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
New Technology, Older Workers: How Workplace Technology is Associated with Indicators of Job Retention (2026)
Zitatform
Abrams, Leah, Daniel Schneider & Kristen Harknett (2026): New Technology, Older Workers: How Workplace Technology is Associated with Indicators of Job Retention. In: Journal of Aging & Social Policy, Jg. 38, H. 4, S. 635-651. DOI:10.1080/08959420.2025.2523122
Abstract
"Middle-aged and older adults who are employed in precarious, high-strain jobs may face challenges to continued work, risking economic insecurity and poor wellbeing in retirement. Technology in the workplace, an under-studied aspect of work environments, could accommodate aging workers or could add stress to their jobs. This study examines how technology in sales and surveillance at work are related to job satisfaction and planned job exits among approximately 6,000 workers aged 50–69 employed in the low-wage service sector (e.g. retail, pharmacy, grocery, hardware, fast food, casual dining, delivery, and hotel). On-the-job surveillance was related to lower job satisfaction and higher reports of looking for a new job, especially when combined with sanctioning for slow speed of work. However, rewards for speed, and to a lesser extent the use of leaderboards, were associated with higher job satisfaction, demonstrating the potential of technology to enhance the work experience for older employees. The use of sales technologies was not associated with job satisfaction or intentions to look for a new job. These results provide a uniquely detailed portrait of prevailing labor market conditions for aging workers in the service sector and demonstrate how certain kinds of technology matter for older workers ’ employment." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Automation and Polarization (2026)
Zitatform
Acemoglu, Daron & Jonas Löbbing (2026): Automation and Polarization. In: Journal of Political Economy, Jg. 134, H. 3, S. 1017-1072. DOI:10.1086/739330
Abstract
"We develop an assignment model of automation. Each of a continuum of tasks of variable complexity is assigned to either capital or one of a continuum of labor skills. We characterize conditions for interiorautomation, whereby tasks of intermediate complexity are performed by capital. Interior automation arises when the most skilled workers have a comparative advantage in the most complex tasks relative to capital, and when the wages of the least skilled workers are sufficiently low relative to both their own productivity and the effective cost of capital in low-complexity tasks. Minimum wages and other sourcesof higher wages at the bottom make interior automation less likely. Starting with interior automation, a reduction in the cost of capital (or an increase in capital productivity) causes employment and wage polarization. Specifically, further automation pushes workers into tasks at the lower and upper ends ofthe task distribution. It also monotonically increases the skill premium above a threshold and reduces the skill premium below this threshold. Moreover, automation tends to reduce the real wage of Workers with comparative advantage profiles close to that of capital. We show that large enough increases in capital productivity ultimately induce a transition to low-skill automation and qualitatively alter the effects of automation—thereafter inducing monotone increases in skill premia rather than wage polarization." (Author's abstract, IAB-Doku) ((en))
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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
Technologischer Wandel und Löhne: Die Anpassung der Berufe spielt eine entscheidende Rolle (2026)
Zitatform
Bachmann, Ronald, Gökay Demir, Colin Green & Arne Uhlendorff (2026): Technologischer Wandel und Löhne: Die Anpassung der Berufe spielt eine entscheidende Rolle. (IAB-Kurzbericht 01/2026), Nürnberg, 8 S. DOI:10.48720/IAB.KB.2601
Abstract
"Technischer Fortschritt verändert die Arbeitswelt - besonders in Berufen, in denen viele Tätigkeiten leicht automatisiert werden können. In den letzten Jahrzehnten ist der Anteil an Routinetätigkeiten in vielen Berufen deutlich zurückgegangen - häufig zugunsten nicht routinemäßiger kognitiver Tätigkeiten wie Analysieren, Planen oder Beraten. Dabei verzeichnen Berufe, deren Tätigkeiten sich im Laufe der Zeit stärker an den technologischen Wandel angepasst haben, steigende Löhne. Sie zeichnen sich zudem durch intensivere Weiterbildungsaktivitäten aus. In Berufen, deren Tätigkeitsprofil sich kaum verändert hat, stagnieren die Löhne dagegen häufiger." (Autorenreferat, IAB-Doku)
Weiterführende Informationen
- Vollzeitbeschäftigte westdeutsche Männer in ursprünglich routinelastigen Berufen
- Veränderung von Tätigkeitsschwerpunkten durch technologischen Wandel
- Veränderung im Anteil der Routinetätigkeiten
- Veränderung im Anteil der Routinetätigkeiten im Vergleich zu nicht routinemäßigen (NR) kognitiven Tätigkeiten in exemplarisch ausgewählten, ursprünglich routinelastigen Berufsfeldern
- Anteil Beschäftigter in Weiterbildungskursen nach Tätigkeitsgruppen
- Relatives Lohnwachstum nach Tätigkeitsgruppen
- Vollzeitbeschäftigte westdeutsche Männer nach Tätigkeitsgruppen
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Literaturhinweis
Technological progress, occupational structure and gender gaps in the German labour market (2026)
Zitatform
Bachmann, Ronald & Myrielle Gonschor (2026): Technological progress, occupational structure and gender gaps in the German labour market. In: International Journal of Manpower, Jg. 47, H. 2, S. 403-422. DOI:10.1108/ijm-10-2024-0711
Abstract
"Purpose: This study examines whether advances in technology and shifts in the occupational structure have enhanced women's position within the labour market. It answers the following research questions: First, how has the occupational employment structure evolved over the last decades, and how is this related to gender gaps? Second, which factors explain the gender gap and its narrowing over time? Design/methodology/approach: We use individual-level panel data from the Socio-Economic Panel (SOEP) for West Germany over the period 1985–2017 and individual-level task data from the BIBB Employment survey. We use the task intensity of an occupation and the task group an occupation belongs to as proxies for technological change. To analyze explanatory factors, we use a Blinder-Oaxaca decomposition. Findings: Women are increasingly engaging in non-routine manual and interactive occupations. The concurrent narrowing of the gender wage gap is attributable to declining gender wage gaps within task groups rather than between them. Observable factors, such as education have grown in importance to the gender wage gap, whereas the importance of unexplained factors has strongly declined. Technological change plays no significant role in the evolution of the gender wage gap. By contrast, part-time employment exerts a substantial influence on the gender wage gap. Originality/value: Our analysis includes part-time and high-skilled workers frequently excluded from other studies. The use of individual-level data combined with task information for occupations facilitates the examination of long-run developments such as technological change both between and within task groups." (Author's abstract, IAB-Doku, © Emerald Group) ((en))
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Literaturhinweis
How important are within‐occupation task changes for wage growth? Evidence from administrative micro data (2026)
Zitatform
Bachmann, Ronald, Gökay Demir, Colin Green & Arne Uhlendorff (2026): How important are within‐occupation task changes for wage growth? Evidence from administrative micro data. In: Economica, Jg. 93, H. 372, S. 1260-1299., 2026-05-19. DOI:10.1111/ecca.70054
Abstract
"We examine how changes in task content over time condition occupational wage development. Using survey data from Germany, we document substantial heterogeneity in within-occupation changes in task content. Combining this evidence with administrative data on individual employment outcomes over a 25-year period, we find important heterogeneity in wage penalties amongst jobs that were initially routine-task-intensive. While occupations that remain (relatively) routine-intensive generate substantial wage penalties, occupations with a decreasing routine intensity experience stable or even increasing wages. These findings suggest that changing task profiles of occupations is an important adaptation mechanism to technological change. They cannot be explained by composition or cohort effects." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Systematic literature review on the digital transformation of the personnel selection process (2026)
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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
Winners and losers when firms robotize: wage effects across occupations and education (2026)
Zitatform
Barth, Erling, Marianne Røed, Pål Schøne & Janis Umblijs (2026): Winners and losers when firms robotize: wage effects across occupations and education. In: The Scandinavian Journal of Economics, Jg. 128, H. 1, S. 3-32. DOI:10.1111/sjoe.12593
Abstract
"This paper analyses the impact of robots on workers' wages in the manufacturing sector, with a particular focus on relative wages for workers with different levels of education and in different occupations. Using high-quality matched employer–employee register data with firm-level information on the introduction of industrial robots, we identify the effects of robotization on relative wages within firms. Skilled blue-collar workers with a vocational degree experience a decline in wages when firms introduce robots, while there are only small effects for the other groups of workers. These results suggest that robots are substitutes for tasks undertaken by skilled blue-collar workers in manufacturing, and furthermore that the adoption of robots contributes to a polarization of the labor market and a hollowing out of the wage distribution, rather than to skill-biased technical change." (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
Digital divide and income inequality: causal evidence from Italian provinces (2026)
Zitatform
Bergantino, Angela Stefania, Giulio Fusco, Mario Intini & Gianluca Monturano (2026): Digital divide and income inequality: causal evidence from Italian provinces. In: The Annals of Regional Science, Jg. 75, H. 1. DOI:10.1007/s00168-025-01440-z
Abstract
"The digital economy can function either as a catalyst to stimulate economic growth or else as a driver of socioeconomic inequality when its benefits are unevenly distributed. This study investigates the effect of rural digital connectivity on income inequality in Italy. Utilizing NUTS 3 panel data spanning 2014–2022, we conduct a counterfactual Difference-in-Differences approach with continuous treatment intensity to estimate the impact of introducing rural broadband coverage at speeds of 30 and 100 Mbps on multiple measures of income distribution, including the Gini, Theil, and Atkinson indices. The empirical framework incorporates a comprehensive set of socioeconomic controls, as well as provincial and time fixed effects, to account for unobserved heterogeneity and regional path dependencies. Our findings indicate that broadband expansion is significantly associated with increasing inequality, suggesting that access alone does not guarantee inclusive outcomes, particularly in localities characterized by structural fragility and limited human capital. Additional heterogeneity and spatial analyses demonstrate that these inequality effects are more evident in southern provinces and localities with a higher concentration of inner areas, where the digital divide remains more pronounced. These findings accentuate the dual role of digitalization and highlight the necessity of coordinated policy interventions that combine infrastructure investment with digital skills development, institutional capacity-building, and spatially integrated governance strategies." (Author's abstract, IAB-Doku) ((en))
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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
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
Re‐Skilling in the Age of Skill Shortage: Adult Education Rather Than Active Labor Market Policy: Special Issue: Bringing the Ecological and the Social Together in the Green Transition: A Multilevel Analysis (2026)
Zitatform
Bonoli, Giuliano, Patrick Emmenegger & Alina Felder-Stindt (2026): Re‐Skilling in the Age of Skill Shortage: Adult Education Rather Than Active Labor Market Policy. Special Issue: Bringing the Ecological and the Social Together in the Green Transition: A Multilevel Analysis. In: Regulation and governance, Jg. 20, H. 2, S. 482-494. DOI:10.1111/rego.70065
Abstract
"European economies face the task of providing the necessary skills for the “twin transition ” in a period of skill shortage. As a result, we may expect countries to reorient their labor market policy towards re-skilling. We look for evidence of a reorientation in two relevant policy fields: active labor market policy (ALMP) and adult education (AE). We explore general trends in both fields based on quantitative indicators and compare recent policy developments in four countries with strong ALMP and AE sectors: Denmark, France, Germany, and Sweden. We do not observe clear evidence of a general movement away from activation and towards re-skilling in ALMP. However, in AE, we identify several re-skilling initiatives that address skill shortages. Relying on insights from queuing theories of hiring and training, we argue that due to changes in the population targeted by ALMP, the locus of re-skilling policy is increasingly moving towards AE." (Author's abstract, IAB-Doku) ((en))
