Digitale Arbeitswelt – Chancen und Herausforderungen für Beschäftigte und Arbeitsmarkt
Der digitale Wandel der Arbeitswelt gilt als eine der großen Herausforderungen für Wirtschaft und Gesellschaft. Wie arbeiten wir in Zukunft? Welche Auswirkungen hat die Digitalisierung und die Nutzung Künstlicher Intelligenz auf Beschäftigung und Arbeitsmarkt? Welche Qualifikationen werden künftig benötigt? Wie verändern sich Tätigkeiten und Berufe? Welche arbeits- und sozialrechtlichen Konsequenzen ergeben sich daraus?
Dieses Themendossier dokumentiert Forschungsergebnisse zum Thema in den verschiedenen Wirtschaftsbereichen und Regionen.
Im Filter „Autorenschaft“ können Sie auf IAB-(Mit-)Autorenschaft eingrenzen.
- Gesamtbetrachtungen/Positionen
- Arbeitsformen, Arbeitszeit und Gesundheit
- Qualifikationsanforderungen und Berufe
- Arbeitsplatz- und Beschäftigungseffekte
- Wirtschaftsbereiche
- Arbeits- und sozialrechtliche Aspekte / digitale soziale Sicherung
- Deutschland
- Andere Länder/ internationaler Vergleich
- Besondere Personengruppen
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Literaturhinweis
Who uses Generative AI? Patterns and inequalities across the EU: Employment and labour markets (2026)
Zitatform
Adăscăliței, Dragoș (2026): Who uses Generative AI? Patterns and inequalities across the EU. Employment and labour markets. (Eurofound working paper), Dublin, 19 S.
Abstract
"This paper describes generative AI use patterns across EU27 Member States in 2025, analysing crossnational variation and socio-demographic inequalities based on Eurostat aggregate data. Overall use of generative AI reaches 32.7% at the EU27 level, ranging from 17.8% in Romania to 48.4% in Denmark. Country patterns do not follow clear geographic clustering, with high and low adopters distributed across all European regions. Private use systematically exceeds professional use, whilst educational use remains concentrated among young populations. Educational attainment emerges as a strong predictor of AI use, with high-educated individuals using generative AI at more than double the rate of low-educated individuals. Age is also a strong predictor, with 63.8% of those aged 16-24 having used generative AI in the past three months compared to just 6.5% of those aged 65 and above. The gender gaps in AI use are moderate but widen with education. In terms of broad occupational groups, the analysis demonstrates that uptake is heavily skewed towards ICT professions. Labour force status also matters decisively, with students (72.0%) far exceeding employed (36.4%), unemployed (28.3%), and retired/inactive populations (12.9%) in technology usage. These patterns reveal stratified diffusion of generative AI use with implications for labour market inequalities across the EU." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Automation, Learning, and Career Dynamics (2026)
Zitatform
Afrouzi, Hassan, Andres Blanco, Andrés Drenik & Erik Hurst (2026): Automation, Learning, and Career Dynamics. (NBER working paper / National Bureau of Economic Research 35157), Cambridge, Mass, 58 S.
Abstract
"We study how an automating technology affects career dynamics, human capital, and welfare in an economy where workers acquire skill through the tasks they perform. In a continuous-time general equilibrium model, learning-by-doing is determined jointly with the share of tasks automated, the frontier of tasks managers maintain, and the worker-to-manager career transition. Economies with high learning capacity admit pairs of stationary equilibria strictly ranked by the aggregate learning rate. Cheaper technology has opposite effects across the two: in the high-learning equilibrium, it raises welfare through the learning channel itself; in the low-learning equilibrium, it tips the economy into a human-capital trap. The planner's first-best combines a tax on automation profits with a subsidy on frontier-maintenance expenditures at a common rate." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Enhancing Worker Productivity Without Automating Tasks: A Different Approach to AI and the Task-Based Model (2026)
Zitatform
Agrawal, Ajay K., John McHale & Alexander Oettl (2026): Enhancing Worker Productivity Without Automating Tasks: A Different Approach to AI and the Task-Based Model. (NBER working paper / National Bureau of Economic Research 34781), Cambridge, Mass, 44 S.
Abstract
"The task-based approach has become the dominant framework for studying the labor-market effects of artificial intelligence (AI), typically emphasizing the replacement of human workers by machines. Motivated by growing empirical evidence that contemporary AI is more often used as a tool that augments workers, this paper develops two related task-based models in which AI enhances worker productivity without automating tasks. Abstracting from capital, we develop a pair of related task-based models that examine how technological progress in AI that provides new tools to augment workers affects aggregate productivity and wage inequality. Both models emphasize the role of human capital in intermediating the effects of AI-related technological shocks. In the first model, AI use requires specialized expertise, and technological progress expands the set of tasks for which such expertise is effective. We show that a larger supply of AI expertise amplifies the productivity gains from improvements in AI technology while attenuating its adverse effects on wage inequality. The second model focuses on non-AI skills, allowing AI tools to alter the set of tasks that workers can perform given their skills. In equilibrium, workers allocate across tasks in response to wages, generating an endogenous distribution of skills across the task space. A central result is that aggregate productivity and wage inequality depend on different global properties of this equilibrium distribution: productivity is particularly sensitive to thinly staffed tasks that create bottlenecks, while wage inequality is driven by the concentration of workers in a narrow set of tasks. As a result, improvements in AI tools can induce non-monotonic co-movement between productivity and inequality. By linking these mechanisms to multidimensional human capital---including AI expertise and higher-order non-AI skills---the paper highlights the role of education and training policies in shaping the economic consequences of AI-driven technological change." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Do AI Expectations Reduce Unemployment in the United States? Evidence from an AI Attention Index (2026)
Akyildirim, Erdinc; Gozgor, Giray; Bekci, Suzan;Zitatform
Akyildirim, Erdinc, Suzan Bekci & Giray Gozgor (2026): Do AI Expectations Reduce Unemployment in the United States? Evidence from an AI Attention Index. (CESifo working paper 12653), München, 13 S.
Abstract
"This paper constructs an AI Attention Index from LexisNexis news coverage and embeds it within an augmented Phillips curve framework. It then examines the relationship between AI attention and unemployment in the United States using monthly data from January 2000 to December 2025. We find that greater AI attention is associated with lower unemployment. Nonlinear estimates reveal a U-shaped relationship, indicating diminishing marginal effects within the observed data range. The relationship weakens after the onset of COVID-19, with both linear and nonlinear effects reduced. These findings indicate that labour market effects of AI-related expectations are sensitive to macroeconomic regime shifts." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
AI adoption, productivity and employment: Evidence from European firms (2026)
Aldasoro, Iñaki; Gambacorta, Leonardo; Pal, Rozalia; Wolski, Marcin; Weiß, Christoph; Revoltella, Debora;Zitatform
Aldasoro, Iñaki, Leonardo Gambacorta, Rozalia Pal, Debora Revoltella, Christoph Weiß & Marcin Wolski (2026): AI adoption, productivity and employment: Evidence from European firms. (Economics - working papers / European Investment Bank 2026/02), Luxembourg, 32 S. DOI:10.2867/1772538
Abstract
"This paper provides new evidence on how the adoption of artificial intelligence (AI) affects productivity and employment in Europe. Using matched EIBIS-ORBIS data on more than 12,000 non-financial firms in the European Union (EU) and United States (US), we instrument the adoption of AI by EU firms by assigning the adoption rates of US peers to isolate exogenous technological exposure. Our results show that AI adoption increases the level of labor productivity by 4%. Productivity gains are due to capital deepening, as we find no adverse effects on firm-level employment. This suggests that AI increases worker output rather than replacing labor in the short run, though longer-term effects remain uncertain. However, productivity benefits of AI adoption are unevenly distributed and concentrate in medium and large firms. Moreover, AI-adopting firms are more innovative and their workers earn higher wages. Our analysis also highlights the critical role of complementary investments in software and data or workforce training to fully unlock the productivity gains of AI adoption." (Author's abstract, IAB-Doku) ((en))
Ähnliche Treffer
auch erschienen als: BIS Working Papers, 1325 -
Literaturhinweis
The effect of AI on labour demand: A critical assessment of 'Power and Progress' by Acemoglu and Johnson (2026)
Aldred, Jonathan;Zitatform
Aldred, Jonathan (2026): The effect of AI on labour demand: A critical assessment of 'Power and Progress' by Acemoglu and Johnson. In: Structural Change and Economic Dynamics, Jg. 78, S. 188-196. DOI:10.1016/j.strueco.2026.03.008
Abstract
"Task-based models of production have led to a theoretical reappraisal of the effect of new technology on labour demand. This paper critically assesses the policy implications of this research agenda, with particular reference to Acemoglu and Johnson’s recent book, Power and Progress. While Acemoglu and Johnson take welcome steps away from previous orthodoxy, their analysis has several flaws which affect the policy lessons to be drawn, including: (i) the explanation for anti-labour bias is unclear; (ii) worker-friendly technologies are not clearly characterised in theory, and hard to identify in practice; (iii) macroeconomic policy orthodoxy is largely unquestioned. More generally, much of Power and Progress remains unhelpfully constrained by theoretical commitments to mainstream economics." (Author's abstract, IAB-Doku, © 2026 The Author. Published by Elsevier B.V.) ((en))
