Contemporary Social Science

AI, automation, and the future of work

In recent years, the automation of work has accelerated dramatically, driven by rapid technological advances, most notably in Artificial Intelligence (AI). As AI continues to evolve, it presents significant opportunities for the future of work, but also complex economic, institutional, and societal challenges. On the one hand, it holds the potential to enhance productivity, foster innovation, and create new types of work (Acemoglu et al., 2022); on the other, it poses substantial risks, including job displacement, algorithmic control, and rising inequality (Frey and Osborne, 2017). These tensions have prompted debates over the implications of AI for the future of work, including how and where work is performed (Akan, et al., 2025; Eisfeldt et al., 2026; Wu, 2026).

 

One of the most immediate impacts of AI lies in the transformation of work tasks. The diffusion of generative AI, in particular, has pushed the boundaries of automation beyond routine, codified processes (Eisfeldt et al., 2026) into the realm of complex cognitive tasks, such as ideation, problem-solving, content creation, and evaluative judgement (De Cremer et al., 2023; Demirci et al., 2025). As these tools become increasingly embedded in everyday workflows, it is essential to reconsider how tasks are restructured within occupations, how responsibilities are redistributed between humans and machines, and how associated labour market skill requirements are redefined (Jia et al., 2024). These shifts especially compel a reassessment of longstanding assumptions around human-machine complementarity versus substitution (Acemoglu et al., 2022; Frey and Osborne, 2017), and the skills likely to remain resilient in the era of AI. Of particular concern is whether uniquely human capabilities, such as creativity (Wang et al., 2025), leadership (Miguel Diez Valle and Nikita, 2025), and decision-making (Saini et al., 2025), will become more valued or vulnerable to displacement. They also call for a broader reconsideration of how skills policy can adapt to an increasingly automated world.

 

Importantly, transformations at the task level often cascade into broader changes within organisations and society. As task boundaries shift and human–machine interactions become more pervasive, organisations are compelled to reconfigure not only how work is performed, but also how it is managed, evaluated, and governed. AI adoption increasingly affects how work is allocated and coordinated across teams and hierarchies, contributing to new patterns of autonomy, accountability, and power distribution within organisations (Prassl, 2019; Flanagan and Walker, 2020). For instance, the use of algorithmic tools in task allocation, performance monitoring, and decision support blurs the boundaries between human and machine agency, introduces new forms of managerial oversight and surveillance, and raises pressing ethical questions around the governance of AI in the workplace (Gruber et al., 2025). These dynamics also shape employees’ job satisfaction and professional identity (Schulz et al., 2025). Taken together, they call for renewed attention to how power, responsibility, and trust are organised in AI-mediated work settings (Bonfigliol et.al, 2025).

 

These transformations also have spatial dimensions, especially the reconfiguration of workplace geographies. As AI accelerates the decoupling of work from employer-provided offices, there is a growing reliance upon homes, coworking spaces, and hybrid “third places”, reshaping the spatial organisation of workplace and urban systems in fundamental ways (Fai & Mariotti, 2025).   Evidence already suggests AI is shifting urban property dynamics, including a weakening of central commercial rent gradients (Rosenthal et al., 2022; Gupta et al., 2026) and altered demand across transit-oriented geographies, alongside broader changes in work-location distributions with major implications for city structure, commuting systems, and service economies (O’Driscoll, 2025). At the same time, innovation-oriented activities have benefited from agglomeration and collective workspaces, such as coworking ecosystems facilitating collaboration and knowledge spillovers (Lavoratori et al., 2024). This raises the question of whether AI will strengthen demand for shared creative spaces or instead diminish the value of certain workplace geographies by substituting routine cognitive tasks.

 

Beyond the workplace, these transformations are impact unevenly upon regional trajectories. Variations in labour market structure, technological readiness, and institutional capacity across urban, suburban, rural, and peripheral regions call for place-based policy responses (Bailey et al., 2026). Where digital divides persist, there is a real risk that AI could further disadvantage rural/peripheral/lagging regions, thereby entrenching existing patterns of spatial inequality. These dynamics call for an integrated framework that considers the future of work across diverse regional (and potentially cross-national) contexts in the era of AI and automation.

 

Taken together, these transformations call for a rethinking of future of work. AI is not simply a new tool or technology; it is a general-purpose system with the potential to reshape work across multiple scales. From the restructuring of individual tasks and skill requirements to shifts in organisational practices and the spatial reconfiguration of workplaces, AI is remaking systems through which work is performed, experienced, and governed. Yet, despite increasing public attention and scholarly interest, research that systematically investigates these contemporary and urgent issues remains in its early stages.