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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
Innovation and Income Inequalities: Comparing Entrepreneurial State and Standard Welfare Policies (2026)
Zitatform
Castellacci, Fulvio (2026): Innovation and Income Inequalities: Comparing Entrepreneurial State and Standard Welfare Policies. In: The B.E. Journal of Economic Analysis and Policy, Jg. 26, H. 3, S. 923-968. DOI:10.1515/bejeap-2025-0421
Abstract
"Innovation fosters economic growth and the long-run dynamics of national economies. However, recent literature shows that innovation is also a source of increasing income inequalities. Public policies face thus an important trade-off between efficiency and equity effects of innovation. What are the possible policy strategies to address this trade-off? The paper presents a model in which innovations can be developed by both private firms and public companies. Technological change increases the profit share in the long-run, exacerbating income inequalities between firms’ owners, employed workers, and the unemployed. I empirically calibrate the model for the US economy and carry out a simulation analysis to investigate the effects of different policies aimed at reducing the inequality effects of innovation. Specifically, the analysis compares two distinct policy strategies: one is based on a standard economic policy approach that increases taxes to finance welfare spending; the other is based on a new approach – the Entrepreneurial State – in which the profits of innovations developed by public R&D companies are used to finance welfare programs. The results point out the advantages and drawbacks of different strategies and show that the optimal policy strategy largely depends on the policy maker’s preferencesregarding the income distribution." (Author's abstract, IAB-Doku) ((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
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
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
Digitalization and remote work in peripheral labour markets in the Nordic region (2026)
Eythórsson, Grétar Thór; Jóhannesson, Hjalti;Zitatform
Eythórsson, Grétar Thór & Hjalti Jóhannesson (2026): Digitalization and remote work in peripheral labour markets in the Nordic region. In: Regional Science Policy & Practice, Jg. 18, H. 8. DOI:10.1016/j.rspp.2026.100321
Abstract
"This article explores the impact of technological transformation as one of the global megatrends in modern times. We focus on how new technologies, especially digitalization, have made it possible for people in peripheral places to work remotely. The focus is on the case municipalities in Iceland, especially Húsavík in in Norðurþing in northern Iceland, but also in a comparative context with remote municipalities in Norway, Sweden, and Åland. The empirical data used is from the Nordic project SUNREM (Sustainable Nordic Remote Labour Markets), which has been studying the impacts of megatrends on the sustainability of remote labour markets in four Nordic countries. Data is collected from in-depth interviews with stakeholders, policymakers, and ordinary citizens in seven selected cases in the four countries. The key research questions dealt with here are: What is the situation of remote work in selected cases in Nordic sparsely populated areas? What preconditions are needed for remote digital working? Has remote work made the labour market more sustainable? The main conclusions are that remote working exists in all of the Nordic cases investigated. The town of Húsavík in the municipality of Norðurþing in North Iceland, stands out with its well-developed remote work centres. There are clear signs that facilities where people can work and meet other remote workers are important. This enables broader recruitment into the labour market, since for example spouses can use this option, enabling them to move to more remote places. Remote work from people’s homes has some disadvantages, as pointed out by various researchers, and therefore infrastructure for remote working contributes to making labour markets in the periphery more sustainable." (Author's abstract, IAB-Doku, © 2026 The Authors. Published by Elsevier B.V. on behalf of The Regional Science Association International.) ((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
Retirement decisions in the age of COVID-19 pandemic: are older employees in digital occupations working longer? (2026)
Zitatform
Gallo, Giovanni & Amparo Nagore García (2026): Retirement decisions in the age of COVID-19 pandemic: are older employees in digital occupations working longer? In: Review of Economics of the Household, S. 1-34. DOI:10.1007/s11150-025-09827-9
Abstract
"This paper investigates the retirement response to the pandemic and to the resulting acceleration in the adoption of new technologies. Using the European Union Statistics of Income and Living Conditions datasets and leveraging the natural experiment of many workers being forced to work from home in Europe during the lockdown, we compare the retirement response of older workers in digital occupations (i.e. more exposed to the accelerated adoption of new technologies) versus non-digital occupations to detect any differences in retirement behaviour, which we interpret as digitalization effects. In addition, we analyze changes in retirement decisions by gender and geographic area. We find that retirement rates increased during COVID-19 in Europe, especially in Mediterranean countries and among women. This trend may be linked to gender occupational segregation. In Mediterranean countries, digitalization increases female retirement, likely due to challenges in balancing digital work and family responsibilities while working from home. In Eastern countries, and to a lesser extent in Northern countries, digitalization leads to postponing retirement among women, likely due to greater gender equality in unpaid work. In contrast, the retirement age for men is less affected by the pandemic with no significant differences between digital and non-digital occupations. This may exacerbate the existing gender gap in labor force participation and pension outcomes." (Author's abstract, IAB-Doku, © Springer-Verlag) ((en))
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Literaturhinweis
Endogenous Task Bundling, Skills and Automation (2026)
Zitatform
Gans, Joshua S. (2026): Endogenous Task Bundling, Skills and Automation. (NBER working paper / National Bureau of Economic Research 35211), Cambridge, Mass, 48 S.
Abstract
"Empirical measures of AI's wage effect typically hold fixed the bundle of activities a worker is paid for at its pre-AI shape. We argue that this assumption hides much of the action. When automation breaks a job apart, firms decide how to recombine the surviving activities; whether they rebundle them into one broad role or split them into specialist roles changes which surviving skills the labour market actually rewards. A skill that played no role in the pre-AI wage can become the dominant component of the post-AI wage, while a skill that anchored the pre-AI wage can disappear from the schedule. We develop an assignment model in which the priced human bundle is endogenous, and we use it to show that a fixed-bundle wage regression can mis-sign the effect of AI exposure. In general, the omitted-redesign bias has no unconditional sign: it is the residual covariance between exposure and role-specific redesign terms. Under explicit sufficient conditions, exposure-correlated unbundling loads specialist comparative-advantage premia onto the exposure coefficient, while exposure-correlated rebundling loads a different, often opposite, omitted term. The sign must therefore be measured from local post-AI partition changes rather than assumed from exposure alone." (Author's abstract, IAB-Doku) ((en))
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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
Occupational gender segregation: what can we learn from computer use trends? (2026)
Zitatform
Herzberg-Druker, Efrat (2026): Occupational gender segregation: what can we learn from computer use trends? In: Social forces, Jg. 105, H. 1, S. 299-320. DOI:10.1093/sf/soaf180
Abstract
"This study posits to an intricate interrelation between changes in occupational gender segregation (OGS) and the rise in computer use in the workplace in the United States. I posit that two contrasting mechanisms underpin this relation. Firstly, computerization has contributed to a more balanced gender distribution in certain professions, previously dominated by men, due to a decrease in physical tasks in occupations, thereby reducing OGS. Conversely, in other occupations, heightened computer use has increased Science, Technology, Engineering, and Mathematics (STEM) knowledge requirements, thus restricting women’s integration and reproducing OGS.My empirical analysis, utilizing fixed-effects regression models, lagged models, ordinary least squares (OLS) models, and mediation analysis on a comprehensive dataset of the United States Census, American Community Survey, and Occupational Information Network data, confirms a significant association between computer use and OGS. The physical attributes of occupations and their required STEM knowledge components emerge as critical factors. These contradictory mechanisms—one involving reduced physical demands and the other increased required STEM knowledge—ultimately maintain a stable OGS level." (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
"… dass wir gut 20 Jahre zu spät dran sind": Die digitale Transformation der sozialen Infrastruktur als Indikator für territorialen Anschluss in "left-behind places" (2026)
Zitatform
Heßmer, Annika & Susann Schäfer (2026): "… dass wir gut 20 Jahre zu spät dran sind": Die digitale Transformation der sozialen Infrastruktur als Indikator für territorialen Anschluss in "left-behind places". In: Raumforschung und Raumordnung, S. 1-14. DOI:10.14512/rur.3981
Abstract
"Ausgehend von Debatten zu left-behind places, digital divide, sozialer Infrastruktur und smart rurality beschäftigt sich dieser Beitrag mit der Frage, inwiefern die Digitalisierung wahrgenommene regionale Ungleichheiten verringert oder reproduziert und so als Facette der Identifikation von leftbehind places fungieren kann. Am Beispiel von öffentlichen Schulen in Thüringen untersuchen die Autorinnen, ob und inwiefern staatlich gesteuerte Digitalisierungsinitiativen und die Bewertung des allgemeinen Digitalisierungsniveaus zu einer Konvergenz wahrgenommener Unterschiede führen. Die empirische Grundlage bilden 17 leitfadengestützte Interviews mit Leitungen staatlicher Schulen in Thüringen, ergänzt durch Daten einer landesweiten quantitativen Befragung aller öffentlichen Schulen. Die Ergebnisse zeigen zeitlich verzögerte, fragmentierte Digitalisierungspfade, in denen sich Schulen, vor allem in ländlich charakterisierten Räumen, als systematisch nachgeordnet erleben. Gefühle des „Zu-spät-Dranseins“ übersetzen sich dabei in Wahrnehmungen politischer Nachrangigkeit. Diese Emotionalität entsteht durch schleppende Ausstattungsprozesse, fragile Supportstrukturen und selektive Prioritätensetzungen. Wir argumentieren, dass Digitalisierung in öffentlichen Schulen ein bedeutsamer Indikator territorialer Anerkennung ist und als institutionelle Manifestation von Emotionen gelesen werden kann, die das Left-behind-Narrativ empirisch fassbar machen." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Not a Typical Firm: Capital–Labor Substitution and Firms' Labor Shares (2026)
Hubmer, Joachim; Restrepo, Pascual;Zitatform
Hubmer, Joachim & Pascual Restrepo (2026): Not a Typical Firm: Capital–Labor Substitution and Firms' Labor Shares. In: American Economic Journal. Macroeconomics, Jg. 18, H. 2, S. 34-71. DOI:10.1257/mac.20230325
Abstract
"The US labor share has declined, especially in manufacturing and retail. Yet the labor share of a typical firm in these sectors has risen. We introduce a model where firms incur fixed costs to automate tasks. A decline in the price of capital goods used for automation reproduces the observed patterns: large firms automate tasks, reducing the aggregate labor share, while the median firm continues to operate a labor-intensive technology. When calibrating the automation fixed costs to match the observed adoption heterogeneity, the model generates the aggregate and firm-level facts quantitatively in response to lower capital prices, especially in manufacturing." (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
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
Tabellenanhang zur Studie "Digital Gender Gap. Schwerpunkt 2026: Künstliche Intelligenz" (2026)
Zitatform
Jahn, Sandy, Carola Burkert, Katharina Diener & Britta Matthes (2026): Tabellenanhang zur Studie "Digital Gender Gap. Schwerpunkt 2026: Künstliche Intelligenz". Berlin, 10 S.
