GenAI value stalls where strategic ambition meets operational reality: in the middle.
Floyd & Wooldridge (1992); Huy (2002)
95%
of enterprise GenAI pilots show no measurable P&L impact, despite $30–40bn invested.
Firms that do capture value put 70% of their resources into people and processes, not the technology.
Industry reports: Challapally et al. (2025, MIT NANDA); de Bellefonds et al. (2024, BCG)
GenAI adoption research looks above and below the middle, but rarely at middle managers.
Jean-Baptiste (2025): roles & skills via activity theory.
This study: misalignment via sociotechnical systems.
Adoption is a top-down decision plus individual uptake, with nothing in between.
e.g. Prasad Agrawal (2023); Hammerschmidt et al. (2024); Noy & Zhang (2023)
Middle managers are where misalignment surfaces.
Drawing on Floyd & Wooldridge (1992); Huy (2002)
Possible implicationInsight into why GenAI so often stalls at the pilot stage.
How do middle managers experience sociotechnical misalignment during generative AI adoption?
GenAI value stalls where strategic ambition meets operational reality: in the middle.
Floyd & Wooldridge (1992); Huy (2002)
Sociotechnical systems as a sensitising lens: misalignment is a perceived gap between people, structure, task and technology.
Bostrom & Heinen (1977); Lyytinen & Newman (2008), gap concept
Semi-structured interviews with middle managers, analysed abductively.
Gioia et al. (2013)
Alvesson, M., & Sandberg, J. (2011). Generating research questions through problematization. Academy of Management Review, 36(2), 247–271.
Bostrom, R. P., & Heinen, J. S. (1977). MIS problems and failures: A socio-technical perspective. Part I: The causes. MIS Quarterly, 1(3), 17–32.
Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942.
Challapally, A., Pease, C., Raskar, R., & Chari, P. (2025). The GenAI divide: State of AI in business 2025. MIT NANDA.
de Bellefonds, N., et al. (2024). Where's the value in AI? Boston Consulting Group.
Floyd, S. W., & Wooldridge, B. (1992). Middle management involvement in strategy and its association with strategic type: A research note. Strategic Management Journal, 13(S1), 153–167.
Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research. Organizational Research Methods, 16(1), 15–31.
Hammerschmidt, T., Stolz, K. A., & Posegga, O. (2024). How leaders' ambidexterity and literacy impact the AI capabilities of organizations. ECIS 2024 Proceedings, 14.
Huy, Q. N. (2002). Emotional balancing of organizational continuity and radical change: The contribution of middle managers. Administrative Science Quarterly, 47(1), 31–69.
Jean-Baptiste, P. (2025). Transformations in the roles and skills of middle managers in the era of generative AI: An activity theory perspective. AMCIS 2025 Proceedings, 20.
Lyytinen, K., & Newman, M. (2008). Explaining information systems change: A punctuated socio-technical change model. European Journal of Information Systems, 17(6), 589–613.
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192.
Prasad Agrawal, K. (2023). Towards adoption of generative AI in organizational settings. Journal of Computer Information Systems, 64(5), 636–651.
claude "/review-presentation"
in a clone of github.com/michelebaumann/genai-middle-management-rq