewer jobs? What AI means for employees in Germany

AI and the German labor market: The great job shift begins

Why especially young professionals could come under pressure

Artificial intelligence is unlikely to transform the German labor market through a sudden collapse in employment, but rather through a profound restructuring of jobs, qualifications, and career opportunities. According to a long-term scenario, around 800,000 jobs could disappear within 15 years – while roughly the same number of new positions are created. However, this near-balanced outcome is no cause for complacency: the affected jobs and people cannot simply be offset against each other. New positions may require different skills, emerge in different locations, or become available later. Those just starting their careers could face particular challenges as companies change their processes and automate standardized tasks. Therefore, the crucial question is not only how many jobs ultimately remain, but also who manages the transition, how productivity gains are distributed, and whether technological progress actually translates into viable career opportunities.

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The job balance may be positive – and yet hundreds of thousands still lose out

Artificial intelligence is likely to transform the German labor market less through a sudden collapse in employment than through a profound restructuring of jobs, qualifications, and career opportunities. Around 800,000 jobs lost within a 15-year period could be offset by a similar number of new ones. However, this largely balanced outcome is no cause for complacency. It would merely mean that job losses and new employment roughly cancel each other out in the overall economy, but not that the affected individuals would be able to transition into new roles without income losses, relocation, or lengthy transition periods.

The economically crucial question is therefore not simply how many jobs will ultimately remain. More important is which jobs will disappear, where additional demand will arise, how quickly companies can reorganize, and who can acquire the necessary skills in time. For Germany, AI offers the opportunity to generate more added value with increasingly scarce human resources. At the same time, this creates the risk of a labor market where existing employment relationships remain relatively stable, while young professionals, certain service sector jobs, and less adaptable companies come under increasing pressure.

What's behind the 800,000?

The figure of 800,000 job losses should not be interpreted as a prediction of a corresponding increase in unemployment. It describes a potential structural transformation over a long period. In the underlying scenario calculation, after 15 years, approximately 790,000 jobs will be lost compared to a reference path, offset by roughly the same number of new jobs. Rounded, this results in the commonly used figure of 800,000 jobs in each case. Taken together, this would mean that around 1.6 million jobs would be affected by additional structural shifts.

The benchmark is crucial. The reference path does not describe a frozen economy without digitalization, but rather a development in which existing economic, technological, and demographic trends are already being extrapolated. The AI scenario examines how increased use of the technology could alter this development path. Therefore, the identified differences should not be equated with either all future job losses or the overall change in employment.

Nor does this mean that 800,000 jobs are disappearing every year. The magnitude emerges over the period under consideration and reflects the differences between two possible economic structures. A uniform distribution across individual years would be misleading because investments, organizational adjustments, and new business models take effect at different rates. The figure is useful as a guide to the scale of the transformation, not as a precise roadmap for layoffs.

A balanced budget protects no one

There is a significant difference between a balanced overall employment situation and individual employment opportunities. A job lost in standardized document processing cannot automatically be replaced by an additional position in IT security or technical systems integration. This may require different expertise, additional professional experience, or a different workplace. Even if both positions are statistically equivalent, they are not interchangeable from the perspective of the individual affected.

This problem is often described as a mismatch between labor supply and demand. People are looking for work, while companies are simultaneously complaining about unfilled positions. AI could exacerbate this situation if the demand for certain tasks changes faster than employees can adapt their skills. A near-zero employment balance can therefore lead to longer periods of unemployment for certain groups, increased training requirements, and widening income inequality.

The temporal dimension is also crucial. Additional jobs don't necessarily arise precisely when other positions are eliminated. A company might downsize its administration while new services only generate sufficient revenue later. Therefore, the long-term end state is not the only factor in economic policy assessment. Equally important are the transitional costs along the way: lost income, unused skills, the financial burden on social security, and the risk of people becoming permanently marginalized.

Tasks disappear in favor of entire professions

AI initially changes tasks, not necessarily entire job profiles. A job typically consists of a range of different tasks: gathering information, checking data, preparing decisions, communicating with customers, taking responsibility for results, and solving unexpected problems. If a technology accelerates some of these tasks, it doesn't automatically mean that the entire job becomes economically obsolete.

An analytically oriented job might, for example, require less time to compile a report, while the review of unusual results, coordination with specialist departments, and responsibility for the decision remain. Therefore, the potential for technical automation should not be equated with a concrete risk of job loss. Between the two lie operational costs, quality requirements, legal responsibilities, and the question of whether the time saved can be used productively elsewhere.