Abstract
"Allgemeine Hinweise zu den Daten und Methoden sowie Interpretationshilfen. - Übersicht über die in den Analysen verwendeten Variablen und deren Operationalisierung. - Ergebnisse des Multinomialen Logit-Modells ohne Interaktionen, wenn allein die soziodemopgraphischen Merkmale berücksichtigt werden. - Ergebnisse des Multinomialen Logit-Modells mit Interaktionen, wenn allein die soziodemopgraphischen Merkmale berücksichtigt werden. - Ergebnisse des Multinomialen Logit-Modells ohne Interaktionen, wenn neben den soziodemopgraphischen Merkmale auch Merkmale des Jobs und Betriebs berücksichtigt werden. - Ergebnisse des Multinomialen Logit-Modells mit Interaktionen, wenn neben den soziodemographischen Merkmale auch Merkmale des Jobs und Betriebs berücksichtigt werden. - Ergebnisse des Multinomialen Logit-Modells ohne Interaktionen, wenn neben den soziodemopgraphischen Merkmale auch Merkmale zu Einstellungen und Kompetenzen berücksichtigt werden. - Ergebnisse des Multinomialen Logit-Modells mit Interaktionen, wenn neben den soziodemopgraphischen Merkmale auch Merkmale zu Einstellungen und Kompetenzen berücksichtigt werden." (Textauszug, IAB-Doku)
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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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Literaturhinweis
„Es geht nicht darum, was KI uns wegnehmen könnte, sondern welche Chancen entstehen“ (2026)
Zitatform
Keitel, Christiane; Britta Matthes & Katharina Grienberger (interviewte Person) (2026): „Es geht nicht darum, was KI uns wegnehmen könnte, sondern welche Chancen entstehen“. In: IAB-Forum H. 11.05.2026. DOI:10.48720/IAB.FOO.20260511.01
Abstract
"Der IAB-Job-Futuromat zeigt, welche beruflichen Tätigkeiten durch digitale Technologien und KI potenziell automatisierbar sind – und welche nicht. Im Interview erklären die Forscherinnen Britta Matthes und Katharina Grienberger, wie das Tool funktioniert, welche Berufe besonders betroffen sind und warum es bei der Berufswahl nicht um die Angst vor der Automatisierbarkeit, sondern vielmehr um Chancen gehen sollte." (Autorenreferat, IAB-Doku)
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Literaturhinweis
When Algorithms Favor the Underrepresented - Race and Gender Biases in LLM Résumé Evaluations (2026)
Zitatform
Kim, Soonyoung Chloe, Ivan Hernandez & Jingyi Li (2026): When Algorithms Favor the Underrepresented - Race and Gender Biases in LLM Résumé Evaluations. In: Journal of Personnel Psychology, Jg. 25, H. 3, S. 148-157. DOI:10.1027/1866-5888/a000388
Abstract
"The literature on name-based biases in hiring suggests pervasive discrimination, as White-sounding names receive more callbacks than Black-sounding ones. This study assesses whether AI systems, through open Large Language Models (LLMs), exhibit similar biases. The LLMs evaluated résumés on attributes such as competence and warmth, aggregating these dimensions into composite scores for each résumé. The different names attached to a résumé led to changes in evaluation, despite identical content. Statistically significant race and gender biases were found in most models for warmth and competence ratings. Unlike typical settings, Black applicants and female names were rated slightly higher through the LLMs’ evaluations. These findings highlight the importance of examining AI tools used in hiring as they may unintentionally reflect societal biases." (Author's abstract, IAB-Doku, © 2026 Hogrefe Verlag) ((en))
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Literaturhinweis
Adoption generativer KI in der Wissensarbeit: Die Rolle von beruflicher Autonomie, Arbeitsanforderungen, Tätigkeiten und Führungsverantwortung (2026)
Komorowski, Tim; Süße, Thomas;Zitatform
Komorowski, Tim & Thomas Süße (2026): Adoption generativer KI in der Wissensarbeit: Die Rolle von beruflicher Autonomie, Arbeitsanforderungen, Tätigkeiten und Führungsverantwortung. In: Zeitschrift für Arbeitswissenschaft. DOI:10.1007/s41449-026-00545-1
Abstract
"Generative KI wird als wichtige Schlüsseltechnologie diskutiert und zunehmend in der Wissensarbeit eingesetzt. Für Deutschland liegen jedoch wenige empirisch belastbare Untersuchungen dazu vor, in welchen beruflichen Kontexten generative KI intensiv zum Einsatz kommt und in welchen dies nicht der Fall ist. In diesem Beitrag greifen wir diesen Forschungsbedarf auf und berichten für die wissensintensive Berufsgruppe der Unternehmensorganisation und -strategie, inwieweit Voraussetzungen am Arbeitsplatz (berufliche Autonomie, Arbeitsanforderungen, Tätigkeiten und Führungsverantwortung) systematisch mit der Adoption generativer KI assoziiert sind. Methodisch wurden logistische Regressionsmodelle mit arbeitsbezogenen Prädiktoren und demografischen Kontrollvariablen eingesetzt. Als Datengrundlage diente die BIBB/BAuA-Erwerbstätigenbefragung 2024. Die Analysen zeigten für die untersuchte Berufsgruppe, dass besonders eine höhere berufliche Autonomie signifikant mit einer stärkeren Adoption generativer KI einherging. Praktische Relevanz : Dieser Artikel berichtet empirische Evidenz zur Verbreitung generativer KI und den Prädiktoren ihrer Nutzung. Die beobachteten Nutzungsmuster bieten empirische Einsichten für die Arbeitsgestaltung. Diese machen sichtbar, welche sozialen Ungleichheiten bei der Adoption generativer KI bestehen, entlang welcher arbeitsbezogenen Voraussetzungen sich die digitale Transformation vollzieht und wo KI-bezogene Kompetenzen voraussichtlich an Bedeutung gewinnen. Darüber hinaus bieten die Ergebnisse eine Grundlage für politische Entscheidungen und die Entwicklung regulatorischer Maßnahmen." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Sachsen-Anhalt im Strukturwandel: Digitalisierung. Mit einer Neuschätzung der Substituierbarkeitspotenziale (2026)
Kropp, Per; Fritzsche, Birgit; Theuer, Stefan;Zitatform
Kropp, Per, Stefan Theuer & Birgit Fritzsche (2026): Sachsen-Anhalt im Strukturwandel: Digitalisierung. Mit einer Neuschätzung der Substituierbarkeitspotenziale. (IAB-Regional. Berichte und Analysen aus dem Regionalen Forschungsnetz. IAB Sachsen-Anhalt-Thüringen 03/2026), Nürnberg, 40 S. DOI:10.48720/IAB.RESAT.2603
Abstract
"Die Arbeitswelt verändert sich rasant. Technische Entwicklungen bei Software, Computer oder computergesteuerten Maschinen schaffen immer neue Anwendungsmöglichkeiten. Bislang waren insbesondere Routinetätigkeiten z. B. bei Helfertätigkeiten automatisierbar. Nun sind durch produktiv nutzbare KI-Technologie auch zunehmend Nicht-Routine-Tätigkeiten von Spezialisten und Experten betroffen. Im Vergleich zu Deutschland hat Sachsen-Anhalt mehr Berufe mit einem hohen Substituierbarkeitspotenzial. Das durchschnittliche Substituierbarkeitspotenzial über alle Berufe stieg bis 2016 rasant an, seitdem jedoch in geringerem Ausmaß. Sicherheitsberufe hatten 2022 mit rund 21 Prozentpunkten einen sehr hohen Anstieg, so wie zuletzt die IT- und naturwissenschaftlichen Dienstleistungsberufe mit rund 20 Prozentpunkten. Für diese Veränderungen konnten drei Ursachen identifiziert werden: die Veränderung der Substituierbarkeitspotenziale einzelner Tätigkeiten in den Berufen, innerberufliche Veränderungen in der Bedeutung von (Kern-)Tätigkeiten und der berufliche Strukturwandel. Für Männer und Frauen sind die Substituierbarkeitspotenziale seit 2013 ähnlich gestiegen, bei Männern allerdings ausgehend von einem höheren Niveau. Künstliche Intelligenz als dominanter Aspekt der jüngsten Entwicklung macht aber eher Tätigkeiten ersetzbar, die mehrheitlich von Frauen erledigt werden. Zwischen den verschiedenen Alterskohorten gibt es insgesamt kaum Unterschiede. Lediglich in zwei Berufssegmenten haben jüngere Beschäftigte ein höheres Substitutionspotenzial – bei Verkehrs- und Logistikberufen sowie bei den IT- und naturwissenschaftlichen Dienstleistungsberufen. Auch wenn sich einige Berufe durch Digitalisierung stark verändern führt das kaum zu Beschäftigungsverlusten. Für solche Berufe und für Regionen, in denen sie vermehrt vorkommen, können jedoch höhere Weiterbildungsbedarfe vermutet werden." (Autorenreferat, IAB-Doku)
Weiterführende Informationen
Online-Anhänge zu Substituierbarkeitspotenzialen in Sachsen-Anhalt (nicht barrierefrei) -
Literaturhinweis
Strukturwandel in Thüringen: Digitalisierung. Mit einer Neuschätzung der Substituierbarkeitspotenziale (2026)
Kropp, Per; Theuer, Stefan; Fritzsche, Birgit;Zitatform
Kropp, Per, Stefan Theuer & Birgit Fritzsche (2026): Strukturwandel in Thüringen: Digitalisierung. Mit einer Neuschätzung der Substituierbarkeitspotenziale. (IAB-Regional. Berichte und Analysen aus dem Regionalen Forschungsnetz. IAB Sachsen-Anhalt-Thüringen 02/2026), Nürnberg, 42 S. DOI:10.48720/IAB.RESAT.2602
Abstract
"Die Arbeitswelt verändert sich in rasant. Technische Entwicklungen bei Software, Computer oder computergesteuerten Maschinen schaffen immer neue Anwendungsmöglichkeiten. Bislang waren insbesondere Routinetätigkeiten z. B. bei Helfertätigkeiten automatisierbar. Nun sind durch produktiv nutzbare KI-Technologie auch zunehmend nicht-routine-Tätigkeiten von Spezialisten und Experten betroffen. Im Vergleich zu Deutschland hatte Thüringen häufiger Berufe mit einem hohen Substituierbarkeitspotenzial. Das durchschnittliche Substituierbarkeitspotenzial über alle Berufe stieg bis 2016 rasant an, seitdem jedoch im geringeren Ausmaß. Sicherheitsberufe hatten 2019 mit rund 21 Prozentpunkten einen sehr hohen Anstieg, so wie zuletzt die IT- und naturwissenschaftlichen Dienstleistungsberufe mit rund 20 Prozentpunkten. Für diese Veränderungen konnten drei Faktoren identifiziert werden: die Veränderung der Substituierbarkeitspotenziale einzelner Berufe, innerberufliche Veränderungen wie bei den Kerntätigkeiten und der berufliche Strukturwandel. Für Männer und Frauen sind die Substituierbarkeitspotenziale seit 2013 ähnlich gestiegen, bei Männern allerdings von einem höheren Anfangsniveau. KI ersetzt dabei eher Tätigkeiten, die mehrheitlich von Frauen erledigt werden. Zwischen den verschiedenen Alterskohorten gibt es insgesamt kaum Unterschiede. Lediglich in zwei Berufssegmenten haben jüngere Beschäftigte ein höheres Substitutionspotenzial – bei Verkehrs- und Logistikberufen sowie bei den IT- und naturwissenschaftlichen Dienstleistungsberufen. Auch wenn sich einige Berufe durch Digitalisierung stark verändern führt das kaum zu Beschäftigungsverlusten. Für solche Berufe und für Regionen, in denen sie vermehrt vorkommen, können jedoch höhere Weiterbildungsbedarfe vermutet werden." (Autorenreferat, IAB-Doku)
Weiterführende Informationen
Online-Anhänge zu Substituierbarkeitspotenzialen in Thüringen (nicht barrierefrei) -
Literaturhinweis
Strukturwandel in Mitteldeutschland (2026)
Kropp, Per; Fritzsche, Birgit; Theuer, Stefan;Zitatform
Kropp, Per, Birgit Fritzsche & Stefan Theuer (2026): Strukturwandel in Mitteldeutschland. (IAB-Regional. Berichte und Analysen aus dem Regionalen Forschungsnetz. IAB Sachsen-Anhalt-Thüringen 01/2026), Nürnberg, 44 S. DOI:10.48720/IAB.RESAT.2601
Abstract