 

This Special Issue therefore welcomes papers dealing with these issues and/or those seeking to build upon recent research on AI, automation, and the future of work from various dimensions. We are particularly interested in papers that address the following (non-exhaustive) topics:

• The transformation of tasks and the evolution of skill compositions in response to AI and automation
• Labour market restructuring, job polarisation, and employment dynamics in the era of AI and automation
• The role of human capabilities (e.g., creativity, leadership, and decision-making) in the era of AI and automation
• New patterns of autonomy, accountability, and power distribution within AI-mediated organisations
• Ethical challenges and governance frameworks for responsible AI adoption in the workplace
• AI adoption and employee productivity, job satisfaction, professional identity and wellbeing
• Education, training, and skills policy responses to AI-driven change
• New working spaces in the era of AI and automation (e.g., coworking spaces, third places, and digital platforms)
• Hybrid working, spatial reconfiguration, and AI-enabled transformations of workplaces and built environments (e.g., buildings, real estate, and cities)
• Comparative analyses of AI and the future of work from regional or global perspectives
• Challenges for labour markets in rural/peripheral/lagging regions in adapting to AI-driven change

 

We invite submissions from researchers across all fields of social science and management studies, and related fields. Conceptual, theoretical and empirical submissions – qualitative and quantitative are welcome, as well as case studies from different regions/cities and contexts.

 

References

Acemoglu, D., Autor, D., Hazell, J.,Restrepo, P. (2022). Artificial Intelligence and Jobs: Evidence from Online Vacancies. Journal of Labor Economics, 40(S1), S293-S340.

Akan, M., Barrero, J. M., Bloom, N., Bowen, T., Buckman, S. R., Davis, S. J.,Kim, H.(s). (2025) The new geography of labor markets. National Bureau of Economic Research Working Paper. Available at: https://www.nber.org/papers/w33582

Bailey, D., De Propris, L., Dimos, C., Fai, F. M., Hardy, S.,Tomlinson, P. R. (2026). A critical review of the UK's Modern Industrial Strategy: lessons for ‘place-based’ policy. Regional Studies, 60(1),

Bonfiglioli, A., Crinò, R., Gancia, G.,Papadakis, I. (2025). Artificial intelligence and jobs: evidence from US commuting zones. Economic Policy, 40(121), 145-194.

De Cremer, D., Bianzino, N. M.,Falk, B. (2023). How generative AI could disrupt creative work. Harvard Business Review, 13(13).

Demirci, O., Hannane, J.,Zhu, X. (2025). Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms. Management Science. 71(10):8097-8108.

Fai, F., & Mariotti, I. (2025). Collaborative spaces in third places: types, impacts and policies. Contemporary Social Science, 20(4), 497–503. https://doi.org/10.1080/21582041.2025.2597227

Eisfeldt, A. L., Schubert, G.,Zhang, M. B.(s). (2026) Generative AI and firm values. Journal of Finance, Forthcoming.

Flanagan, F.,Walker, M. (2020). How can unions use Artificial Intelligence to build power? The use of AI chatbots for labour organising in the US and Australia. New Technology, Work and Employment, 36(2), 159-176.

Frey, C. B.,Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114(254-280.

Gruber, M., Langley, A., Roberson, Q.,Von Krogh, G. (2025). Special Research Forum—Our Transformational Era: Implications for Management and Organizations. Academy of Management Journal, 68(5), 875-880.

Gupta, A., Mittal, V.,Van Nieuwerburgh, S. (2026). Work from home and the office real estate apocalypse. American Economic Review, Forthcoming

Jia, N., Luo, X., Fang, Z.,Liao, C. (2024). When and How Artificial Intelligence Augments Employee Creativity. Academy of Management Journal, 67(1), 5-32.

Lavoratori, K., Wu, Y.,Zhang, F. (2024). In the flexible working era: the micro-location choices of co-working spaces. Regional Studies, 59(1),

Miguel Diez Valle, J.,Nikita, N. (2025). AI is revolutionising decision-making, but it can't replace human leaders. LSE Business Review,

O’driscoll, C. (2025). Navigating change: Residential relocation, commuting behaviours, and built environments. Papers in Regional Science, 104(6),

Prassl, J. (2019). What if your boss was an algorithm? Economic Incentives, Legal Challenges, and the Rise of Artificial Intelligence at Work. Comparative Labor Law and Policy Journal, 41(1),

Rosenthal, S. S., Strange, W. C.,Urrego, J. A. (2022). JUE insight: Are city centers losing their appeal? Commercial real estate, urban spatial structure, and COVID-19. Journal of Urban Economics, 127, 103381.

Saini, J., Choudhary, S.,Walia, K. (2025). The Future of AI in Decision-Making: Replacing or Assisting Humans? International Journal of Sciences and Innovation Engineering, 2(5), 751-778.

Schulz, C., Bendig, D., Bräunche, A.,Kindermann, B. (2025). Curse or Blessing: Investigating the Influence of Firms’ Artificial Intelligence Adoption on Employee Job Satisfaction. Journal of Management Studies, 0022-2380

Wang, Z., Baksy, A., Bakhshi, H.,Siepel, J.(s). (2025) Demand for Creativity and AI Skills in the Post-ChatGPT Labour Market: Evidence from UK Job Vacancies. Institution or Creative PEC Discussion Papers Series. Available at: https://pec.ac.uk/wp-content/uploads/2025/11/DP-AI-and-Creativity-1.pdf.

Wu, Y. (2026). Rent Effects of Proximity to AI-Exposed Workplaces in the Post-ChatGPT Era: Evidence from New York City. Woking Paper