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Literaturhinweis
The end of work feels near. How do people perceive the impact of digital technologies and automation? (2026)
Zitatform
Arntz, Melanie, Sebastian Blesse & Philipp Doerrenberg (2026): The end of work feels near. How do people perceive the impact of digital technologies and automation? In: Labour Economics, Jg. 102, 2026-05-11. DOI:10.1016/j.labeco.2026.102897
Abstract
"Anxieties about technological change in the context of the labor market are a recurring historical phenomenon. Using customized survey data collected in 2019 in the US and Germany, prior to the recent wave of generative AI applications, we study how respondents perceive the impact of the digital (automation) technologies available at the time of the survey on the labor market. We document that a majority views digital technologies and automation as a major threat to overall employment and as a cause of rising inequality, while a quarter is concerned about their own labor market prospects. Providing scientific information on the likely labor market implications of digital technologies in a randomized experiment reduces these concerns. Yet, treatment responses depend on prior beliefs about the future of work, resulting in heterogeneous and opposing treatment effects on policy demand." (Author's abstract, IAB-Doku, © 2026 Elsevier) ((en))
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Literaturhinweis
Artificial intelligence exposure and occupational wages: Evidence from the United States (2026)
Zitatform
Atalay, Ozan (2026): Artificial intelligence exposure and occupational wages: Evidence from the United States. In: Journal of Economic Studies, S. 1-15. DOI:10.1108/jes-03-2026-0281
Abstract
"Purpose: This paper investigates the relationship between occupational exposure to artificial intelligence (AI) and wage structures in the United States. While much of the literature focuses on the displacement effects of AI, less attention has been given to its implications for wage outcomes across occupations. Design/methodology/approach: The study uses occupation-level data for 671 occupations, combining wage information with an AI exposure index that captures the extent to which occupations are affected by AI technologies. Cross-sectional regression models with robust standard errors are employed, controlling for employment size and occupational characteristics. Quantile regression is also used to examine variation across the wage distribution. Findings: The results indicate a positive and statistically significant association between AI exposure and wages. Occupations with higher exposure tend to have higher wage levels. This pattern is consistent across model specifications and across the wage distribution. The findings are broadly consistent when using an instrumental variable approach. The association is stronger in occupations with higher cognitive skill intensity. Research limitations/implications: This study is based on occupation-level data rather than individual-level observations, which limits the ability to capture within-occupation wage heterogeneity. In addition, the AI exposure index reflects potential exposure rather than actual adoption at the firm level. Future research could extend this analysis using firm-level or longitudinal data. Practical implications: The findings suggest that occupations with higher exposure to artificial intelligence tend to exhibit higher wages, highlighting the importance of skill upgrading and targeted workforce policies. Policymakers and organizations should focus on enhancing digital skills and supporting workforce transition to maximize the benefits of AI. Social implications: The results indicate that artificial intelligence may contribute to wage differences across occupations by enhancing productivity in certain roles. This highlights the need to ensure equal access to skills and training opportunities so that the benefits of AI are distributed more evenly across the labor market. Originality/value: This study contributes by providing occupation-level evidence on the relationship between AI exposure and wages, shifting attention from employment effects to wage structures. It also highlights that this relationship is partly explained by occupational skill composition, while a residual association remains." (Author's abstract, IAB-Doku, © Emerald Group) ((en))
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Literaturhinweis
Systematic literature review on the digital transformation of the personnel selection process (2026)
Zitatform
Baranyi, Virág (2026): Systematic literature review on the digital transformation of the personnel selection process. In: German Journal of Human Resource Management, Jg. 40, H. 2, S. 223-254. DOI:10.1177/23970022251363012
Abstract
"Digital Transformation technologies (DT technologies) are reshaping work processes, including personnel selection, an area traditionally viewed as inherently human-centric. While prior studies have examined various digital technologies in personnel selection, they have not provided sufficient evidence on the different levels of digitalization in selection processes and the factors influencing organizations’ adoption decisions. To address these gaps, this study systematically reviews 94 Scopus-indexed studies to analyze how DT technologies are applied across selection stages, categorizing practices into Manual, Digitalized, and Digitally Transformed approaches. By further distinguishing between Digital Technologies and AI Enhancements, this study offers a structured framework for understanding how organizations integrate digital technologies into selection and what drives or hinders their adoption. The findings highlight both the benefits (efficiency gains, potential bias reduction, improved candidate experience) and challenges (ethical concerns, algorithmic bias, technical and cultural barriers, and candidate perceptions) associated with these technologies, providing insights for both academic research and HR practice." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Automation Experiments and Inequality (2026)
Zitatform
Benzell, Seth Gordon & Kyle R. Myers (2026): Automation Experiments and Inequality. (NBER working paper / National Bureau of Economic Research 34668), Cambridge, Mass, 26 S., App. DOI:10.3386/w34668
Abstract
"Many experiments study the productivity effects of automation technologies such as generative algorithms. A key test in these experiments relates to inequality: does the technology increase output more for high- or low-skill workers? However, the theoretical content of this empirical test has been unclear. Here, we formalize a theory that describes the experimental effect of automation technologies on worker-level output and, therefore, inequality. Worker-level output depends on a task-level production function, and workers are heterogeneous in their task-level skills. Workers perform a task themselves or delegate it to the automation technology. The inequality effect of improved automation depends on the interaction of two factors: (i) the correlation in task-level skills across workers, and (ii) workers' skills relative to the technology's effective skill. In many cases we study, the inequality effect is non-monotonic --- as technologies improve, inequality decreases then increases. The model and descriptive statistics of skill correlations generally suggest that the diversity of automation technologies will play an important role in the evolution of inequality." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Mind the Gap: AI Adoption in Europe and the US: BPEA Conference Draft, March 26-27, 2026 (2026)
Zitatform
Bick, Alexander, Adam Blandin, David J. Deming, Nicola Fuchs-Schündeln & Jonas Jessen (2026): Mind the Gap: AI Adoption in Europe and the US. BPEA Conference Draft, March 26-27, 2026. In: Brookings Papers on Economic Activity, Jg. Conference Draft, H. Spring, S. 1-70.
Abstract
"This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI." (Author's abstract, IAB-Doku) ((en))
Ähnliche Treffer
- frühere (möglicherweise abweichende) Version erschienen u.d.T. "Mind the Gap: KI-Einführung in Europa und den USA" als: CEPR discussion paper / Centre for Economic Policy Research, 21337
- frühere (möglicherweise abweichende) Version erschienen u.d.T. "Mind the Gap: AI Adoption in Europe and the US" als: RF Berlin - CReAM Discussion Paper Series, 102/26
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Literaturhinweis
Mind the Gap: KI-Einführung in Europa und den USA (2026)
Zitatform
Bick, Alexander, Adam Blandin, David Deming, Nicola Fuchs-Schündeln & Jonas Jessen (2026): Mind the Gap: KI-Einführung in Europa und den USA. (CEPR discussion paper / Centre for Economic Policy Research 21337), London, 80 S.
Abstract
"This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We find no clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI." (Author's abstract, IAB-Doku) ((en))
Ähnliche Treffer
- spätere (möglicherweise abweichende) Version erschienen u.d.T. "Mind the Gap: AI Adoption in Europe and the US : BPEA Conference Draft, March 26-27, 2026" in: Brookings Papers on Economic Activity, Conference Draft (2026), 1-70
- auch erschienen u.d.T. "Mind the Gap: AI Adoption in Europe and the US" als: RF Berlin - CReAM Discussion Paper Series, 102/26
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Literaturhinweis
Mind the Gap: AI Adoption in Europe and the US (2026)
Zitatform
Bick, Alexander, Adam Blandin, David Deming, Nicola Fuchs-Schündeln & Jonas Jessen (2026): Mind the Gap: AI Adoption in Europe and the US. (RF Berlin - CReAM Discussion Paper Series 102/26), Berlin, 71 S.