"Technologischer Wandel, darunter die Digitalisierung vieler Wirtschaftsbereiche, die Verschiebung von Märkten und nicht zuletzt die demografische Entwicklung prägten den beruflichen Strukturwandel in Mitteldeutschland in der letzten Dekade. Diese Entwicklung abzubilden ist das Ziel der vorliegenden Studie. Dabei liegt der Fokus auf Mitteldeutschland, seine Kreise und Arbeitsmarktregionen; zum Vergleich wurden allerdings häufig die Werte aller 16 Bundesländer herangezogen. Hinzu kommt der Vergleich für die Braunkohlereviere, zu denen das Mitteldeutsche Revier gehört. Für die Analysen wurden Indikatoren entwickelt, welche die Veränderung der Beschäftigungsstruktur in Berufssegmenten und ihre Dynamik abbilden." (Autorenreferat, IAB-Doku)
Weiterführende Informationen
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Literaturhinweis
What do software developers think about the automation of their work and its limits? Findings from a large-scale international survey (2026)
Zitatform
Krzywdzinski, Martin (2026): What do software developers think about the automation of their work and its limits? Findings from a large-scale international survey. (WZB discussion paper SP III 2026-301), Berlin, 43 S.
Abstract
"This paper investigates how software developers perceive the current and future automation of their work in the context of rapidly advancing generative and agentic AI. While existing research has primarily focused on productivity effects of specific AI coding tools in experimental settings, less is known about the broader organization of software-development work, the limits of automation, and developers' own expectations regarding labor-market outcomes. The paper addresses four research questions: the current level of automation across software-development tasks and occupations; expectations regarding future automation and its drivers; structural limits to automation; and perceived implications for job security, employability, and income. The analysis draws on an original survey of 1,731 software developers from eleven countries and six professional subgroups. The findings show that software development is currently characterized by moderate automation across all task domains, with humans still central to planning, coordination, and problem-solving. Respondents expect substantial increases in automation over the next five years, driven primarily by generative and agentic AI. However, the study also identifies important limits to automation: as automation increases, remaining tasks become less standardized, while problem-solving and collaboration demands persist. Finally, most developers remain cautiously optimistic about their labor-market prospects, although workers already operating in highly automated environments express significantly greater concerns about future job security." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
The broken ladder: AI, remote work, and early-career hiring (2026)
Lambert, Peter John; Schindler, Yannick;Zitatform
Lambert, Peter John & Yannick Schindler (2026): The broken ladder: AI, remote work, and early-career hiring. (CEP discussion paper / Centre for Economic Performance 2193), London, 81 S.
Abstract
"Is generative AI replacing junior workers? A growing literature answers yes, citing large declines in early-career hiring concentrated in GenAI-exposed occupations. We argue that this verdict is premature because GenAI exposure is strongly correlated with another post pandemic shock, working from home (WFH). Using two data sources spanning 243million new hires and 407 million online job postings, collected across the US, UK, Canada, and Australia during 2017-2025, we estimate difference-in-difference designs at the occupation, region, and firm level. When estimated separately, a two-standard-deviation increase in GenAI and WFH exposure each predicts, by 2025, a fall of around 5pp in the junior-share of new hires and around 3pp in the share of job ads requiring limited experience. Estimated jointly, the WFH effect remains, while the GenAI coefficient attenuates sharply and is often statistically indistinguishable from zero. Alternative exposure measures, residualization designs, flexible non-parametric co-treatment controls, and replacing exposure-measures with actual WFH adoption as the treatment all support our finding that WFH is a robust predictor of the decline in early-career hiring." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Automation and the risk of labor market exclusion across Europe (2026)
Zitatform
Lamperti, Fabio & Davide Castellani (2026): Automation and the risk of labor market exclusion across Europe. In: Structural Change and Economic Dynamics, Jg. 77, S. 62-76. DOI:10.1016/j.strueco.2025.12.014
Abstract
"Labor market exclusion represents a major concern in several European economies, particularly affecting highly exposed demographic groups. This paper examines the potential effect of automation technologies on the risk of being locked into protracted unemployment or inactivity, using Labour Force Survey data for the European Union 27 countries and the United Kingdom, between 2009 and 2019. Our study employs repeated cross-sections of individual-level data to compute probabilities of exclusion outcomes due to automation adoption, controlling for several individual, macroeconomic, and region-specific characteristics, and for potential selection mechanisms. Findings highlight that, on average, the adoption of new automation technologies is associated with a higher probability of being inactive. This is consistent with the view that automation may exacerbate job insecurity, psychological discouragement, and detachment from job-seeking. This relationship is heterogeneous across demographic groups, with younger individuals being relatively more affected." (Author's abstract, IAB-Doku, © 2025 The Authors. Published by Elsevier B.V.) ((en))
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Literaturhinweis
Can artificial intelligence replace human labour? Global evidence from 65 countries (2026)
Zitatform
Le, Quoc Hoi & Cong Minh Huynh (2026): Can artificial intelligence replace human labour? Global evidence from 65 countries. In: Applied Economics, S. 1-17. DOI:10.1080/00036846.2026.2688439
Abstract
"Despite growing concerns about technological unemployment, existing evidence remains fragmented, country-specific, and largely silent on whether productivity growth driven by artificial intelligence (AI) can decouple from human labour globally. This paper provides new global evidence on AI-labour substitution using an unbalanced panel of 65 countries over 2013–2023. Building on a task-based theoretical framework, we conceptualize AI as a potentially labour-replacing technology that reallocates economic tasks from humans to machines. We test this framework empirically using AI development, combined with cross-country data on labour force participation, productivity, human capital, and institutional characteristics. The results reveal three main findings. First, AI development is associated with a statistically significant decrease in labour force participation, indicating the presence of labour displacement effects. Second, AI simultaneously raises productivity, pointing to productivity-labour decoupling, whereby output growth becomes less dependent on human labour. Third, human capital and governance quality play crucial moderating roles in weakening labour-displacing effects of AI. Remarkably, dynamic panel estimates suggest that AI induces short-run labour displacement, while long-run outcomes depend on countries’ capacity to adapt through education and institutional quality." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Automation, Trade Unions and Atypical Employment (2026)
Zitatform
Lewandowski, Piotr & Wojciech Szymczak (2026): Automation, Trade Unions and Atypical Employment. In: Industrial Relations, Jg. 65, H. 3, S. 378-396. DOI:10.1111/irel.70017
Abstract
"We study the effect of automation technologies—industrial robots, software and databases—on the incidence of involuntary atypical employment in 13 EU countries between 2006 and 2018. Robots do not affect the total employment rate but significantly increase the involuntary atypical employment share, mainly through fixed-term work. Software and databases increase total employment and are neutral for atypical employment. Higher trade union density mitigates the robots' impact on atypical employment, while employment protection legislation plays no role. Using historical decompositions, we attribute 1–2 percentage points of a 15% average atypical employment share in our sample to automation." (Author's abstract, IAB-Doku, Published by arrangement with John Wiley & Sons) ((en))
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Literaturhinweis
Skill-biased technological change in the age of AI: a theoretical analysis of automation and inequality (2026)
Li, Te; Nichols, Hadrian;Zitatform
Li, Te & Hadrian Nichols (2026): Skill-biased technological change in the age of AI: a theoretical analysis of automation and inequality. In: Economics of Innovation and New Technology, S. 1-29. DOI:10.1080/10438599.2026.2649376
Abstract
"This paper develops a general equilibrium model to analyze how artificial intelligence (AI)–driven automation reshapes productivity, labor markets, and income distribution. The model features heterogeneous workers, endogenous automation decisions, and irreversible skill investment choices, allowing a unified examination of displacement, complementarity, and skill-supply responses. Automation substitutes for labor in routine tasks, reducing demand and wages for low-skilled workers, while simultaneously enhancing productivity in complex tasks where AI complements high-skilled labor. As a result, aggregate output and total welfare increase, but income inequality widens and the skill premium rises. A key finding is that technological progress is not Pareto improving: due to heterogeneous and irreversible skill investment costs, workers below a critical ability threshold experience absolute welfare losses despite overall economic growth. This generates a ‘growth paradox’ in which productivity gains coexist with immiseration for vulnerable groups. The paper further evaluates three policy instruments – redistributive taxation, education subsidies, and technology policy – and derives conditions under which each improves social welfare. Education subsidies emerge as the most effective tool for mitigating inequality while preserving efficiency, whereas excessive automation may arise when private incentives diverge from social optima." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Economics of Human and AI Collaboration: When is Partial Automation More Attractive than Full Automation? (2026)
Li, Wensu; Lyu, Harry; Goehring, Brian C.; Aboutorabi, Atin; Qian, Kaizhi; Thompson, Neil; Fleming, Martin;Zitatform
Li, Wensu, Atin Aboutorabi, Harry Lyu, Kaizhi Qian, Martin Fleming, Brian C. Goehring & Neil Thompson (2026): Economics of Human and AI Collaboration: When is Partial Automation More Attractive than Full Automation? (arXiv papers 2603.29121), 57 S.