Conversely, it would be overly optimistic to conclude that this task perspective guarantees job security. If enough work steps are eliminated and companies reorganize their processes, the same workload can be managed with fewer employees. The transition may then be gradual: a departing employee is not replaced, an additional position is not approved, or a previously independent function is merged with another. The labor market thus changes even without publicly visible waves of layoffs.

Why efficiency knows two directions

Whether higher productivity reduces employment or creates additional jobs depends largely on demand. If a company can provide the same output with fewer employees and sales volume remains unchanged, the need for labor initially decreases. However, if services become cheaper, better, or accessible to more customers, demand can increase and offset some of the initial savings.

For example, a service provider could use AI to more economically support small businesses because the costs of simple analyses would decrease. This could lead to additional customer relationships and new consulting services. However, in a market with largely saturated demand, the same technological advancement could primarily trigger price pressure and staff reductions. Technology alone does not determine which development prevails.

The use of profits also plays a role. Higher profits can finance investments, higher wages can support consumption, and lower prices can improve purchasing power. However, if savings are largely absorbed without generating additional domestic demand or investment, the job creation effect will be weaker. A sound economic analysis must therefore go beyond individual automated processes and consider the repercussions for markets, incomes, and business decisions.

The first pressure hits services

Business-related services are particularly in focus because many of their services involve processing digitally available information. This includes standardized analyses, preliminary research, documentation, and certain administrative tasks. Generative AI can support or partially automate these processes. This not only changes staffing requirements but also the economic basis of existing service offerings.

In the long-term AI scenario, employment in business service providers is projected to be around 120,000 people below the reference trajectory after 15 years. In contrast, IT and information service providers are projected to see approximately 110,000 additional jobs. These differences demonstrate that the digitalization of an industry does not automatically translate into job growth or decline. The crucial factor is which services are replaced, supported, or additionally demanded.

This presents a fundamental challenge for consulting and knowledge service providers. If simply producing a standard report becomes cheaper, it loses its value as a standalone differentiator. The selection of relevant questions, robust industry knowledge, the validation of assumptions, and the implementation of concrete changes become more important. A potential structural shift thus leads away from merely selling information processing and towards demonstrably improving decisions and business results. However, the various groups of business services are not synonymous: a specific industry metric cannot be applied without modification to all consulting or knowledge professions.

Banks need more than analyses

Analytical activities at banks and insurance companies are particularly vulnerable because they often rely on structured data, extensive documentation, and recurring evaluation steps. Potential applications range from summarizing submitted documents to preparing standardized risk assessments. While technical support can reduce processing times, it does not automatically eliminate the need for expert oversight.

Especially in financial services, erroneous results can be costly. A convincingly formulated assessment is not necessarily an accurate one. If input data is incomplete, unusual circumstances are misclassified, or a model contains systematic biases, an apparent increase in efficiency can create additional risks. Automation is therefore only economically viable if the costs of monitoring and potential errors in judgment are taken into account.

Staffing needs are therefore likely to become more differentiated. Standardized preliminary work may become less important, while demanding audits, handling exceptions, and monitoring deployed systems could become more crucial. Whether this will result in new positions of the same magnitude remains to be seen. Not every additional control task requires a new full-time position, and not every existing specialist can easily transition to a specialized control function.

Policies do not offer complete protection

Equating higher education with lower automatability is an oversimplification when it comes to generative AI. This technology is now reaching tasks that were long considered typical knowledge work: formulating texts, compiling information, creating program code, or pre-structuring complex issues. Therefore, a university degree does not protect every aspect of a job from technological support or replacement.

However, this does not mean that academic education loses its economic value. Specialized knowledge helps to identify errors, ask the right questions, and place results in a broader context. What would be problematic is a qualification whose practical benefit consists almost exclusively of reliably executing standardized work steps. Therefore, an important dividing line exists between reproducible processing and independent, responsible problem-solving.

Even employees without a university degree are by no means automatically protected. While long-term scenario analysis points to more significant changes in highly skilled occupations, current analyses simultaneously reveal potential negative correlations between AI exposure and the employment of young people in non-academic professions. These two findings can coexist because they concern different time periods, job structures, and research approaches. Therefore, a sound assessment avoids making sweeping generalizations about winners and losers based solely on educational attainment.

