Strategic tensions in organizational GenAI adoption: A game theory modeling of internal resource competition, workforce dynamics, and value management

M. Ferrara, G. Viglia and J. C. Romero Moreno ()
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G. Viglia: Audencia Business School
J. C. Romero Moreno: Audencia Business School

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Abstract: Organizations adopting generative AI (GenAI) face complex strategic tensions among management, departments, and employees that fundamentally determine adoption outcomes. This study develops a multi-level Bayesian game-theoretic framework modeling these multi-stakeholder interactions, identifying four distinct adoption patterns through formal equilibrium analysis. Our theoretical derivations establish that successful GenAI implementation requires three analytically-derived conditions: (1) strong strategic complementarity across departments, (2) efficient investment allocation, and (3) effective employee displacement mitigation. The formal model specifies explicit utility functions for three stakeholder groups — senior management, departmental units, and individual employees — and characterizes Bayesian Nash equilibria under incomplete information. Companies must simultaneously invest in cross-functional coordination mechanisms, establish shared governance structures, and implement workforce development programs that position GenAI as a capability enhancement rather than a job replacement. Our computational analysis, based on 10,000 Monte Carlo simulations with explicit parameter specifications and convergence criteria, demonstrates that coordination-focused strategies significantly outperform technology-focused approaches in organizational welfare, providing actionable guidance for AI transformation leadership.

Keywords: Value co-destruction; Strategic information systems; Value co-creation; Resource allocation; Workforce dynamics; Game theory; Organizational transformation; Generative artificial intelligence (search for similar items in EconPapers)
Date: 2026-06
Note: View the original document on HAL open archive server: https://hal.science/hal-05567536v2
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Published in Technological Forecasting and Social Change, 2026, 227, pp.124653. ⟨10.1016/j.techfore.2026.124653⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05567536

DOI: 10.1016/j.techfore.2026.124653

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