Abstract
"This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI." (Author's abstract, IAB-Doku) ((en))
Ähnliche Treffer
- spätere (möglicherweise abweichende) Version erschienen u.d.T. "Mind the Gap: AI Adoption in Europe and the US : BPEA Conference Draft, March 26-27, 2026" in: Brookings Papers on Economic Activity, Conference Draft (2026), 1-70
- auch erschienen u.d.T. "Mind the Gap: KI-Einführung in Europa und den USA" als: CEPR discussion paper / Centre for Economic Policy Research, 21337
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Literaturhinweis
Explaining women's skepticism toward artificial intelligence: The role of risk orientation and risk exposure (2026)
Borwein, Sophie ; Bonikowski, Bart ; Ognyanova, Katherine; Alvarez, R. Michael; Loewen, Peter J.; Magistro, Beatrice ;Zitatform
Borwein, Sophie, Beatrice Magistro, R. Michael Alvarez, Bart Bonikowski & Peter J. Loewen (2026): Explaining women's skepticism toward artificial intelligence: The role of risk orientation and risk exposure. In: PNAS nexus, Jg. 5, H. 1. DOI:10.1093/pnasnexus/pgaf399
Abstract
"This article examines the gender gap in attitudes toward the adoption of AI in the workplace, with a focus on how gender differences in risk orientation and risk exposure drive skepticism toward AI’s economic benefits. Using original surveydata from ∼3,000 respondents across Canada and the United States, we find that women consistently perceive AI to be riskier than men. We identify two key drivers behind this gender gap: women’s higher general risk aversion and their greater exposure to AI-related risks. To establish a causal relationship between risk and AI attitudes, we show experimentally that as the probability of net positive employment effects decreases, women’s support for companies adopting AI falls more sharply than men’s. Finally, structural topic modeling of open-ended responses confirms that women express greater uncertainty about AI’s benefits and more frequently anticipate little to no benefits. Given AI’s potential to exacerbate existing gender inequalities, our study highlights the critical importance of incorporating women’s perspectives into AI policy-making. Policies that do not address gender-specific risks may not only reinforce existing inequalities in employment and income but could also generate political backlash against AI adoption." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Comment on “Artificial Intelligence and Technological Unemployment” by Wang and Wong (2026)
Braxton, J. Carter;Zitatform
Braxton, J. Carter (2026): Comment on “Artificial Intelligence and Technological Unemployment” by Wang and Wong. In: Journal of monetary economics, Jg. 159. DOI:10.1016/j.jmoneco.2026.103924
Abstract
"Since the launch of ChatGPT in November 2022 there has been a surge in the uptake of generative artificial intelligence (AI). The recent advances in AI have also been met with sweeping statements about the potential future employment effects. For example, the CEO of Anthropic said in May 2025 that “AI could wipe out half of all entry-level white-collar jobs - and spike unemployment to 10%–20% in the next one to five years.”1 In their paper, Wang and Wong (2025) develop and quantify an equilibrium model of the labor market to evaluate the potential employment effects of the spread of AI. Their model builds on labor search models with technological change (e.g., Mortensen and Pissarides (1998), and Postel-Vinay (2002)), augmenting the framework to capture the specifics of AI and calibrating it to recent empirical evidence on the impact of AI. The authors arrive at a striking result that the spread of AI will increase productivity by a factor of three but decrease employment by 23%, with approximately half of the increase occurring over the next 5-years. In this comment, I first discuss the central model ingredients of Wang and Wong (2025), which implies that there are both a job creation and job destruction channel of AI. I then discuss how we can learn about the relative strength of the job creation and destruction channels from the spread of computers between the 1980s and early 2000s. Finally, I conclude with avenues for future research." (Author's abstract, IAB-Doku, © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.) ((en))
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Literaturhinweis
Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence (2026)
Zitatform
Brynjolfsson, Erik, Bharat Chandar & Ruyu Chen (2026): Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence. (Working Papers / Stanford Institute for Economic Policy Research), Stanford, 140 S.
Abstract
"Using a sample of high-frequency administrative payroll data from ADP covering millions of U.S. workers through June 2026, we document six facts about the labor market following the widespread adoption of generative AI. (1) We find no evidence of widespread, economy-wide job displacement. (2) However, employment of young workers (ages 22–25) in AI-exposed occupations now stands 19% below where it would be had it kept pace with that of their less-exposed peers; experienced workers show no comparable gap. (3) This divergence has widened steadily since we first documented it in August 2025. (4) It operates primarily through reduced hiring of young workers rather than increased separations. (5) Declines are concentrated in occupations where AI usage primarily substitutes for human tasks; where usage primarily complements workers, employment is flat or rising, especially for experienced workers. (6) Adjustment is occurring through employment rather than base compensation. The divergence is not explained by several prominent alternative factors: it persists when excluding technology firms and computer occupations, when controlling for exposure to interest-rate increases and for remote work, and across alternative measures of AI exposure. These patterns attenuate when controlling for education, show some divergent trends predating generative AI, and are more pronounced in the ADP analysis sample than in national survey benchmarks, with some evidence of consistent patterns in government administrative data. We interpret these facts as early, descriptive indicators—canaries in the coal mine—rather than causal estimates, and we provide a public set of AI Economic Indicators to facilitate ongoing tracking of changes in the economy." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Der KI-Irrtum: Warum Deutschland auf Zuwanderung angewiesen ist: Leitartikel (2026)
Zitatform
Brücker, Herbert, Yuliya Kosyakova & Enzo Weber (2026): Der KI-Irrtum: Warum Deutschland auf Zuwanderung angewiesen ist. Leitartikel. In: Wirtschaftsdienst, Jg. 106, H. 5, S. 304-305. DOI:10.2478/wd-2026-0074
Abstract
"Sieben Millionen - so viele Arbeitskräfte wird Deutschland in den nächsten 15 Jahren allein aufgrund des demografischen Wandels verlieren. Bereits seit vielen Jahren ist der demografische Effekt negativ, mit mehr als 400.000 Arbeitskräften pro Jahr. Tatsächlich beginnt der deutsche Arbeitsmarkt jedoch erst jetzt zu schrumpfen. Denn bislang konnte dieser Rückgang überkompensiert werden - durch eine steigende Erwerbsbeteiligung von Älteren und Frauen; und vor allem durch Zuwanderung. Doch diese Ausgleichsmechanismen stoßen zunehmend an Grenzen. Europa altert insgesamt, und die Dynamik der Zuwanderung innerhalb Europas nimmt ab. Zugleich sind viele der besonders mobilen, jüngeren Kohorten bereits gewandert. Vor diesem Hintergrund wird Migration schwieriger - und genau hier setzt ein verbreitetes Argument an: Wenn Künstliche Intelligenz (KI) zunehmend Aufgaben übernimmt, braucht man doch keine zusätzlichen Arbeitskräfte mehr. Diese Folgerung ist ein Trugschluss. Arbeitskräfteknappheit lässt sich gesamtwirtschaftlich nicht einfach wegdigitalisieren." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Das knappe Gut Arbeit: Automatisierung, Arbeitskräftemangel und sozialer Konflikt (2026)
Zitatform
Butollo, Florian (2026): Das knappe Gut Arbeit. Automatisierung, Arbeitskräftemangel und sozialer Konflikt. (Edition Suhrkamp 2815), Berlin: Suhrkamp Verlag, 254 Seiten.
Abstract
"Angesichts von Digitalisierung und Künstlicher Intelligenz wird allerorten vor massiven Arbeitsplatzverlusten gewarnt. Gleichzeitig reißen die Klagen über Fachkräftemangel nicht ab, zahllose Stellen bleiben unbesetzt, und dem Pflegesektor droht der Kollaps. Florian Butollo geht diesem Paradoxon auf den Grund und analysiert, warum gerade Automatisierung immer mehr Arbeit schafft – und damit zur Keimzelle eines neuen sozialen Konflikts wird: Die anbrechende Ära der Arbeitskräfteknappheit ist geprägt vom Leiden an Überlastung und den Kämpfen dagegen. Zugleich stellt sich die Frage nach der Sinnhaftigkeit von Arbeit neu: Wofür wollen wir angesichts sozialer und ökologischer Krisen künftig unsere Arbeitskraft einsetzen – und welche Tätigkeiten können verschwinden?" (Verlagsangaben, IAB-Doku)
Weiterführende Informationen
Inhaltsverzeichnis -
Literaturhinweis
Evidence on the Adoption of Artificial Intelligence: The Role of Skills Shortage (2026)
Zitatform
Carioli, Paolo, Dirk Czarnitzki & Gastón P. Fernández (2026): Evidence on the Adoption of Artificial Intelligence: The Role of Skills Shortage. (IZA discussion paper / IZA Network @ LISER 18793), Esch-sur-Alzette, 34 S.