Craftsmanship remains robust, not untouched

Occupations with a high proportion of practical and manual tasks are considered relatively robust against current generative AI applications. On-site repairs, workpiece adjustments, or working in changing spatial environments cannot be accomplished solely through a language model. Physical execution, situational judgment, and immediate responsibility limit direct replaceability in these cases.

This relative robustness, however, does not mean that traditional craftsmanship will remain unaffected by AI. Offers could be prepared more quickly, technical documents more easily evaluated, or customer appointments planned more efficiently. The potential productivity gains then lie primarily in supporting processes. A business could handle more orders without fundamentally altering the core craftsmanship of its services.

It is also important to distinguish this from robotics and other forms of automation. Low exposure to generative AI is not a general guarantee of technological irreplaceability. Nor does it say anything about the cyclical nature of a profession. A practically oriented job can be robust against language models and yet still be vulnerable to declining demand, weak investment, or other technological changes. For economic evaluation, these risks must be considered separately.

The entry into the workforce is becoming a bottleneck

The development of young employees requires particularly close monitoring. Many entry-level tasks consist of preparatory work that is technically necessary but still relatively standardized. This can include initial research, simple analyses, documentation, and the preparation of documents. Such tasks are particularly well-suited for AI support. Companies could therefore consider hiring fewer junior staff and instead equipping experienced employees with more powerful tools.

An early indicator for Germany shows that full-time employment among 20- to 24-year-olds in the most AI-exposed occupations has declined from around 279,000 to 239,000 since 2022. This corresponds to a decrease of approximately 14 percent. Across all age groups, however, employment in these particularly exposed occupations has developed much more favorably. This difference makes the entry into the workforce an important point of observation, but it does not yet prove that AI is responsible for the entire decline.

Furthermore, this age group is not identical to all new entrants to the workforce. Some people begin their careers earlier, others only after a longer period of study. Nevertheless, a plausible risk emerges: A job market can appear relatively stable for already established employees while simultaneously narrowing its access. The lack of an entry-level position often remains less visible to the public than a dismissal, but can have significant consequences for long-term career development.

Fewer young talents will later cost us more expertise

When companies automate simple entry-level tasks, they must rethink how future professionals should gain experience. Professional judgment doesn't develop solely through theoretical knowledge. It also evolves through repeatedly working with real-world cases, receiving feedback, and reflecting on mistakes. If this learning phase is shortened or eliminated entirely, a gap can later emerge between formal qualifications and practical competence.

From the perspective of an individual company, it may be attractive in the short term to primarily employ experienced professionals and reduce the number of junior positions. However, if many companies act similarly, the collective foundation for the future skilled workforce could be weakened. Recruiting already experienced employees does not solve this macroeconomic problem, but merely redistributes a limited pool of talent.

A sensible business response would therefore not be to artificially maintain every existing routine task. Instead, learning pathways would need to be consciously redesigned. Trainees could review AI results, compare different solutions, and gradually assume responsibility for real-world decisions. This would shift the focus of training more towards understanding and critical thinking. The time saved would then not only be a tool for reducing personnel costs but could also be used specifically for faster and more thorough skills development.

Productivity can also empower beginners

The impact of AI on new professionals is not entirely negative. A suitable assistant can make knowledge accessible, provide guidance on phrasing, and support the solution of less familiar problems. This allows inexperienced employees to become productive more quickly. Whether companies use this as an opportunity for additional hiring or for reducing staffing needs is a separate business decision.

A comprehensive study in customer service involving 5,172 employees revealed that AI support resulted in an average of approximately 15 percent more successfully resolved requests per working hour. Less experienced and previously lower-performing employees benefited particularly significantly. This finding demonstrates that AI can reduce existing performance disparities within a job. However, it does not allow for general conclusions about how employment or wages will change across the entire economy.

This distinction is crucial. A tool can increase the productivity of a young employee while simultaneously reducing the overall need for young employees within a company. Similarly, the increased benefit of an additional employee can make new hires economically viable. Whether the supporting or crowding effect prevails depends on demand, organization, and corporate strategy. Technological capabilities do not entirely determine the outcome.

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Starting a career under pressure: The challenge for young people

The employment data require a sober interpretation

So far, no widespread decline in employment in particularly AI-exposed occupations is apparent in Germany. At the beginning of 2026, full-time employment in the most exposed occupations was 1.8 percent higher than in 2022. In contrast, it was 6.4 percent lower in the least exposed occupations. These figures contradict the simplistic notion that a high level of technical impact from AI must necessarily lead to a general reduction in jobs.