Abstract
"This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation. On the supply side, we estimate an AI production function via scaling-law experiments linking performance to data, compute, and model size. Because AI systems exhibit predictable but diminishing returns to these inputs, the cost of higher accuracy is convex: good performance may be inexpensive, but near-perfect accuracy is disproportionately costly. Full automation is therefore often not cost-minimizing; partial automation, where firms retain human workers for residual tasks, frequently emerges as the equilibrium. On the demand side, we introduce an entropy-based measure of task complexity that maps model accuracy into a labor substitution ratio, quantifying human labor displacement at each accuracy level. We calibrate the framework with O*NET task data, a survey of 3,778 domain experts, and GPT-4o-derived task decompositions, implementing it in computer vision. Task complexity shapes substitution: low-complexity tasks see high substitution, while high-complexity tasks favor limited partial automation. Scale of deployment is a key determinant: AI-as-a-Service and AI agents spread fixed costs across users, sharply expanding economically viable tasks. At the firm level, cost-effective automation captures approximately 11% of computer-vision-exposed labor compensation; under economy-wide deployment, this share rises sharply. Since other AI systems exhibit similar scaling-law economics, our mechanisms extend beyond computer vision, reinforcing that partial automation is often the economically rational long-run outcome, not merely a transitional phase." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
AI adoption among German firms (2026)
Zitatform
Licht, Thomas & Klaus Wohlrabe (2026): AI adoption among German firms. In: Structural Change and Economic Dynamics, S. 1-5. DOI:10.1080/13504851.2026.2669986
Abstract
"This paper examines the adoption of Artificial Intelligence (AI) among German firms using firm-level data from the ifo Business Survey. Between 2023 and 2024, AI usage more than doubled from 13.3% to 27%, with substantial variation across sectors and firm sizes. Large firms in manufacturing and services lead adoption, while SMEs and construction companies lag behind. Using linear probability models with fixed effects for industry and firm size, we find that managerial characteristics – particularly risk tolerance and patience – significantly influence adoption decisions. Firms led by more risk-tolerant and patient managers are more likely to implement AI. Most firms expect notable productivity gains from AI – approximately 8% overfive years – though expectations vary by sector. Our findings providenew firm-level evidence by linking validated measures of managerial preferences to AI adoption decisions, highlighting both the economic potential and the structural barriers to broader diffusion." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Do robots decrease humans’ wages? (2026)
Zitatform
Logchies, Thomas, Tom Coupé & W. Robert Reed (2026): Do robots decrease humans’ wages? In: Applied Economics Letters, Jg. 33, H. 13, S. 2194-2198. DOI:10.1080/13504851.2025.2466748
Abstract
"While there are studies that show a positive or negative impact of robots on wages, a meta-analysis of 2,586 estimates from 52 studies in this paper finds that when one looks at the literature as a whole, there is no clear evidence of a sizable impact of robots on wages." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
The collaboration code: how humans and AI work together across millions of conversations on job tasks (2026)
Lynch, Patrick D.;Zitatform
Lynch, Patrick D. (2026): The collaboration code: how humans and AI work together across millions of conversations on job tasks. In: Frontiers in artificial intelligence, Jg. 9. DOI:10.3389/frai.2026.1801235
Abstract
"As generative AI transforms work across all fields, understanding human–AI collaboration is critically important to the future of work. This study analyzed behavioral data from millions of Claude AI conversations mapped to occupational tasks via the O*NET taxonomy to examine whether human–AI collaboration patterns are consistent with expectations derived from traditional technology adoption frameworks. Utilizing Technology Acceptance Model, Protection Motivation Theory, Social Exchange Theory, and Socio-Technical Systems Theory as parallel lenses on post-adoptive behavior, the analysis examined how interaction patterns predict AI usage intensity, risk-management behaviors, and the development of cognitive partnerships. Results demonstrated that collaborative interaction patterns, including iterative refinement and learning-oriented exchanges, predicted significantly higher AI usage intensity, consistent with expectations derived from TAM. Contrary to Protection Motivation Theory predictions, risk-management behaviors such as validation and feedback loops were positively associated with usage intensity, suggesting these function as protective engagement strategies that enable continued use, not resistance mechanisms. Analysis of extended thinking mode usage suggested distinct trust pathways: delegation trust, where users rely on AI’s autonomous reasoning, versus collaboration trust, built through iterative dialogue, although sensitivity analyses indicate that task complexity may partly account for this divergence. A cross-level reversal emerged whereby augmentation patterns positively predicted usage intensity at the task level but negatively predicted user adoption at the cluster level, indicating that depth and breadth of AI use follow different dynamics. These findings challenge assumptions that caution suppresses AI use and suggest that effective human–AI partnership supports cognitive partnerships through multiple complementary pathways. For practitioners and leaders, supporting diverse ways of interacting with AI can be important for successful integration. The analyses drew on the Anthropic Economic Index dataset of 3,365 occupational tasks grouped into 593 task clusters, with model sample sizes varying by hypothesis." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Meinungsmonitor Künstliche Intelligenz – Die Gestaltung des KI-Einsatzes am Arbeitsplatz. Zwischen autonomer Nutzung und Arbeitsentlastung: Kurzbericht einer Conjoint-Studie (2026)
Zitatform
Lünich, Marco, Birte Keller, Florian Kruse & Frank Marcinkowski (2026): Meinungsmonitor Künstliche Intelligenz – Die Gestaltung des KI-Einsatzes am Arbeitsplatz. Zwischen autonomer Nutzung und Arbeitsentlastung: Kurzbericht einer Conjoint-Studie. (SocArXiv papers), 11 S. DOI:10.31235/osf.io/y3fx5_v1
Abstract
"Der Kurzbericht präsentiert zentrale Ergebnisse einer Conjoint-Studie des Projekts Meinungsmonitor Künstliche Intelligenz 3.0, die im Mai 2026 durchgeführt wurde. Analysiert werden Präferenzen von rund 2.800 abhängig Beschäftigten in Deutschland für unterschiedliche Gestaltungsformen von Künstlicher Intelligenz (KI) am Arbeitsplatz. Die Ergebnisse zeigen, dass für die Bewertung von KI-Systemen nicht allein ihre technische Leistungsfähigkeit entscheidend ist. Am stärksten prägt die Autonomie bei der KI-Nutzung die Präferenzentscheidung: Beschäftigte bevorzugen Systeme, die sie nach eigenem Ermessen nutzen können, während eine verpflichtende Nutzung deutlich abgelehnt wird. Ebenfalls bedeutsam sind die Auswirkungen auf den Arbeitsumfang und der Weiterbildungsbedarf. Präferiert werden KI-Systeme, die Arbeit entlasten oder zumindest keine zusätzliche Arbeitsbelastung erzeugen und mit einer praxisnahen, zeitlich überschaubaren Qualifizierung verbunden sind. Die Qualität der KI-Ergebnisse, die Überwachung der KI-Nutzung und mögliche Auswirkungen auf soziale Kontakte beeinflussen die Auswahlentscheidung ebenfalls, sind im direkten Vergleich jedoch weniger ausschlaggebend. Insgesamt verdeutlichen die Befunde, dass die Einführung von KI am Arbeitsplatz nicht nur eine technische, sondern auch eine arbeits- und organisationspolitische Gestaltungsaufgabe ist." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Perceived Industry Resilience to AI: A Two-Wave Survey of Employees Across 15 Sectors (2026)
Zitatform
Lünich, Marco, Birte Keller & Frank Marcinkowski (2026): Perceived Industry Resilience to AI: A Two-Wave Survey of Employees Across 15 Sectors. (SocArXiv papers), 83 S. DOI:10.31235/osf.io/m8de3_v1
Abstract
"Artificial intelligence (AI) is expected to transform industries unevenly, yet little is known about how employees themselves evaluate the capacity of their industries to adapt. This study introduces perceived industry resilience to AI as a meso-level construct linking individual perceptions to sectoral conditions, and examines its level, sectoral differences, short-term change, and individual-level correlates. We draw on a two-wave repeated cross-sectional survey of employed respondents in Germany (T1: July 2025; T2: January/February 2026; pooled N = 1,707) covering 15 industry sectors, with research questions and hypothesis preregistered between the two waves. Measurement invariance testing established the cross-wave comparability of the outcome. Survey-weighted models and cluster-robust regressions with equivalence tests were used for inference. Perceived AI resilience was generally low and showed no evidence of overall change between waves. Industries differed markedly: employees in information technology and data processing reported the highest resilience perceptions, employees in administration and public service as well as social services the lowest, and these differences showed no evidence of change across waves. Employees who saw the benefits of AI for their own profession as outweighing its risks, and who regarded political institutions as responsive, reported higher perceived resilience in both waves. The findings suggest that resilience perceptions are anchored in these occupational and political evaluations, with implications for sector-specific workforce policy and communication." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Monitor Digitale Arbeitsgesellschaft – Kurzbericht der dritten Befragungswelle. Nach dem KI-Aufmerksamkeitsanstieg: Zwischen Stabilisierung, Skepsis und Regulierungsanforderungen (2026)