Abstract
"Artificial Intelligence (AI) is considered to be the next general-purpose technology, with the potential of performing tasks commonly requiring human capabilities. While it is commonly feared that AI replaces labor and disrupts jobs, we instead investigate the potential of AI for overcoming increasingly alarming skills shortages in firms. We exploit unique German survey data from the Mannheim Innovation Panel on both the adoption of AI and the extent to which firms experience scarcity of skills. We measure skills shortage by the number of job vacancies that could not be filled as planned by firms, distinguishing among different types of skills. To account for the potential endogeneity of skills shortage, we also implement instrumental variable estimators. Overall, we find a positive and significant effect of skills shortage on AI adoption, the breadth of AI methods, and the breadth of areas of application of AI. In addition, we find evidence that shortage on academic qualifications and STEM skills relates to firms adopting AI." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Revisiting the occupational impact of AI in the generative AI era (2026)
Casas, P.; González-Vázquez, I.; Salotti, S.; Martínez-Plumed, F.; Gómez, E.; Fernández-Macías, E.;Zitatform
Casas, P., E. Fernández-Macías, F. Martínez-Plumed, E. Gómez, I. González-Vázquez & S. Salotti (2026): Revisiting the occupational impact of AI in the generative AI era. (JRC working papers series on labour, education and technology 2026,02), Sevilla, 71 S.
Abstract
"Generative AI is reshaping what artificial intelligence can do in the workplace, calling into question pre-GenAI assessments of which workers and tasks are most exposed. In this paper we trace the evolution of AI exposure in the European labour market from 2008 to 2024 by linking 352 AI benchmarks to 14 cognitive abilities, 108 work tasks and 127 ISCO-3 occupations, weighting benchmarks by their research intensity in the AI literature and thus deriving AI exposure by cognitive ability. Bundling work tasks into occupations based on intensity indicators, we explore occupational exposure to AI. We find that the cognitive abilities most exposed to the recent surge of AI research are ideas-related, such as attention and search, comprehension and expression and logical reasoning. Because the associated information processing and problem-solving tasks are the most transversal across occupations, we find an exponential increase in AI exposure across all occupational categories of workers, even though comparatively high-skilled occupations are more exposed than elementary occupations. This points at a substantial and transversal labour market impact of AI." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
A rising tide: revisiting the occupational impact of AI in the generative era (2026)
Casas, Pablo ; González-Vázquez, Ignacio ; Gómez, Emilia; Salotti, Simone ; Fernández-Macías, Enrique ; Martínez-Plumed, Fernando;Zitatform
Casas, Pablo, Enrique Fernández-Macías, Fernando Martínez-Plumed, Emilia Gómez, Ignacio González-Vázquez & Simone Salotti (2026): A rising tide: revisiting the occupational impact of AI in the generative era. In: Structural Change and Economic Dynamics, Jg. 80, S. 223-240. DOI:10.1016/j.strueco.2026.07.016
Abstract
"Generative AI is reshaping what artificial intelligence can do in the workplace, calling into question pre-GenAI assessments of which workers and tasks are most exposed. In this paper we trace the evolution of AI exposure in the European labour market from 2008 to 2024 by linking 352 AI benchmarks to 14 cognitive abilities, 108 work tasks and 127 ISCO-3 occupations, weighting benchmarks by their research intensity in the AI literature and thus deriving AI exposure by cognitive ability. Bundling work tasks into occupations based on intensity indicators, we explore occupational exposure to AI. We find that the cognitive abilities most exposed to the recent surge of AI research are ideas-related, such as attention and search, comprehension and expression and logical reasoning. Because the associated information processing and problem-solving tasks are the most transversal across occupations, we find a steep increase in AI exposure across all occupational categories of workers, even though comparatively high-skilled occupations are more exposed than elementary occupations. This points at a substantial and transversal labour market impact of AI." (Author's abstract, IAB-Doku, © 2026 The Authors. Published by Elsevier B.V.) ((en))
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Literaturhinweis
The AI workforce and firm maturity: old firms, new tech (2026)
Zitatform
Chatjuthamard, Pattanaporn, Pornsit Jiraporn, Pandej Chintrakarn & Woraphon Wattanatorn (2026): The AI workforce and firm maturity: old firms, new tech. In: Journal of business economics, S. 1-45. DOI:10.1007/s11573-026-01271-0
Abstract
"We examine how firm age affects the adoption of artificial intelligence (AI) within the workforce. Drawing on a novel dataset developed by Babina et al. (2024) combining resume and job posting data for U.S. firms, we find that older firms often encounter difficulties integrating AI talent, possibly due to entrenched practices, outdated systems, and resistance to change. Specifically, a rise in firm age by one standard deviation reduces the share of AI workers by 5.2%. However, investments in R&D and infrastructure can mitigate these challenges, enabling modernization and attracting skilled AI professionals. Importantly, our analysis reveals that board composition, particularly the presence of female and minority directors, impacts AI adoption. In older firms, risk-averse decision-making and slower consensus-building among diverse boards may hinder the integration of AI talent. Firms that successfully integrate AI talent achieve significant improvements in market valuation and operational efficiency. Our study provides practical insights for managers and policymakers aiming to guide mature organizations through the challenges of technological transformation. Our findings relate to internal AI capability development and may not fully capture firms’ reliance on external AI solutions." (Author's abstract, IAB-Doku)((en))
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Literaturhinweis
Measuring Task-Based Advanced Automation Technologies & Digital Assistance Systems in the National Educational Panel Study (2026)
Zitatform
Dicks, Alexander, Martin Ehlert, Insa Grüttgen, Benjamin Schulz & Basha Vicari (2026): Measuring Task-Based Advanced Automation Technologies & Digital Assistance Systems in the National Educational Panel Study. (NEPS Survey Papers 128), Bamberg, 31 S. DOI:10.5157/NEPS:SP128:1.0
Abstract
"Wir stellen ein neues taskbasiertes Instrument zur Messung der Automatisierung am Arbeitsplatz vor. Dabei bezeichnen wir die Automatisierungstechnologien als „digitale Assistenzsysteme“, die wir als fortschrittliche Automatisierungssysteme definieren, wie beispielsweise Software und Geräte, die Aufgaben automatisch ausführen, um die Arbeitnehmer zu unterstützen. Dazu gehören Chatbots mit künstlicher Intelligenz, Algorithmen zur Bild- und Videoerstellung, Tools für vorausschauende Wartung und Analyse, digitale Anleitungssysteme sowie Wearables und kollaborative Roboter. Aufbauend auf dem taskbasierten Ansatz und unter Berücksichtigung der jüngsten technologischen Fortschritte haben wir für das Nationale Bildungspanel (NEPS) sieben Items entwickelt, die verschiedene Arten von digitalen Assistenzsystemen erfassen. Ergebnisse auf Grundlage von Daten der NEPS-Startkohorten 4 und 6 zeigen, dass die Nutzung der digitalen Assistenzsysteme je nach Alter und Bildungsniveau variiert und mit komplementären beruflichen Aufgaben sowie Indikatoren für die Digitalisierung am Arbeitsplatz korreliert. Das neue Instrument ergänzt bestehende NEPS-Items zur Digitalisierung am Arbeitsplatz und bietet Potenzial für eine Längsschnittanalyse des technologischen Wandels am Arbeitsplatz und dessen Auswirkungen auf Qualifikationsanforderungen, Ungleichheit und lebenslanges Lernen." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Biased by Design? Case Managers' Multidimensional Preferences Toward the Design of Algorithmic Decision Support Systems (2026)
Zitatform
Dietz, Martin, Christopher Osiander, Mareike Sirman-Winkler & Markus Tepe (2026): Biased by Design? Case Managers' Multidimensional Preferences Toward the Design of Algorithmic Decision Support Systems. In: Public Administration Review, S. 1-14. DOI:10.1111/puar.70111
Abstract
"This study examines whether street-level bureaucrats' preferences toward algorithmic decision support (ADS) induce a unilateral shift of technology-related risks onto clients of the public employment service. Expanding on public value theory and research on moral agency in public service work, we argue that case managers' choices of ADS designs are shaped by a plurality of professional, service, and efficiency values. To test this argument, we conducted a conjoint experiment on a representative sample of German Federal Employment Agency case managers. Respondents compared pairs of hypothetical ADS systems that differed in their design features, reflecting varying degrees of the realization of public values. The empirical results indicate that case managers' choices do not result in biased design. Instead, case managers balance design features reflecting professional and service values while maintaining administrative efficiency. Case managers appreciate ADS support but firmly reject the mandatory use of such advice." (Author's abstract, IAB-Doku, © Wiley) ((en))
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Literaturhinweis
State of AI and Society: AI's quantitative impacts on labour markets (2026)
Dorobantu, Cosmina; Uppal, Akash; Margetts, Helen; Grivokostopoulos, Aristeidis; Balarama, Hemawathy; Goehmann, Maximilian;Zitatform
Dorobantu, Cosmina, Hemawathy Balarama, Akash Uppal, Aristeidis Grivokostopoulos, Maximilian Goehmann & Helen Margetts (2026): State of AI and Society: AI's quantitative impacts on labour markets. London, 41 S. DOI:10.21953/researchonline.lse.ac.uk.00140781
Abstract