However, they also do not prove that AI has already increased employment to this extent. The development of the occupational groups studied differed in some cases even before the widespread availability of generative AI. Digitization, industry shifts, and other structural factors can influence the results. Anyone who interprets the entire difference as an AI effect is confusing an observed correlation with a proven cause.

An additional limitation arises from the segment of employment considered. Full-time employment does not represent all employment nor automatically the total volume of work. Changes in part-time work, self-employment, or working hours can provide additional information. For a reliable diagnosis, therefore, employment levels, new hires, working hours, and income should be considered together. A single indicator can raise a warning signal, but it cannot provide a complete picture of the economic transformation.

Economic trends and AI overlap

The current economic downturn makes it difficult to attribute observed changes to specific factors. Companies are often more hesitant to hire when sales prospects are uncertain. This can particularly affect young professionals, as they do not yet have an existing job and their capabilities are harder for potential employers to assess. Therefore, a more difficult entry into the workforce is not automatically evidence of technological displacement.

Another analysis of Germany focuses on 20- to 29-year-olds, a broader age group. In this group, full-time employment fell by 3.8 percent between 2022 and 2025 in occupations with greater exposure to AI and by 2.6 percent in less exposed occupations. After additionally considering economic vulnerability and suitability for working from home, the negative correlation with AI exposure for this age group remained statistically inconclusive. The correlation with economic weakness was significantly stronger.

These results do not necessarily contradict the more pronounced decline among 20- to 24-year-olds. The age definition, time period, and occupational classification differ. Taken together, the findings warrant a cautious interpretation: there are serious indications of changing entry opportunities, but no sufficient reason to attribute all the problems faced by young workers to AI. Economic policy must therefore address technological adaptation and general weakness in investment and demand simultaneously.

Distribution is not yet transformation

Generative AI has spread rapidly in German companies. Its usage rate rose from approximately 5 percent in 2023 to almost 25 percent in 2025. Around 40 percent of all employees subject to social security contributions worked in these user companies. However, this does not mean that all of these people were working with AI themselves. The technology may remain limited to specific departments or tasks within a company.

Significant differences also existed between company sizes. In 2025, around 48 percent of companies with at least 200 employees used generative AI. For companies with up to nine employees, the figure was approximately 21 percent. The technology was particularly widespread in information and communication technology (59 percent), finance and insurance (50 percent), and business-related services (37 percent).

The economic significance of these figures lies not only in the speed of adoption. An occasional text summary changes a business differently than the integration of AI into continuous workflows. Approximately 90 percent of user companies relied on publicly available solutions; only a smaller proportion used more customized or in-house developed models. High adoption rates can therefore coincide with limited organizational penetration. For productivity and employment, what matters most is how intensively and for what purpose the technology is used.

The benefit is determined during operation

Investments in AI make economic sense if the added benefits outweigh the total costs. These costs include not only licensing fees, but also resources for data preparation, technical interfaces, training, security measures, and results monitoring. A cheap tool can prove expensive if its results require frequent and costly corrections.

The central business question is therefore not whether a single task is completed faster. The crucial question is whether the entire process becomes more economical while maintaining or improving quality. If an initial evaluation saves time, but subsequent coordination and checks are necessary, the actual gain may remain small. Conversely, a seemingly minor improvement can have a significant impact if it eliminates a bottleneck in a frequently repeated process.

A realistic introduction therefore requires verifiable criteria. These include the processing time until a usable result is achieved, error and rework rates, and the actual capacity freed up. The number of texts, presentations, or program drafts produced is not, in itself, a measure of productivity. Economic progress only occurs when results become more usable, customers receive added value, or scarce resources are used more effectively. AI as a symbol of modernization is not enough to achieve this.

Why large growth figures are conditionally valid

The long-term AI scenario links structural transformation with significant macroeconomic opportunities. Under the assumptions made, annual economic growth over 15 years could average 0.8 percentage points higher than in the reference path. Over the entire period, this would result in cumulative additional value creation of approximately €4.5 trillion. This figure represents a potential, not a guaranteed return.

Two common misconceptions should be avoided. An increase of 0.8 percentage points in annual growth is not the same as a one-time increase in economic output of 0.8 percent. Similarly, 4.5 trillion euros in additional value added over 15 years is neither an additional government budget nor a single annual sum of money. What is meant is the cumulative difference in economic output between two development paths over that period.