Zitatform
Marcinkowski, Frank, Birte Keller, Marco Lünich & Florian Golo Flaßhoff (2026): Monitor Digitale Arbeitsgesellschaft – Kurzbericht der dritten Befragungswelle. Nach dem KI-Aufmerksamkeitsanstieg: Zwischen Stabilisierung, Skepsis und Regulierungsanforderungen. (SocArXiv papers), 11 S. DOI:10.31235/osf.io/zcxje_v1
Abstract
"Der Kurzbericht präsentiert zentrale Ergebnisse der dritten von vier repräsentativen Befragungswellen der Studie Monitor Digitale Arbeitsgesellschaft, die im Rahmen des Forschungsprojekts Meinungsmonitor Künstliche Intelligenz 3.0 durchgeführt wurde. Grundlage sind die Angaben von 1.647 Befragten (928 Erwerbstätige, 719 Nichterwerbstätige) im Januar 2026. Die dritte Befragungswelle zeigt, dass das im Verlauf des Jahres 2025 gestiegene Interesse an KI zu Beginn des Jahres 2026 wieder leicht zurückgeht, während die subjektive Einschätzung des eigenen Wissens auf dem erreichten Niveau stabil bleibt. Gleichzeitig nehmen Vorbehalte gegenüber bestimmten gesellschaftlichen Einsatzfeldern zu, insbesondere in Bereichen mit Bezug zu staatlichen Sicherheitsaufgaben und politischen Entscheidungen. In der Arbeitswelt bleibt die Bewertung der Auswirkungen von KI auf den eigenen Beruf insgesamt stabil und leicht nutzenorientiert. Die berufliche Nutzung von KI stagniert zuletzt weitgehend. Viele Beschäftigte berichten weiterhin keine grundlegenden Veränderungen ihrer Tätigkeitsanforderungen und nur geringe persönliche Jobunsicherheit, während die Sorge, dass KI menschliche Arbeit allgemein ersetzen oder grundlegend verändern könnte, weiterhin deutlich stärker verbreitet ist als die persönliche Jobunsicherheit. Erstmals wird zudem ein besonderes Schlaglicht auf die Regulierungspräferenzen der Bevölkerung geworfen: Während staatliche Eingriffe bei arbeitsmarktbezogenen Folgen breite Unterstützung finden, werden Eingriffe in wirtschaftliche Freiheiten und steuerpolitische Maßnahmen deutlich ambivalenter bewertet. Insgesamt deutet sich eine stärkere Ausdifferenzierung und Festigung der Einstellungen gegenüber KI an." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Artificial Intelligence in Firms: A Synthesis of Micro-Level Evidence on Productivity, Work, and Skills (2026)
Marie-Christine, Fregin; Danique, Eijkenboom; Pelin, Özgül -. Persyn; Mantej, Pardesi; Nicholas, Rounding;Zitatform
Marie-Christine, Fregin, Eijkenboom Danique, Özgül -. Persyn Pelin, Pardesi Mantej & Rounding Nicholas (2026): Artificial Intelligence in Firms: A Synthesis of Micro-Level Evidence on Productivity, Work, and Skills. (ROA Policy Brief 2026,2), Maastricht, 13 S. DOI:10.26481/umarpb.2026002e
Abstract
"AI is reshaping work through shifts in tasks and skills rather than widespread job losses. This policy brief synthesises micro-level evidence on how AI affects productivity, work, and skills within organisations. It finds that generative AI is already embedded in everyday business practice, with productivity gains most visible among less experienced workers. Outcomes vary significantly depending on how AI is integrated into work processes, governance structures, and training. As autonomous AI systems become more prevalent, questions of responsibility, human oversight, and the distribution of risks and benefits grow more pressing. Successful implementation requires not just technological investment, but deliberate choices about leadership, work design, and employee involvement." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Improving the effects of industrial robot adoption on employment, total factor productivity, and real wages in 52 world economies and OECD members (2026)
Zitatform
Matsuki, Takashi (2026): Improving the effects of industrial robot adoption on employment, total factor productivity, and real wages in 52 world economies and OECD members. In: Review of world economics, Jg. 162, H. 2, S. 417-448. DOI:10.1007/s10290-025-00626-z
Abstract
"This study investigates the effects of industrial robot adoption in the production process on unemployment rate, employment ratio in manufacturing, and total factor productivity (TFP) growth in 52 countries, and real wage growth in 31 and 20 OECD member countries for 2007–2019. The operating stock of robots per employee significantly impacts these variables; robot adoption lowers the unemployment rate and raises TFP and real wage growth. However, it reduces the employment ratio in manufacturing. In addition, the slight but significant positive contribution of robot adoption is observed only in the 90-percentile (top 10-percentile) of the real wage distribution. Interestingly, workers in the bottom and top tails (10- and 90-percentiles) of the wage distribution asymmetrically benefit from robotization. The industry ratio of value-added improves the labor market by reducing the unemployment rate and raising the employment ratio in manufacturing, TFP growth, and real wage growth. The information and communication technology (ICT) development also positively contributes to the employment ratio in Asia’s manufacturing, TFP growth, and real wage growth." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
AI innovation and labor market polarization: Evidence from European regions (2026)
Zitatform
Minniti, Antonio, Klaus Prettner & Francesco Venturini (2026): AI innovation and labor market polarization: Evidence from European regions. In: Economics Letters, Jg. 268. DOI:10.1016/j.econlet.2026.113180
Abstract
"We study whether AI innovation changes labor-market polarization by weakening the relative position of high-skilled workers. We develop a CES production framework showing how AI affects the wage-cost ratio between high- and low-skilled workers through wage and employment margins. We test the model’s predictions using European regional labor-market data and find that AI reduces the relative wage-cost ratio by nearly 7 percent on average, with effects broadly split between wage and employment adjustments. These results are primarily driven by regions in countries highly specialized in AI technologies. Overall, our evidence suggests that AI may counteract the polarization effects associated with over two centuries of technological change." (Author's abstract, IAB-Doku, © 2026 The Authors. Published by Elsevier B.V.) ((en))
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Literaturhinweis
Artificial Intelligence and Productivity in Europe (2026)
Misch, Florian; Park, Ben; Sher, Galen; Pizzinelli, Carlo;Zitatform
Misch, Florian, Ben Park, Carlo Pizzinelli & Galen Sher (2026): Artificial Intelligence and Productivity in Europe. (CESifo working paper 12401), München, 37 S.
Abstract
"The discussion on Artificial Intelligence (AI) often centers around its impact on productivity, but macroeconomic evidence for Europe remains scarce. Using the Acemoglu (2024) approach we simulate the medium-term impact of AI adoption on total factor productivity for 31 European countries. We compile many scenarios by pooling evidence on which tasks will be automatable in the near term, using reduced-form regressions to predict AI adoption across Europe, and considering relevant regulation that restricts AI use heterogeneously across tasks, occupations and sectors. We find that the medium-term productivity gains for Europe as a whole are likely to be modest, at around 1 percent cumulatively over five years. While economically still moderate, these gains are still larger than estimates by Acemoglu (2024) for the US. They vary widely across scenarios and countries and are substantially larger in countries with higher incomes. Furthermore, we show that national and EU regulations around occupation-level requirements, AI safety, and data privacy combined could reduce Europe's productivity gains by over 30 percent if AI exposure were 50 percent lower in tasks, occupations and sectors affected by regulation." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Predicting and Preventing Turnover in Industry 4.0: Understanding the Impact of Artificial Intelligence Adoption on Employee Turnover (2026)
Moon, Young-Kook; Mitropoulos, Tanya;Zitatform
Moon, Young-Kook & Tanya Mitropoulos (2026): Predicting and Preventing Turnover in Industry 4.0: Understanding the Impact of Artificial Intelligence Adoption on Employee Turnover. In: Human resource development quarterly, S. 1-11. DOI:10.1002/hrdq.70018
Abstract
"With the increasing adoption of Artificial Intelligence (AI) in the workplace, employees' career paths have become more diverse and less predictable in the era of Industry 4.0. As technological transformations accelerate, employee turnover patterns are also changing, as reflected in the growing prevalence of occupational transitions and large-scale layoffs. These shifts highlight the need for more developmental approaches to understanding and managing retention. To address this issue, the present paper examines how AI implementation influences employee turnover by focusing on both the motives and psychological states underlying the withdrawal process. Drawing on proximal withdrawal states theory, the paper provides a deeper understanding of the dynamic nature of employee withdrawal patterns. This perspective elucidates the withdrawal process shaped by AI adoption, emphasizing that employees' responses to technological disruption are diverse in both psychological and behavioral terms rather than uniform. Furthermore, the paper proposes Human Resource Development (HRD)-based interventions aimed at preventing undesirable turnover through employee education and career development initiatives. By integrating these elements, we introduce an HRD-focused framework for addressing turnover issues associated with AI adoption in a more developmental and sustainable manner. Finally, the paper outlines future research directions for empirically testing the proposed theoretical mechanisms and intervention strategies within the rapidly evolving technological landscape of work." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
KI in Betrieben: Mehr Ausbildung – aber Weiterbildung zunehmend für anspruchsvollere Tätigkeiten (2026)
Zitatform
Muehlemann, Samuel (2026): KI in Betrieben. Mehr Ausbildung – aber Weiterbildung zunehmend für anspruchsvollere Tätigkeiten. In: Ifo-Schnelldienst, Jg. 79, H. 03, S. 09-13.