"In this report we set out to separate what AI might do to work from what it has demonstrably done and evaluate how the evidence base compares to common claims. The predictions that dominate public debate describe a technology already widely adopted and operating at full scope. The evidence describes something narrower: successive iterations of a technology observed early, in limited slices of the economy, with effects that are smaller and more unevenly distributed than most headlines suggest. Though studies have made substantial progress, especially on the theoretical front, AI's actual net effects in the labour market remain highly uncertain. Evidence gaps are not evenly distributed and point towards a necessary research and policy agenda. Where the evidence is largely absent – such as for developing economies, informal work, deployment mechanisms apart from automation, and worker outcomes – researchers, funders, and policymakers must work alongside industry to create new datasets and methodologies to fill the collective gaps in our knowledge. Where evidence is contested, the binding constraint is nearly always attribution, pointing towards a need for research designs that can separate AI from the many other forces affecting patterns in time series. Where the technology is running ahead of any possible evidence, as is the case with embodied and agentic systems, there are valuable insights to be drawn in building the measurement infrastructure that will allow these effects to be documented as they arrive, rather than reconstructed later from data that were not designed to capture them. The deeper lesson of this report is one of humility about understanding a technology and its impacts in real time. Every empirical estimate in this report is a measurement of a past version of AI, taken during the early phases of diffusion, through instruments lagging behind an evolving world of work. That is not a reason to ignore the evidence, but rather to hold it lightly, and resist the pull of any single figure, regardless of how decisively it is declared. An honest account of AI's effect on work resists the temptation to issue a verdict at this moment in time, opting instead to chart the questions and research agenda that will help us understand what our uncertain future holds." (Text excerpt, IAB-Doku) ((en))
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Literaturhinweis
Impact of robotics on unemployment: Moderating effect of the national culture (2026)
Zitatform
Du, Guangjie, Suman Lodh, Monomita Nandy, Marina Dabić, Vikas Kumar & Jyoti Choudrie (2026): Impact of robotics on unemployment: Moderating effect of the national culture. In: Technological forecasting & social change, Jg. 227. DOI:10.1016/j.techfore.2026.124643
Abstract
"The integration of robotics has created significant opportunities while simultaneously introduced labour market challenges. The aim of this exploratory research is to investigate the impact of robotic adoption on unemployment in a cross-country context. By presenting a novel theoretical framework that integrates the Innovation Diffusion Theory (IDT) and the Absorptive Capacity Theory (ACT), and analysing a panel data from 33 countries, we show that countries with stronger absorptive capacity are better positioned to convert robotics adoption into employment gains. We further test whether national culture, proxied by Hofstede's cultural dimensions, moderates this relationship, uncovering a substantial heterogeneity across six cultural factors. This provides a practical blueprint for policymakers with clear evidence that uniform approaches to robotics adoption are unlikely to be effective and robotics policies must be tailored to local cultural norms, institutional capabilities, and national readiness. The policymaker should prioritise capability building alongside adjustment measures that support inclusive labour-market transitions." (Author's abstract, IAB-Doku, © 2026 The Authors. Published by Elsevier Inc.) ((en))
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Literaturhinweis
AI Diffusion and the Possibility of Full Unemployment: An Urgent but Neglected Issue (2026)
Zitatform
D’Orlando, Fabio (2026): AI Diffusion and the Possibility of Full Unemployment: An Urgent but Neglected Issue. In: Review of radical political economics, S. 1-20. DOI:10.1177/04866134261452321
Abstract
"Artificial Intelligence (AI) is often viewed as a potential catalyst for mass unemployment. However, there has been a notable lack of contributions published in economics journals on this topic, with virtually none exploring the possibility of full unemployment. This article has three main goals. First, it defines full unemployment and outlines its key theoretical features. Second, it seeks to explain economists’ reluctance to acknowledge AI’s potential to induce mass unemployment and, a fortiori, full unemployment. Finally, the article aims to clarify why, despite its low probability, full unemployment deserves serious theoretical consideration and detailed analysis by economists. It suggests that full unemployment might be seen as the long-term asymptotic endpoint of short-term dynamics marked by rising unemployment, even if the actual magnitudes never reach it." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Can machines replace human workers? Notes on agency, automation, and AI (2026)
Enrique, Fernandez Macias;Zitatform
Enrique, Fernandez Macias (2026): Can machines replace human workers? Notes on agency, automation, and AI. (JRC working papers series on labour, education and technology 2026,06), Sevilla, 26 S.
Abstract
"This paper proposes a conceptual framework for analysing the automation of human labour, with particular attention to artificial intelligence. Rather than presenting empirical findings, it builds an argument step by step from simple initial definitions and examines the implications, aiming to clarify a debate that is often muddled. The central proposal is to distinguish two components of human labour: operational (the execution of specifiable transformations) and agentic (the interpretation of situations, the exercise of judgement, the capacity to adapt and innovate). Machines can substitute for the operational component, but not for the agentic one. Automatability is therefore best understood not as a property of task content but as a property of work organisation: only labour that has been stripped of agency and reduced to the operation of predefined tasks can be automated. The framework is illustrated through the historical case of Taylorism, which suggests that agency extraction has typically preceded mechanisation, and then applied to AI. AI expands the operational frontier and appears to extend into the agentic domain, but its apparent agency is simulated rather than real: a system with genuine agency would be able to refuse and resist as human workers can. The implication is that AI cannot fully replace human labour while retaining its productive value, though a dystopian arrangement in which agency is concentrated in few hands while operational systems carry out the rest remains conceivable. A more desirable arrangement, in which AI augments individual workers as an extension of their own capacities, is also conceivable but requires treating AI as a public good rather than a private corporate asset." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Algorithmic Profiling: Effective Tool for Targeting Active Labour Market Policies? (2026)
Zitatform
Eppel, Rainer, Ulrike Huemer, Helmut Mahringer & Lukas Schmoigl (2026): Algorithmic Profiling: Effective Tool for Targeting Active Labour Market Policies? In: Labour, S. 1-20. DOI:10.1111/labr.70013
Abstract
"Digitisation has sparked interest in automated decision making in Public Employment Services (PES). We evaluate an algorithmic profiling model in Austria that predicts the reemployment prospects of unemployed individuals to classify and assign them to active labour market policies. Our analysis shows that reallocating resources from jobseekers with low to medium prospects, as proposed by the PES, does not yield the expected efficiency gains. We find no systematic evidence that programmes are less effective for individuals with low predicted employment prospects than for those with medium prospects. These findings caution against crude algorithmic profiling and highlight the need for nuanced targeting strategies that prioritise the most disadvantaged jobseekers." (Author's abstract, IAB-Doku, Published by arrangement with John Wiley & Sons) ((en))
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Literaturhinweis
Labor Market Consequences of Generative AI: Early Evidence from Norway (2026)
Zitatform
Facius, Dennis & Roberto Iacono (2026): Labor Market Consequences of Generative AI: Early Evidence from Norway. (CESifo working paper 12752), München, 49 S.
Abstract
"Does Generative AI displace early-career workers? We provide population-wide evidence from Norwegian administrative registers, 2015 through March 2025, exploiting the November 2022 release of ChatGPT as an availability shock. Using the within-firm composition difference-in-differences employed in recent work, supplemented with a synthetic difference-in-differences at the occupation level and a firm-level shift-share design, we find no robust evidence of employment displacement among young workers in highly AI-exposed occupations, nor any robust response across other age cohorts or on incumbent labor-market outcomes. While estimated coefficients for young workers are negative, in line with the existing literature, they are small and statistically insignificant. A backdating exercise on the synthetic difference-in-differences yields larger absolute estimates than the actual treatment date across most age bands. This suggests the apparent post-2022 decline reflects, at least in part, pre-existing secular trends rather than a clean AI-period break." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Toward a Bad Job Economy: AI Adoption, Agency Costs, and Job Design (2026)
Zitatform
Fahn, Matthias, Jin Li & Chang Sun (2026): Toward a Bad Job Economy: AI Adoption, Agency Costs, and Job Design. (CESifo working paper 12612), München, 23 S., App.