The underlying mechanism is understandable: higher labor productivity, reduced material input, and new sales opportunities. However, the actual magnitude of these effects depends on implementation. Results from particularly successful individual applications cannot be directly extrapolated to the entire economy. Furthermore, uncertainties arise regarding new business models, international competitive reactions, and the speed of dissemination. Therefore, high potential figures should be understood as justification for improved framework conditions, not as a substitute for a verifiable investment strategy.

New jobs don't create automatically

A long-term, near-balanced employment balance requires that, in addition to efficiency gains, further economic activity emerges. This includes the development and operation of technical infrastructure, new IT services, and business models that were previously not economically viable. This employment is not an automatic side effect of every hour saved. It requires investing companies, customers willing to pay, and the necessary skills.

The development over time is also not linear. Initially, additional personnel may be needed for data acquisition, technical implementation, and infrastructure. Later, productivity gains may reduce the demand for labor. New business areas could offset this effect in the longer term. If their development falls short of expectations, the employment balance will also change. Scenario analysis can illustrate this relationship, but it cannot guarantee that all necessary conditions will be met.

Furthermore, additional work does not necessarily have to be reflected entirely in new job titles. An existing technical service provider could expand its offerings, an industrial company could develop additional digital maintenance services, or a consulting firm could take on new implementation tasks. What matters is the additional economic demand, not the number of newly created job titles. For employment policy, this means that innovation and business start-ups are just as relevant as the further training of existing employees.

Demographics make efficiency particularly valuable

In an aging society, labor productivity is of particular importance. When personnel are harder to find for certain jobs, automating administrative tasks can help utilize existing skilled workers more effectively. A lower demand for labor in one sector therefore does not necessarily mean a deterioration of the overall labor market situation. It can also help to alleviate shortages.

However, this connection should not be overstretched. An office worker relieved of administrative tasks by AI does not automatically become a sought-after nursing professional, and additional technical capacity does not replace all practical work. Qualification requirements, personal preferences, and organizational limitations lie between these areas. Therefore, a demographically driven staff shortage and technological displacement can occur simultaneously.

The economically attractive prospect lies in shifting scarce working time from standardized tasks to those with greater human benefit. This could include improved customer service, faster problem-solving, or better technical preparation. Whether this succeeds depends on work organization. If only personnel costs are reduced without making good use of remaining capacity, some of the potential remains untapped. AI is therefore a possible component in addressing demographic challenges, but not a complete replacement for education, labor force participation, and appropriate immigration.

Further education requires concrete career goals

The demand for further training remains ineffective if it is unclear which skills will subsequently be needed economically. A general introductory course on using a chatbot can be helpful, but it does not replace the adaptation of professional skills. Employees must be able to recognize which of their tasks will change and what additional responsibilities they will be expected to assume in the future.

The operational situation reveals a significant implementation gap. In 2025, approximately 27 percent of companies using generative AI offered relevant training courses. A further 21 percent were planning to offer such courses. Measures already implemented and mere plans should not be equated. The implementation of internal regulations was also incomplete: around 21 percent of user companies had corresponding guidelines, and another 22 percent were planning to implement them.

Meaningful professional development therefore combines specialist knowledge, practical skills, and results evaluation. Employees should not only learn to formulate instructions, but also to recognize uncertainty, appropriately interpret data, and assess the limitations of a model. For companies, it is crucial to actually allocate learning time within the daily work routine. Shifting the responsibility entirely to the individual employee's willingness to pursue further education privately would disadvantage those with less time, financial resources, or suitable learning opportunities.

Middle classes between access and dependence

For smaller companies, readily available AI services initially lower the barriers to entry. Even without their own development department, they can receive support with communication, research, or analysis. This creates opportunities to partially compensate for organizational disadvantages compared to larger competitors. However, further integration into company-specific processes remains more challenging than using a single tool.

Large companies can spread fixed implementation costs across more use cases and employ more specialized teams. Smaller businesses, on the other hand, often have to decide with limited personnel resources which applications make sense and how they fit into existing systems. Standardized industry solutions or external support could help here. However, this only makes economic sense if the benefits, data access, ongoing costs, and switching options remain sufficiently transparent.

