Measuring the Effects of AI in the Workplace: Co-Creating a Research Approach with Companies

In the current highly digitized economy, companies are under growing pressure to implement artificial intelligence (AI) applications to remain competitive (e.g., Makridakis, 2017). Therefore, the share of the working population directly interacting with AI in their workplace is likely to increase drastically in the upcoming years. In 2021, 20% of all employees in Germany stated that they were regularly using AI at work. However, when asked on the use of specific systems with AI technology, this percentage is twice as high (Giering et al., 2021). With capabilities increasingly replicating human cognition, AI is continuing to transform the working world by, e.g., substituting or complementing workers in a wide array of tasks (Fregin et al., 2023). This transformation can have various implications for workers’ well-being, job satisfaction, perceived job security, economic prospects as well as the need for upskilling, among others Cramarenco et al., 2023; Giuntella et al., 2023; Lane et al., 2023). Although understanding workers’ perceptions of AI implementation in their job becomes of vital importance for researchers, employers and policy makers, to date, the empirical evidence is still scarce. Establishing such an evidence-based understanding of AI at work would enable

Measuring the Effects of AI in the Workplace: Co-Creating a Research Approach with Companies

In the current highly digitized economy, companies are under growing pressure to implement artificial intelligence (AI) applications to remain competitive (e.g., Makridakis, 2017). Therefore, the share of the working population directly interacting with AI in their workplace is likely to increase drastically in the upcoming years. In 2021, 20% of all employees in Germany stated that they were regularly using AI at work. However, when asked on the use of specific systems with AI technology, this percentage is twice as high (Giering et al., 2021). With capabilities increasingly replicating human cognition, AI is continuing to transform the working world by, e.g., substituting or complementing workers in a wide array of tasks (Fregin et al., 2023). This transformation can have various implications for workers’ well-being, job satisfaction, perceived job security, economic prospects as well as the need for upskilling, among others Cramarenco et al., 2023; Giuntella et al., 2023; Lane et al., 2023). Although understanding workers’ perceptions of AI implementation in their job becomes of vital importance for researchers, employers and policy makers, to date, the empirical evidence is still scarce. Establishing such an evidence-based understanding of AI at work would enable

Generation Ungewiss: Berufseinsteiger auf dem Weg ins Abseits? Empirische Vergleiche zur Chancenentwicklung von befristet beschäftigten Arbeitsmarkteinsteiger/innen

This paper examines the effect of changing labor market conditions and individual characteristics on early labor market career results. More precisely, it tackles the chances for a transition from fixed-term to permanent employment during the professional start-up phase and explores explaining factors. Longitudinal data of the SOEP (survey years 1990-2010) are used to conduct an empirical investigation. Several theory-based hypotheses are being tested by means of descriptive analysis and multivariate nested discrete-time survival analysis models. Findings suggest that the timing of labor market entrance has hardly any effect on the transition from fixed-term to permanent work. The stock of human capital (level of formal qualification) has a statistical highly significant impact on transitions: compared to those with a middle qualification level, people with a high and a low educational qualification do have poorer chances of contracts for an indefinite term. Public employees have poorer chances than other employees. There is no statistical evidence for the effects of the variables sex, migration background, age and size of corporation. In contrast, higher regional unemployment rates decrease, a growth of GDP increases the opportunity for a transition to permanent employment

On the Automation of Job Tasks: Occupational exposure to Artificial Intelligence and Software

While rapid advances in digital technologies transformed the occupational structures and workers‘ skill and task composition over the past decades, much less is known about how Artificial Intelligence technologies (AI) will shape future labour markets. As part of the “ai:conomics” project, we analyze the extent to which employees subject to social security contributions in Germany are potentially exposed to AI and software technology. Our results show that highly educated, high-income workers are most exposed to AI, while their exposure is lower to software. Overall, the findings suggest that given AI’s far-reaching potential to carry out different sets of tasks, these technologies are expected to impact workers across a wider skill and wage spectrum, which previous automation technologies had limited impact on