Abstract
"We study how AI affects compensation and job design when performance depends on workers' non-contractible effort. In a principal–agent model with limited liability, AI reduces effort costs but disproportionately lowers the cost of achieving satisfactory performance. This raises the incentive cost of sustaining high effort and can induce firms to replace high-wage, high-effort good jobs with low-wage, low-effort bad jobs, even when good jobs create more total surplus. As a result, AI can lower wages, reduce worker welfare, and even depress profits. If workers can adopt AI unilaterally, adoption occurs even when the resulting equilibrium harms both parties; when adoption requires worker cooperation, resistance is strongest where AI erodes rents embodied in good jobs. In a search-and-matching extension, endogenous outside options amplify these forces, reinforcing a bad-job economy and potentially reducing employment." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
AI and labor market outcomes: evidence from China (2026)
Zitatform
Fang, Tony, Carl Lin & Qing Liu (2026): AI and labor market outcomes: evidence from China. In: Journal of Productivity Analysis, Jg. 65, H. 3. DOI:10.1007/s11123-026-00817-2
Abstract
"As artificial intelligence (AI) spreads worldwide, its impact on labor markets is still unfolding and remains uncertain, yet potentially far-reaching in developing economies undergoing rapid structural transformation. This paper provides the first large-scale evidence from China, where AI investment and adoption have expanded rapidly, linking local AI labor demand to individual wage outcomes. We construct city–year measures of AI labor demand from 1.6 million online job postings between 2016 and 2024, capturing the intensity, breadth, and diversity of AI-related hiring, and merge them with nationally representative microdata from the China Family Panel Studies (2016–2022). Fixed-effects estimates show that local AI labor demand has positive impacts on individual wages: a one-unit increase in AI demand (1,000 postings, firms, or job titles) raises wages by about 0.2–0.3%. The benefits, however, are uneven. Womenexperience stronger gains—about 0.5–0.7% per unit increase—while men show no measurable effect. Wage effects are largest in Western provinces and in China’s major AI-cluster cities—Jing–Jin–Ji, Yangtze River Delta, Pearl River Delta, and the Southwest–Central corridor—where complementary production factors and digital infrastructure are most developed. Occupational analyses further show that women’s gains are concentrated in service-oriented, less skill-intensive jobs where AI complements interpersonal and coordination tasks rather than substituting them. Overall, AI diffusion generates meaningful but unequal labor market spillovers, with wage gains concentrated among women, dynamic regions, and human–AI complementary occupations, underscoring both the opportunities of technological transformation and the challenges of achieving inclusive growth." (Author's abstract, IAB-Doku, © Springer-Verlag) ((en))
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Literaturhinweis
Künstliche Intelligenz in deutschen Betrieben: Jeder vierte Betrieb nutzt mittlerweile generative KI (2026)
Zitatform
Friedrich, Martin & Christian Kagerl (2026): Künstliche Intelligenz in deutschen Betrieben: Jeder vierte Betrieb nutzt mittlerweile generative KI. (IAB-Kurzbericht 08/2026), Nürnberg, 8 S. DOI:10.48720/IAB.KB.2608
Abstract
"Generativer Künstlicher Intelligenz (KI) wird häufig bescheinigt, die Wirtschaft fundamental zu verändern. Wie verbreitet diese Technologie bereits in deutschen Betrieben ist, zeigen aktuelle Auswertungen aus dem IAB-Betriebspanel. Daten zum Themenschwerpunkt „Generative KI“ wurden 2025 erstmals erhoben." (Autorenreferat, IAB-Doku)
Weiterführende Informationen
- Weiterbildung und Regelungen zum Umgang mit der Technologie in Betrieben, die generative KI nutzen
- Art des betrieblichen Einsatzes von KI
- Entwicklung des betrieblichen Einsatzes von KI
- Determinanten der Wahrscheinlichkeit, generative KI im Betrieb zu nutzen
- Generative KI in deutschen Betrieben
- IAB-Forum Video
- Verbreitung von KI nach Betriebsgröße, Betriebsalter und Branchen
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Literaturhinweis
Nutzung und Verwendung von Künstlicher Intelligenz in Betrieben in Baden-Württemberg: Auswertung des IAB-Betriebspanels Baden-Württemberg (2026)
Gasparro, Jonah; Wiemann, Jan Simon;Zitatform
Gasparro, Jonah & Jan Simon Wiemann (2026): Nutzung und Verwendung von Künstlicher Intelligenz in Betrieben in Baden-Württemberg. Auswertung des IAB-Betriebspanels Baden-Württemberg. (IAW-Kurzberichte 2026,01), Tübingen, 46 S.
Abstract
"KI ist in den Betrieben in Baden-Württemberg angekommen, wird aber noch nicht flächendeckend genutzt. 31 Prozent der Betriebe nutzen generative und/oder nicht-generative KI. Die Nutzung wird vor allem von generativer KI getragen: 28 Prozent der Betriebe setzen generative KI ein; damit liegt Baden-Württemberg über dem deutschlandweiten Vergleichswert von 24 Prozent. Nicht-generative KI ist mit 7 Prozent deutlich seltener verbreitet. KI wurde überwiegend erst ab 2024 in den Betrieben eingeführt. Die Nutzung von KI unterscheidet sich deutlich nach Betriebsgröße und Branche. Größere Betriebe nutzen KI wesentlich häufiger als kleinere Betriebe. Die Nutzung von KI ist darüber hinaus im Dienstleistungssektor besonders verbreitet. Der Einsatz generativer KI wird bislang nur teilweise durch Investitionen, Weiterbildungsmaßnahmen oder formale Regelungen begleitet. Größere Betriebe weisen solche begleitenden Maßnahmen häufiger als Klein(st)betriebe auf. Betriebe, die derzeit keine KI nutzen und auch keinen künftigen Einsatz planen, begründen dies vor allem mit fehlenden Anwendungsmöglichkeiten. Als weitere Gründe nennen diese Betriebe fehlendes Wissen, Inkompatibilitäten mit bestehenden betrieblichen Strukturen, rechtliche Unsicherheiten sowie Datenschutz- und Datensicherheitsbedenken. Die Nutzung von KI in den Betrieben in Baden-Württemberg hängt besonders deutlich mit der Exportneigung und dem Vorhandensein eines Betriebsrats zusammen. Exportierende Betriebe sowie Betriebe mit Betriebsrat nutzen KI häufiger als nichtexportierende Betriebe beziehungsweise Betriebe ohne Betriebsrat. Im Handwerk ist KI bislang weniger verbreitet als in der Gesamtwirtschaft: Rund 21 Prozent der Handwerksbetriebe nutzen generative und/oder nicht-generative KI." (Autorenreferat, IAB-Doku)
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Literaturhinweis
AI and Europe's labor market (2026)
Zitatform
Gathmann, Christina (2026): AI and Europe's labor market. In: EconPol Forum, Jg. 27, H. 2, S. 33-38.
Abstract
"Adoption of AI has accelerated in Europe, especially after 2022. To adopt AI, firms need employees with data and AI skills, but small firms in particular lack the necessary expertise. AI shifts skill demand in exposed occupations, requiring AI skills combined with judgment and decision-making skills. AI changes what workers do on the job - it has not, so far, destroyed jobs. Productivity gains from AI remain largely invisible in aggregate statistics - so far." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
AI-Powered Skill Classification: Mapping Technology Intensity in the German Labor Market (2026)
Zitatform
Grenz, Sabrina, Terry Gregory & Florian Lehmer (2026): AI-Powered Skill Classification: Mapping Technology Intensity in the German Labor Market. (IZA discussion paper / IZA Network @ LISER 18415), Bonn: IZA Network @ LISER, 47 S.
Abstract
"The rapid evolution of technology is reshaping labor markets by altering skill demands and job profiles. This paper introduces a novel skill-based measure of occupational technology intensity – the Occupational Technology Skill Share (OTSS) – that distinguishes between manual, digital, and frontier technologies, including artificial intelligence (AI). Using natural language processing, generative AI, and supervised machine learning, we develop an AI-powered skill classification that enriches occupation linked skill labels with standardized GenAI-generated descriptions and structured indicators of technological content, enabling transparent classification by technology intensity. We compute OTSS for all occupations in the German labor market. For the average worker in 2023, manual technologies account for the largest share of skill content (42%), followed by digital (38%) and frontier technologies (20%). Frontier technologies remain concentrated in specialized occupations, while digital technologies are widespread. Linking these measures to administrative data from 2012–2023 shows a broad shift from manual and digital toward frontier skills across occupations, and reveals a non-linear, U-shaped relationship between changes in frontier skill intensity and employment growth." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Generative AI and Career Choices (2026)
Zitatform
Gschwendt, Christian, Martina Viarengo & Thea S. Zoellner (2026): Generative AI and Career Choices. (Working paper / Swiss Leading House 251), Zürich, 52 S.