Abstract
"Auf Basis des BIBB-Betriebspanels werden die Folgen der Einführung von Künstlicher Intelligenz für die betriebliche Aus- und Weiterbildung in Deutschland analysiert. Die Ergebnisse deuten darauf hin, dass KI-einführende Ausbildungsbetriebe im Durchschnitt rund 14% mehr neue Auszubildende einstellen. Das spricht für verstärkte Investitionen in den internen Kompetenzaufbau im Zuge des technologischen Wandels. Zugleich verschiebt sich die betriebliche Weiterbildung zugunsten hochqualifizierter Tätigkeiten, während Beschäftigte in Fachkraft- und einfachen Tätigkeiten seltener teilnehmen. Daraus ergibt sich das Risiko einer kumulativen Benachteiligung Geringqualifizierter. Es werden drei wirtschaftspolitische Handlungsfelder abgeleitet: die bessere Integration Jugendlicher mit geringen schulischen Qualifikationen in die duale Ausbildung, eine zielgerichtete Weiterbildungsförderung für Geringqualifizierte sowie schnellere Aktualisierungszyklen für Ausbildungsordnungen und Rahmenlehrpläne." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Machine learning for labor market matching (2026)
Zitatform
Mühlbauer, Sabrina & Enzo Weber (2026): Machine learning for labor market matching. In: Machine learning with applications, Jg. 23, 2026-02-03. DOI:10.1016/j.mlwa.2026.100861
Abstract
"This paper develops a large-scale machine learning framework to improve labor market matching using rich administrative data. Matching is defined as a job seeker entering employment in a specific occupational field. We exploit comprehensive employment biographies from Germany, covering individual characteristics and job-related information, to estimate employment probabilities across occupations and generate personalized job recommendations. The contribution lies in demonstrating why machine learning methods are particularly well suited for administrative labor market data and outperform traditional statistical approaches. We compare logit, ordinary least squares (OLS), k-nearest neighbors, and random forest (RF). RF consistently achieves the highest predictive performance. Its advantage is rooted in key methodological properties: RF builds an ensemble of decision trees trained on bootstrap samples, introduces random feature selection at each split, and aggregates predictions through majority voting. This enables RF to capture nonlinear relationships and complex interactions, remain robust in high-dimensional settings, and reduce overfitting — features that are particularly relevant for heterogeneous and imbalanced administrative data. Compared to conventional models, RF better exploits the full informational content of employment histories, especially when estimating on all employment spells rather than restricting the sample to unemployment-to-employment transitions. The sample comprises approximately 55 million spells, representing about 6 percent of the German workforce from 2012 to 2018. Our results suggest that ML-based matching, relative to standard statistical approaches, could hypothetically reduce the unemployment rate by up to 0.3 percentage points, highlighting the practical relevance of RF-based decision support for labor market policy." (Author's abstract, IAB-Doku, © Elsevier) ((en))
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Literaturhinweis
Fehler mit System. Scheiternde Digitalisierung in der Arbeit (2026)
Nies, Sarah ; Kern, Annemarie; Neumer, Judith; Heim, Jana; Rieger, Samuel; Schneiß, Daniel ; Heiland, Heiner ; Sommer, Matthias; Zierott, Lea P.; Brödner, Peter; Dolata, Ulrich; Pongratz, Hans J. ; Altendorf-Schöpke, Antonia; Petersen, Benjamin Henry; Schneider, Pauline ; Schulz-Schaeffer, Ingo; Ritter, Tobias; Wiggert, Kevin; Nies, Sarah ; Heiland, Heiner ; Clausnitzer, Tim; Kuhlmann, Martin; Meister, Martin; Bolte, Annegret; Faust, Michael; Ferschli, Benjamin; Sommer, Matthias; Heinlein, Michael ; Rieger, Samuel; Gerber, Christine ; Kern, Annemarie;Zitatform
Nies, Sarah, Heiner Heiland, Annemarie Kern, Samuel Rieger & Matthias Sommer (Hrsg.) (2026): Fehler mit System. Scheiternde Digitalisierung in der Arbeit. (Arbeit - Organisation - Politik), Weinheim: Beltz Juventa, 360 S. DOI:10.3262/978-3-7799-9024-6
Abstract
"Das große Versprechen digitaler Technologien liegt in der technischen Bewältigung betrieblicher und ökonomischer Widersprüche. Die Betrachtung konkreter Digitalisierungsprojekte offenbart jedoch: Digitalisierung scheitert ständig. Die Beiträge des Bandes diskutieren die Diskrepanz von Digitalisierungsversprechen und dem allgegenwärtigen Phänomen scheiternder Digitalisierungen, die zugrundeliegenden Widersprüche zwischen technischer Rationalität und arbeitsweltlichen Realitäten und die Folgen für Machtverhältnisse, Interessenkonflikte und Belastungskonstellationen." (Verlagsangaben, IAB-Doku)
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Literaturhinweis
How local labour market skill relatedness and size moderate the impacts of automation (2026)
Zitatform
Njekwa Ryberg, Peter (2026): How local labour market skill relatedness and size moderate the impacts of automation. In: Regional Studies, Jg. 60, H. 1. DOI:10.1080/00343404.2025.2598031
Abstract
"This paper examines how local labour market skill relatedness and size moderate the impacts of automation on occupations across Swedish local labour markets. Using administrative data and a spatially explicit risk of automation measure that accounts for regional differences in occupational task contents, it finds a negative association between automation and employment growth and wage income growth for non-metropolitan occupations between 2011 and 2021. Skill relatedness and labour market size mitigate these negative relationships. In contrast, no negative associations are found for metropolitan occupations. Due to their higher shares of non-automatable tasks, they are more resilient to adverse automation effects." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Drei Jahre ChatGPT: Auswirkungen von LLMs auf den deutschen Arbeitsmarkt: Teil des Zeitgesprächs "Arbeitszeit im Wandel - Wie sich Wohlstand trotz sinkenden Arbeitskräfteangebots sichern lässt" (2026)
Normann, Morten Grinna;Zitatform
Normann, Morten Grinna (2026): Drei Jahre ChatGPT: Auswirkungen von LLMs auf den deutschen Arbeitsmarkt. Teil des Zeitgesprächs "Arbeitszeit im Wandel - Wie sich Wohlstand trotz sinkenden Arbeitskräfteangebots sichern lässt". In: Wirtschaftsdienst, Jg. 106, H. 4, S. 301-302. DOI:10.2478/wd-2026-0073
Abstract
"Technologischer Wandel ist ein kontinuierlicher Prozess, der Arbeitsmärkte seit jeher beeinflusst. Schon Ökonomen wie David Ricardo warnten offen vor einer Zukunft, in der rasante technologische Entwicklungen (menschliche) Arbeit überflüssig und wertlos machen würden (Ricardo, 1821 in Hollander, 2019). Die neueste Technologie, die als Bedrohung für den Arbeitsmarkt gilt, ist die Entwicklung von Werkzeugen der künstlichen Intelligenz, insbesondere von Large Language Models (LLMs)." (Autorenreferat, IAB-Doku)
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Literaturhinweis
AI Skill Demand and Employment Stratification in Copyright-Intensive Information Industries: Evidence from Job Postings and Industry-Level Employment Data (2026)
Palmedo, Michael; Safner, Ryan; Lutes, Brent;Zitatform
Palmedo, Michael, Brent Lutes & Ryan Safner (2026): AI Skill Demand and Employment Stratification in Copyright-Intensive Information Industries: Evidence from Job Postings and Industry-Level Employment Data. In: Work and occupations, Jg. 53, H. 3, S. 707-744. DOI:10.1177/07308884261447535
Abstract
"This paper examines how employer demand for workers skilled in the use of artificial intelligence (AI) is associated with employment patterns in copyright-intensive industries within the U.S. information sector. Using panel data from the Census Bureau's Quarterly Workforce Indicators combined with data on AI-related job postings, we analyze how the relationship between employer demand for AI skills and employment levels varies across worker groups between 2018 and 2024. Furthermore, we document changes in this relationship following the widespread introduction of generative AI in late 2022. We find that employment in copyright-reliant information industries declined relative to other industries after late 2022, and that the association between AI-related job postings and employment is highly heterogeneous. Initial findings indicate that employment relationships were negative or statistically insignificant for workers who had not completed a four-year college degree, for women, and for the youngest and oldest age cohorts. A positive relationship existed for college educated workers, men, and prime-age workers, though this weakened in the periods after the introduction of generative AI. These findings provide early insights into how demand for generative AI skills may reshape labor market stratification within creative and information-intensive industries and underscore the importance of examining technological change in future research." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Artificial intelligence applications supporting women’s career development: a scoping review (2026)
Portell-Fonolla, Sara ; El Fassi, Yasmina ; Gaspar, Augusta D. ; Pinto, Joana Carneiro ; Correia, Luis ;Zitatform
Portell-Fonolla, Sara, Yasmina El Fassi, Augusta D. Gaspar, Luis Correia & Joana Carneiro Pinto (2026): Artificial intelligence applications supporting women’s career development: a scoping review. In: International Journal for Educational and Vocational Guidance, S. 1-24. DOI:10.1007/s10775-026-09807-0
Abstract
"Artificial intelligence (AI) is increasingly integrated into career guidance and organisational decision systems, yet empirical evidence on applications designed to support women’s career development remains limited. Following the PRISMA extension for scoping reviews (PRISMA-ScR) and a preregistered protocol, we searched seven databases (plus backward and forward citation searching) and synthesised 13 empirical studies published between 2018 and 2025. Using inductive thematic analysis, we identified three functional domains: (1) bias mitigation and representation (e.g. auditing gendered language and platform-level disparities), (2) skills development and empowerment (e.g. AI-supported learning and writing interventions) and (3) career pathways and retention (e.g. matching and attrition-risk modelling). The evidence base was concentrated in system-facing applications that detect or shape inequities within recruitment, evaluation and exposure systems; fewer studies evaluated individual-facing developmental support, and sustained career outcomes were rarely measured. Formal theory use was limited, with only a small minority of studies explicitly drawing on established frameworks; reporting on ethics, transparency and governance was inconsistent. We suggest that research prioritises longitudinal and theory-informed evaluations, including intersectionality-informed analyses, and assess downstream impacts on women’s career trajectories alongside robust governance and accountability practices." (Author's abstract, IAB-Doku, © Springer-Verlag) ((en))