An additional risk lies in the dependence on individual providers. If core workflows are tightly tied to a single system, subsequent price changes, technical limitations, or difficulties in switching providers can diminish the initial benefits. Therefore, for an AI strategy geared towards medium-sized businesses, not only affordable entry-level licenses are relevant. Equally important are transferable data, transparent interfaces, and an organizational structure that doesn't completely outsource critical expertise.

Who receives the productivity gains

Increased economic output does not automatically answer the question of distribution. AI-related benefits can accrue to employees, customers, user companies, or technology providers. The extent to which each group benefits depends, among other things, on competition, bargaining power, and the scarcity of certain skills. Therefore, a positive overall economic development can be accompanied by significant disparities in income and job security.

Standardized services could come under price pressure, while robust expertise, customer access, or the ability to implement solutions practically would become more valuable. At the same time, AI can enable individual employees to take on more demanding tasks. Such changes do not necessarily have to lead to greater inequality. They can reduce performance differences within a single job while simultaneously creating new income gaps between different jobs.

For social security, it is also relevant whether higher productivity translates into rising wages, additional employment, or predominantly into other forms of income. A stable workforce alone does not guarantee a correspondingly growing contribution base. Conversely, a more productive economy with broad participation could facilitate the financing of social responsibilities. Distributional issues should therefore be treated neither as a secondary matter nor as already settled. They are a central component of shaping the economic aspects of technological change.

Infrastructure limits the digital promise

AI requires a physical and organizational foundation. Computing capacity, power supply, network connectivity, data management, and IT security are prerequisites for productive use. The assumption that these foundations will be available in time is a condition for favorable growth scenarios. If these conditions are not met, investments can become more expensive or have a delayed impact.

The construction of data centers can initially trigger additional economic activity and employment. However, this does not guarantee an unlimited and lasting job creation effect. Construction and technical operation have different personnel structures, and the broadest economic impact only arises from productive applications in other companies. A data center is therefore a necessary infrastructure for certain applications, but not yet proof of successfully realized productivity gains.

For industry and logistics, the challenge lies particularly in integrating digital tools with real-world processes. This could include improved technical documentation, support for maintenance decisions, or faster handling of operational deviations. Such applications must be compatible with data quality, security requirements, and existing systems. Therefore, a successful demonstration is not synonymous with an economically viable implementation in ongoing operations. Ultimately, the crucial factor is whether the technology operates reliably under real-world conditions and improves overall process performance.

Labor market policy must finance transitions

A sound economic policy approach should not fundamentally prevent productive automation. Attempting to preserve every existing job structure could hinder necessary efficiency gains and new business models. However, it would be equally inadequate to expect the market to solve all transition problems without support. Information gaps, financial risks, and a lack of learning opportunities can make meaningful changes difficult.

Labor market policy should therefore begin early. If tasks within a company change, timely training can potentially prevent future job loss. If a change is necessary, further training and counseling should be geared towards specific employment opportunities. Participation in a program alone is not proof of success; what is crucial afterward are viable employment prospects, suitable jobs, and minimal long-term income losses.

Access to the labor market deserves special attention. Apprenticeships, supported entry-level positions, and reliable learning pathways fulfill a function that extends beyond the short-term benefit of individual companies. An employment policy strategy should therefore not only address existing unemployment. It must also help prevent companies from saving on recruitment costs in the short term, thereby weakening the economy's own skilled workforce in the long run. Maintaining learning opportunities is something different from artificially preserving outdated routine work.

How the renovation can be measured

For the coming years, spectacular announcements of job cuts are not the only revealing indicators. Particularly important will be new hires based on age and profession, the number of available entry-level positions, internal transfers, and the transition from training to regular employment. Working hours and income should also be considered so that a stable workforce does not mask a change in the quality or scope of work.

At the company level, reliable key performance indicators (KPIs) are needed to demonstrate genuine performance improvement. Productive AI use should be reflected in usable results, reduced rework, improved quality, or added customer value. For a scientifically sound evaluation, data on actual implementation within companies should ideally be linked to employment data. Only then can it be better distinguished whether observed changes are related to AI or primarily have other economic causes.

The economic policy perspective should remain clear: not every disappearing routine task is a societal loss, and not every newly created job is proof of successful transformation. The benchmark is whether higher productivity translates into additional value creation, attainable career opportunities, and sustainable incomes. The crucial design challenge lies in linking technological progress and human development in such a way that a balanced job market translates into an economically secure future for as many people as possible.

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