Abstract
"The economic impact of technological change will critically depend on how future workers invest in their human capital. Yet, little is known about how future workers themselves evaluate and choose their educational and occupational paths in light of emerging technologies. This paper examines how adolescents currently at the school-to-work transition stage value working with generative artificial intelligence (GenAI) in their future occupations, and how automation risk and opportunities for continuing education shape these preferences. We field a discrete-choice experiment among a nationally representative sample of over 7,000 Swiss adolescents aged around 15. We find that adolescents generally exhibit an aversion to collaborating with GenAI at work, with females consistently more averse than males. However, preferences are nuanced: adolescents welcome greater GenAI collaboration, provided that GenAI usage levels remain moderate and that it is not accompanied by increases in job-automation risk. Finally, continuing education opportunities in occupations improve attitudes towards working with GenAI across genders. Our results challenge simple narratives of technology acceptance or rejection, revealing that adolescents' willingness to work with GenAI depends on how it is implemented - its intensity, associated displacement risks, and accompanying skill development - rather than the technology itself. Our findings suggest that the way future workers value GenAI collaboration in their career choices critically depends on its intensity and on the interplay with automation risk and AI-related educational opportunities." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Automation, skill and job creation (2026)
Zitatform
Guo, Kaizhao (2026): Automation, skill and job creation. In: Empirical economics, Jg. 70, H. 5. DOI:10.1007/s00181-026-02912-7
Abstract
"This paper explores the heterogeneous effects of automation technologies on employment rate across US regions from different income groups, and investigates mechanisms through proportion of skilled workers. Automation, measured by both robotic penetration and ICT trade volumes, is replacing labour force. Exploiting variations across US commuting zones, this study finds that employment reductions are significant and substantial in low and middle income areas, and rising income levels could cause insignificant employment responses. Leveraging shift-share IV strategies and generalised model specifications, further evidence suggests that a simple net job creation channel can explain these patterns. Specifically, displacement effects outweigh productivity effects in low income CZs with lower proportion of skilled labour, and job losses are larger in middle income CZs with concentration of routine occupations; job creations are complementing job destructions with growing income levels and higher skill shares. These technical changes are particularly significant in manufacturing sectors." (Author's abstract, IAB-Doku, © Springer-Verlag) ((en))
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Literaturhinweis
Arbeitszeit, Produktivität, KI - wie Deutschland sein Arbeitskräfteangebot stabilisieren kann: Teil des Zeitgesprächs "Arbeitszeit im Wandel - Wie sich Wohlstand trotz sinkenden Arbeitskräfteangebots sichern lässt" (2026)
Hammermann, Andrea; Stettes, Oliver;Zitatform
Hammermann, Andrea & Oliver Stettes (2026): Arbeitszeit, Produktivität, KI - wie Deutschland sein Arbeitskräfteangebot stabilisieren kann. Teil des Zeitgesprächs "Arbeitszeit im Wandel - Wie sich Wohlstand trotz sinkenden Arbeitskräfteangebots sichern lässt". In: Wirtschaftsdienst, Jg. 106, H. 4, S. 248-252. DOI:10.2478/wd-2026-0064
Abstract
"Längere Arbeitszeiten können einen wichtigen Beitrag zur Stabilisierung des Arbeitskräfteangebots leisten, während Investitionen in technologischen und organisatorischen Fortschritt notwendig sind, um die Arbeitsproduktivität zu steigern. Vor diesem Hintergrund geht der Beitrag der Frage nach, wie sich das Arbeitskräfteangebot in Deutschland trotz des demografischen Wandels stabilisieren und der Wohlstand langfristig sichern lässt. Empirische Befunde legen nahe, dass KI und Humankapital in der Regel komplementär wirken und KIAnwendungen menschliche Arbeit eher ergänzen als ersetzen. Entscheidend für den Erhalt des Wohlstands ist somit, beide Hebel – Arbeitszeitund Produktivität – gemeinsam zu nutzen." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Reassessing the Impact of Artificial Intelligence on Employment: Evidence Against the Mass Job Loss Hypothesis (2026)
Handel, Michael J.;Zitatform
Handel, Michael J. (2026): Reassessing the Impact of Artificial Intelligence on Employment: Evidence Against the Mass Job Loss Hypothesis. In: Labour, S. 1-23. DOI:10.1111/labr.70018
Abstract
"Advances in artificial intelligence (AI) and robotics since 2000 led to widespread concern that automation would eliminate a massive number of jobs. Frey and Osborne's influential study concluded AI and robots might eliminate nearly half of all US jobs between 2010 and 2030. However, these and related claims have received little critical evaluation. This paper contrasts predictions of mass job loss with employment projections made by the U.S. Bureau of Labor Statistics (BLS) suggesting modest changes over the same period and evaluates both with respect to empirical employment data for 2010–2023. Theevidence strongly supports BLS projections of gradual change. There is little evidence of mass displacement or any kind of technology-driven structural break with previous employment trends, arguing for caution in making predictions of the future impacts of robotics and AI on jobs." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Use of Gen AI in the Workplace and the Value of Access to Training (2026)
Zitatform
Hashim, Ali, Gizem Koşar & Wilbert van der Klaauw (2026): Use of Gen AI in the Workplace and the Value of Access to Training. (Liberty Street Economics / Federal Reserve Bank of New York 2026-04-14), New York, NY: Federal Reserve Bank of New York, 5 S. DOI:10.59576/lse.20260414
Abstract
"The rapid spread of generative AI (AI) tools is reshaping the workplace at a remarkable rate. Yet relatively little is known about whether workers have access to these tools, how the tools affect workers’ daily productivity, and how much workers value the training needed to use the tools effectively. In this post, we shed light on these issues by drawing on supplemental questions in the November 2025 Survey of Consumer Expectations (SCE), fielded to a representative sample of the U.S. population. We find that adoption of AI tools at work is heterogeneous, that a sizable share of workers see AI training as important, and that a significant share of employers are nonetheless not yet providing access to AI tools or training on how to use them." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Künstliche Intelligenz im Übergang Schule – Beruf: Chancen, Risiken und Qualitätskriterien (2026)
Hense, Julia; Goertz, Lutz;Zitatform
Hense, Julia & Lutz Goertz (2026): Künstliche Intelligenz im Übergang Schule – Beruf. Chancen, Risiken und Qualitätskriterien. (ueberaus.de), Bonn, o. Sz.
Abstract
"Künstliche Intelligenz verändert den Übergang von der Schule in Ausbildung und Beruf – aber wie nutzen Fachkräfte KI verantwortungsvoll? Julia Hense und Lutz Goertz erläutern, wie KI diese unterstützen kann und wo ihre Grenzen liegen. Von der Simulation von Vorstellungsgesprächen über Chatbots zur Berufsorientierung bis zur datengestützten Analyse von Potenzialen: Die praktischen Chancen sind vielfältig. Gleichzeitig braucht es kritische Reflexion und klare Qualitätskriterien, um Diskriminierung zu vermeiden und Vertrauen zu schaffen. Der Basisartikel bietet konkrete Handlungsempfehlungen für Fachkräfte und zeigt: KI ist kein Ersatz für menschliche Beziehungsarbeit, sondern ein hilfreiches Werkzeug – wenn es richtig eingesetzt wird." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Has AI Widened Employment Gaps? Tracking Early-Career Employment by Occupational Exposure in Norway (2026)
Zitatform
Hernæs, Øystein & Andreas Kostøl (2026): Has AI Widened Employment Gaps? Tracking Early-Career Employment by Occupational Exposure in Norway. (IZA discussion paper / IZA Network @ LISER 18767), Esch-sur-Alzette, 50 S.