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Literaturhinweis
Human-centred digital transitions and skill mismatches in European workplaces (2026)
Zitatform
Pouliakas, Konstantinos & Giulia Santangelo (2026): Human-centred digital transitions and skill mismatches in European workplaces. (CEDEFOP working paper series / European Centre for the Development of Vocational Training 2026,01), Luxembourg, 163 S. DOI:10.2801/9894877
Abstract
"New digital and artificial intelligence technologies are fast reshaping skill requirements in the EU labour market, fostering skill mismatches. There are marked concerns about the potentially adverse consequences of automation and AI on employment, as well as the lagging competitiveness of EU economies as individuals’ upskilling or reskilling is failing to adapt. To deepen understanding of how digitalisation is affecting the nature of work and skill mismatches in EU labour markets, Cedefop carried out the second wave of the European skills and jobs survey in 2021. In this special edition of Cedefop’s working paper series, ten original, short contributions have been drafted in which researchers explore in depth, for the first time, the ESJS2 microdata. The publication presents a wealth of focused and robust empirical analyses, covering a wide range of different issues on how the digital transition is affecting jobs, skills and training in Europe." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Mehr Arbeit, weniger Jobs? Konsequenzen der KI-Technologieeinführung: Teil des Zeitgesprächs "Arbeitszeit im Wandel - Wie sich Wohlstand trotz sinkenden Arbeitskräfteangebots sichern lässt" (2026)
Zitatform
Pusch, Toralf, Muhamed Kudic & Akua Franziska Agyepong (2026): Mehr Arbeit, weniger Jobs? Konsequenzen der KI-Technologieeinführung. Teil des Zeitgesprächs "Arbeitszeit im Wandel - Wie sich Wohlstand trotz sinkenden Arbeitskräfteangebots sichern lässt". In: Wirtschaftsdienst, Jg. 106, H. 4, S. 296-300. DOI:10.2478/wd-2026-0072
Abstract
"Steigt durch die zunehmende Einführung von Künstlicher Intelligenz (KI) die Arbeitsbelastung? Und sind Arbeitsplätze bedroht? Daten der WSI-Betriebsrätebefragung zeigen, dass sich KI in ihrer Wirkung – zumindest in der frühen Einführungsphase – von anderen digitalen Technologien zu unterscheiden scheint. Statt einer bei anderen Technologien häufiger auftretenden Arbeitsverdichtung berichteten viele Betriebsräte eher von Arbeitsentlastung, und nach einem Beobachtungszeitraum von zwei Jahren zeigte sich per Saldo ein leichter Stellenaufbau." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Künstliche Intelligenz (KI) im Geschäftsprozessmanagement der Sozialwirtschaft: Anwendungsfälle, Implementierung und verantwortungsvolle Steuerung (2026)
Reismann, Hendrik;Zitatform
Reismann, Hendrik (2026): Künstliche Intelligenz (KI) im Geschäftsprozessmanagement der Sozialwirtschaft: Anwendungsfälle, Implementierung und verantwortungsvolle Steuerung. In: Sozialer Fortschritt, Jg. 75, H. 4, S. 553-579. DOI:10.3790/sfo.2026.1485201
Abstract
"Der Beitrag untersucht den Zusammenhang von Geschäftsprozessmanagement und Künstlicher Intelligenz (KI) in der Sozialwirtschaft. Ausgangspunkt ist die Beobachtung, dass Geschäftsprozessmanagement in der Sozialwirtschaft bislang nur begrenzt etabliert ist und Digitalisierungsbemühungen in diesem Bereich häufig noch unvollständig bleiben. Vor diesem Hintergrund analysiert der Beitrag, inwiefern KI als Weiterentwicklung bestehender IT-Prozessunterstützung genutzt werden kann, ohne professionelle Handlungsspielräume einzuschränken. Im Beitrag werden Management- und Unterstützungsprozesse sowie primäre Leistungsprozesse unterschieden. Es wird gezeigt, dass KI grundsätzlich in allen organisationalen Prozessen Entlastungs- und Effizienzpotenziale bietet, wobei an den Einsatz in Leistungsprozessen besondere professionsethische und steuerungsbezogene Anforderungen gestellt sind. Der Beitrag argumentiert, dass KI im Geschäftsprozessmanagement und auch generell weniger als disruptive Technologie, denn als langfristiges organisationsentwickelndes Projekt zu begreifen ist. Entscheidend für einen verantwortungsvollen Einsatz sind Prozessklarheit, Daten-Readiness, geeignete Governance-Strukturen sowie eine reflektierte professionelle Nutzung. Für die Implementierung werden verschiedene Vorschläge diskutiert und ein generalisiertes Stufenkonzept vorgestellt." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Implikationen generativer künstlicher Intelligenz für den Arbeitsmarkt: Eine Analyse von Online-Stellenanzeigen in Deutschland (2026)
Zitatform
Rüschoff, Britta, Timm Eichenberg, Tom Staab & Michael Vogelsang (2026): Implikationen generativer künstlicher Intelligenz für den Arbeitsmarkt: Eine Analyse von Online-Stellenanzeigen in Deutschland. In: Zeitschrift für Arbeitswissenschaft, S. 1-17. DOI:10.1007/s41449-026-00528-2
Abstract
"Mit dem immer stärkeren Einsatz künstlicher Intelligenz (KI) in der Arbeitswelt wandeln sich berufliche Tätigkeiten und damit einhergehend die Anforderungen, die an Beschäftigte gestellt werden. Diese Studie untersucht die Implikationen generativer KI für den deutschen Arbeitsmarkt in der frühen Implementierungsphase der Technologie. Anhand eines umfangreichen Datensatzes von 9,3 Mio. Online-Stellenanzeigen aus den Jahren 2020 bis 2023 analysiert die Studie die Entwicklung der Anzahl an Stellenanzeigen mit Bezug zu generativer KI und identifiziert sowie klassifiziert Tätigkeiten im Zusammenhang mit generativer künstlicher Intelligenz unter dem Einsatz von Methoden des Natural Language Processing (NLP) und manueller Klassifizierung. Die Ergebnisse zeigen, dass generative KI auf dem deutschen Arbeitsmarkt bis Ende 2023 noch eine eher geringe Bedeutung erlangt hatte. Einzig das Prompt Engineering taucht als grundlegend neue berufliche Tätigkeit in einem sehr geringen Umfang auf. Praktische Relevanz: Diese Studie beleuchtet die Implementierung generativer KI auf dem deutschen Arbeitsmarkt auf Basis beobachtbarer Veränderungen in diesbezüglichen Stellenausschreibungen und damit einhergehende Veränderungen in beruflichen Tätigkeiten auf Basis realer Arbeitsmarktanforderungen. Die Ergebnisse dieser Studie können Erkenntnisse darüber liefern, wie generative KI den deutschen Arbeitsmarkt verändert. Unternehmen können Einblicke in die derzeitige Verbreitung generativer KI auf dem Arbeitsmarkt erlangen und diese nutzen, um sowohl ihre Rekrutierungsstrategien und Aufgabenprofile gezielt anzupassen sowie bestehende Mitarbeitende durch Weiterbildungsstrategien auf neue Anforderungen vorzubereiten. Auf individueller Ebene können die Ergebnisse dieser Studie Beschäftigten und Jobsuchenden ein realistisches Bild davon liefern, wo und auf welche Weise generative KI berufliche Tätigkeiten beeinflusst, sodass sie ihre Bildungs- und Karriereentscheidungen entsprechend ausrichten können." (Autorenreferat, IAB-Doku)
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Literaturhinweis
The Impact of Digital Technologies on Skills‐to‐Job Mismatches (2026)
Zitatform
Santiago-Vela, Ana (2026): The Impact of Digital Technologies on Skills‐to‐Job Mismatches. In: New Technology, Work and Employment, S. 1-29. DOI:10.1111/ntwe.70036
Abstract
"A pervasive discourse calls for worker upskilling amid ongoing digital transformation. This study introduces a skills-to-job mismatch perspective into research on the digital transformation of work by conceptualising technological adoption as a process that reshapes the relationship between workers’ skill endowments and evolving job requirements. To empirically address this perspective, the study combines German individual- and establishment-level data with an entropy balancing method. The findings reveal that adopting new digital technologies in the workplace is associated with greater underskilling and reduced overskilling (i.e., skill underutilisation). Furthermore, effect heterogeneity emerges across the level of digitalisation in economic sectors. Technology adoption is more strongly associated with lower overskilling in less digitally mature sectors, while the positive association with underskilling is evenly distributed across sectors. Overall, this study provides a theoretically grounded framework and an informed empirical basis for a more nuanced debate on the digital transformation of work." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
KI in der Pflege zwischen Anspruch und Realität: Empfehlungen für die Praxis aus dem Projekt ETAP (2026)
Sarwary, Hares; Wielga, Jenny; Enste, Peter;Zitatform
Sarwary, Hares, Jenny Wielga & Peter Enste (2026): KI in der Pflege zwischen Anspruch und Realität: Empfehlungen für die Praxis aus dem Projekt ETAP. (Forschung aktuell / Institut Arbeit und Technik 2026-03), Gelsenkirchen, 11 S. DOI:10.53190/fa/202603
Abstract
"Die Implementierung von digitalen Innovationen ist in der stationären Altenpflege mit vielen Herausforderungen verbunden, für die sich unterschiedliche Lösungsoptionen aufzeigen lassen. Eine stabile IT-Infrastruktur und Interoperabilität sind zentral, um einen nachhaltigen Nutzen zu schaffen und Insellösungen zu vermeiden. Kontinuierliche und praxisnahe Schulungsangebote für Pflege- und IT-Personal müssen Teil einer langfristig angelegten Digitalisierungsstrategie in Einrichtungen werden. Transparente Kommunikation innerhalb der Einrichtung ist ein entscheidender Faktor, um Erwartungen zu steuern und Vertrauen aufzubauen." (Autorenreferat, IAB-Doku)
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Aspekt zurücksetzen
- 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