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

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.