Abstract
"This paper uses data on the universe of private-sector employment in Norway up to February 2026 to examine whether AI exposure has contributed to a widening employment gap across occupations with varying AI exposure. Since October 2022, the month before ChatGPT's release, employment in the most exposed occupations has grown by 0.1 percent, against 0.3 percent in the least exposed occupations. We track this number on a monthly basis on the public dashboard \emph{kiindeksen.no}. The dashboard updates the full-distribution comparison each month as new administrative data arrives, allowing differential employment growth by AI exposure to be tracked over time. We also show that when we compare young workers by complete occupation quintiles of exposure, the relative decline of the most exposed quintile is estimated as an imprecise zero." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Age-based stereotypes in hiring and training: how digitalization and labor shortages reshape employer decisions (2026)
Zitatform
Heyer, Philipp, Wiebke Schmitz & Kathrin Weis (2026): Age-based stereotypes in hiring and training: how digitalization and labor shortages reshape employer decisions. In: European Journal of Ageing, Jg. 23, H. 1. DOI:10.1007/s10433-026-00936-7
Abstract
"Demographic aging contributes to labor shortages and strains welfare systems across Europe, including Germany. In response, extending older workers’ labor market participation has become a growing policy concern. Yet, age-based stereotypes may hinder this goal by reducing their chances in personnel decisions like hiring and training. However, the introduction of digital technologies is reshaping work dynamics, while labor shortages—partly driven by demographic aging—are intensifying competition for skilled workers. This puts pressure on companies to upskill their workforce and secure qualified personnel, potentially limiting the scope for decisions based on stereotypes. While age-based stereotypes have been widely studied, the organizational context —such as labor shortages and digitalization—has received less attention. This study analyzes representative employer data from the 2024 BIBB Training Panel in Germany to examine (1) how age-based stereotypes among employers are linked to personnel decisions regarding hiring and training of older workers and (2) how this association differs by perceived labor shortages and digitalization levels. Using fractional-logit regression models (n = 1310), the results show that while age-based stereotypes matter, their influence might be moderated by structural conditions: Digitalization mitigates training decisions that disadvantage older employees. There is also some evidence indicating that labor shortages may reduce hiring bias. These findings indicate that age-based stereotypes and their consequences do not operate in isolation: They are shaped by structural constraints and opportunities. In times of labor shortages and digital transformation, policy should address age-based stereotypes and reposition older workers not as a burden but as an asset." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI (2026)
Zitatform
Humlum, Anders & Emilie Vestergaard (2026): Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI. (RF Berlin - CReAM Discussion Paper Series 2026,78), Berlin, 81 S.
Abstract
"We study the early labor market impacts of AI chatbots by linking large-scale adoption surveys to administrative labor market records in Denmark. We document rapid currents: most employers in exposed occupations have adopted chatbot initiatives, workers report productivity benefits, and new AI-related tasks are widespread. Yet these currents have not broken the surface: using difference-in-differences, we estimate precise null effects on earnings and recorded hours at both the worker and workplace levels, ruling out effects larger than 2% two years after the launch of ChatGPT. What moves is the structure of work: employers absorb AI through task reorganization-including new tasks in content generation, AI oversight, and AI integration-and adopters transition into higher-paying occupations where AI chatbots are more relevant, though still too few to move average earnings. Technological change reshapes work well before it surfaces in earnings or hours." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Generative AI and the labour market: the disconnect between task-level capability and downstream employment (2026)
Zitatform
Iacono, Roberto (2026): Generative AI and the labour market: the disconnect between task-level capability and downstream employment. (LEM working paper series / Laboratory of Economics and Management 2026/24), Pisa, 33 S.
Abstract
"Generative artificial intelligence (GenAI) has produced rapid, well-measured gains in task-level capability since the public release of ChatGPT in late 2022, yet the transmission of those gains into downstream employment and earnings remains slow, uneven and shaped by frictions that headline coverage of the technology often understates. Linked administrative records from Denmark, Finland and Norway estimate small effects precisely on incumbent earnings and hours; large multi-country firm surveys report very small employment changes; and macroeconomic projections grounded in current task content imply modest aggregate impacts. Adjustment is becoming visible only on the margin with the fewest barriers, entry-level hiring in exposed occupations. Even there, identification is contested by an organisational co-shock, the post-pandemic shift to working from home, that is closely correlated with AI exposure. This research argues that the main object of analysis is not whether aggregate employment has fallen, but the chain of intervening stages: firm adoption, depth of deployment, task reorganisation, complementary investment, and reallocation across hiring and incumbent margins. This paper proposes a research agenda that measures those stages one by one." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
The impact of AI on global knowledge work (2026)
Ide, Enrique; Talamas, Eduard;Zitatform
Ide, Enrique & Eduard Talamas (2026): The impact of AI on global knowledge work. In: Journal of monetary economics, Jg. 157. DOI:10.1016/j.jmoneco.2025.103876
Abstract
"We analyze how Artificial Intelligence (AI) reshapes global knowledge work in a two-region world where firms organize production hierarchically to use knowledge efficiently: the most knowledgeable individuals specialize in problem-solving, while others perform routine work. Before AI, the Advanced Economy specializes in problem-solving services, whereas the Emerging Economy focuses on routine work. AI converts compute — which is located in the Advanced Economy — into autonomous “AI agents” that perfectly substitute for humans with a given level of knowledge. Basic AI reduces the Advanced Economy ’s net exports of problem-solving services, potentially reversing pre-AI trade patterns. In contrast, sophisticated AI expands these exports, reinforcing existing trade patterns. Finally, we show that a global ban on AI autonomy redistributes AI’s gains toward lower-skilled workers, while a regional ban — such as prohibiting autonomy only in the Emerging Economy — offers little benefit to lower-skilled workers and harms the most knowledgeable individuals in that region." (Author's abstract, IAB-Doku, © 2025 The Authors. Published by Elsevier B.V.) ((en))
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Literaturhinweis
Digital Gender Gap: Schwerpunkt 2026 Künstliche Intelligenz (2026)
Zitatform
Jahn, Sandy, Carola Burkert, Katharina Diener & Britta Matthes (2026): Digital Gender Gap. Schwerpunkt 2026 Künstliche Intelligenz. Berlin, 20 S. DOI:10.48720/IAB.D21.2026
Abstract
"Künstliche Intelligenz wird immer mehr zur Schlüsselressource. Ihre Nutzung entscheidet zunehmend über Wettbewerbsfähigkeit, Beschäftigungschancen und gesellschaftliche Teilhabe – vergleichbar mit Alphabetisierung oder Internetzugang in früheren Transformationsphasen. Die Studie des IAB und der Initiative D 21 zeigt: Es besteht ein signifikanter Gender AI Gap. Frauen nutzen KI-Anwendungen seltener und weniger intensiv als Männer (rund 16 Prozentpunkte Unterschied in der Ausgangsbetrachtung). Wenn Unterschiede in Alter, Bildung, Einkommen, beruflichem Kontext sowie Kompetenzen und Einstellungen statistisch berücksichtigt werden, verringert sich die Lücke zwar – bleibt aber auch dann bestehen (rund 8 Prozentpunkte)." (Autorenreferat, IAB-Doku)
Weiterführende Informationen
Interview mit den Autorinnen im Online-Magazin IAB-Forum -
Literaturhinweis
AI and Our Economic Future (2026)
Jones, Charles I.;Zitatform
Jones, Charles I. (2026): AI and Our Economic Future. In: The Journal of Economic Perspectives, Jg. 40, H. 3, S. 3-22. DOI:10.1257/jep.20261505
Abstract
"Artificial intelligence (AI) will likely be the most transformative technology of the modern era. What if machines—AI for cognitive tasks and AI plus advanced robots for physical tasks—can perform every task a human can? This essay makes three main points. First, even though US growth rates have been stable at roughly 2 percent per year for 150 years, it is distinctly possible that automating intelligence leads economic growth rates to accelerate. Second, this acceleration is likely to be slowed by the presence of “weak links.” While we each have access to 100 million times more transistors on our desktop computer than people in the 1970s, we are not 100 million times more productive. Computers can invert matrices at lightning speed, but we humans must still decide what matrix to invert, what hypothesis to test, and so on. Accelerating economic growth requires the vast majority of the weak links to be automated away, which delays the large gains. Finally, even though weak links slow the benefits, they may actually speed up the risks. When a chain is only as strong as its weakest link, damaging one link in the chain can be very costly. A powerful AI that is superhuman at software engineering could be misused by a bad actor to do substantial harm by hacking the financial system or a virology lab." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Der Gender AI Gap: "KI wird zur Schlüsselressource - aber Männer und Frauen nutzen sie nicht gleich" (2026)
Zitatform
Keitel, Christiane; Katharina Diener, Britta Matthes, Sandy Jahn & Carola Burkert (interviewte Person) (2026): Der Gender AI Gap: "KI wird zur Schlüsselressource - aber Männer und Frauen nutzen sie nicht gleich". In: IAB-Forum H. 23.04.2026. DOI:10.48720/IAB.FOO.20260423.01
Abstract
"Mit der rasanten Verbreitung von Künstlicher Intelligenz in der Arbeitswelt entsteht eine neue Lücke zwischen den Geschlechtern: der Gender AI Gap. Dies zeigt eine aktuelle Studie des IAB, die in Zusammenarbeit mit der Initiative D21 entstanden ist, Deutschlands größtem gemeinnützigen Netzwerk für die digitale Gesellschaft. Der Studie zufolge nutzen Frauen KI deutlich seltener und weniger intensiv nutzen als Männer – selbst bei vergleichbaren Voraussetzungen. Warum das so ist, welche Rolle Netzwerke und Wahrnehmungen spielen, und an welchen Stellschrauben Politik und Betriebe jetzt ansetzen müssen, erläutern die Autorinnen der Studie im Interview." (Autorenreferat, IAB-Doku)
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