On the Automation of Job Tasks: Occupational exposure to Artificial Intelligence and Software

While rapid advances in digital technologies transformed the occupational structures and workers‘ skill and task composition over the past decades, much less is known about how Artificial Intelligence technologies (AI) will shape future labour markets. As part of the “ai:conomics” project, we analyze the extent to which employees subject to social security contributions in Germany are potentially exposed to AI and software technology. Our results show that highly educated, high-income workers are most exposed to AI, while their exposure is lower to software. Overall, the findings suggest that given AI’s far-reaching potential to carry out different sets of tasks, these technologies are expected to impact workers across a wider skill and wage spectrum, which previous automation technologies had limited impact on

Trust, transparency, encouragement: Starting points for the design of operational AI innovation processes

This ai:conomics Policy Brief complements the economic research on the consequences of AI introductions conducted by ai:conomics with companies. It thus will invite companies to take a targeted approach to the conscious design of corporate innovation processes in the technology field of AI, taking uncertainty into account as a key contextual factor for AI innovation. Current practice-oriented process models for the operational introduction of AI innovations serve as the basis for interviews with people with experience in the introduction of AI in large German companies. Scientific findings and models from organizational psychology research enrich the practical reflection with starting points for the targeted design of sustainable operational innovation processes

Trust, transparency, encouragement: Starting points for the design of operational AI innovation processes

This ai:conomics Policy Brief complements the economic research on the consequences of AI introductions conducted by ai:conomics with companies. It thus will invite companies to take a targeted approach to the conscious design of corporate innovation processes in the technology field of AI, taking uncertainty into account as a key contextual factor for AI innovation. Current practice-oriented process models for the operational introduction of AI innovations serve as the basis for interviews with people with experience in the introduction of AI in large German companies. Scientific findings and models from organizational psychology research enrich the practical reflection with starting points for the targeted design of sustainable operational innovation processes

Die Auswirkungen von KI am Arbeitsplatz messen: Co-Creation eines Forschungsansatzes mit Unternehmen

In der heutigen, hochdigitalisierten Wirtschaft stehen Unternehmen zunehmend unter Druck, Anwendungen künstlicher Intelligenz (KI) zu implementieren,um wettbewerbsfähig zu bleiben (z. B. Makridakis, 2017). Daher ist davon auszugehen, dass der Anteil der Erwerbstätigen, die an ihrem Arbeitsplatzdirekt mit KI interagieren, in den kommenden Jahren erheblich steigt. Im Jahr 2021 gaben 20% aller Beschäftigten in Deutschland an, dass sie regelmäßigKI am Arbeitsplatz nutzen (Giering et al., 2021). Bei der Nennung spezifischer Systeme mit integrierter KI-Technologie ist dieser Anteil sogar doppelt sohoch (ebd.). Da die Fähigkeiten von KI zunehmend der menschlichen Kognition ähneln bzw. teilweise bereits übersteigen, wird KI die Arbeitswelt weiterverändern, z.B. indem sie Beschäftigte bei der Ausführung einer Vielzahl von Aufgaben ersetzt oder ergänzt (Fregin et al., 2023). Dieser Wandel kann sichunter anderem auf das Wohlbefinden von Beschäftigten, ihre Arbeitszufriedenheit sowie wahrgenommeneArbeitsplatzsicherheit, ihre wirtschaftlichenZukunftsperspektiven und den Bedarf an Weiterqualifizierungauswirken (Cramarenco et al., 2023;Giuntella et al., 2023; Lane et al., 2023). Obwohl esfür Forscher:innen, Arbeitgeber:innen und politischeai:conomics Kurzdossier 2Entscheidungsträger:innen von entscheidenderBedeutung ist, zu verstehen, wie Beschäftigte denEinsatz von KI am eigenen Arbeitsplatz wahrnehmen,gibt es bisher nur wenige empirische Belege