Strategic AI Underuse: Effort Incentives, Displacement Risk, and Organizational Responses

ABSTRACT

This paper studies how workers' employment concerns can affect the effective use of Artificial Intelligence (AI) tools after adoption. When productive use of these tools raises workers' perceived risk of displacement, employees may optimally reduce productive effort, trading current earnings against employment security. The baseline represents the first implementation period, when neither side has yet observed behaviour and production under the new technology. The firm evaluates adoption using the observed pre‐AI effort level, while workers forecast the production scale from the most recently observed aggregate output. Strategic underuse can therefore partly or fully offset the productivity gains expected from AI tools and make an initially attractive technology unprofitable after implementation. After implementation, the firm observes the effective effort response, while its organizational options depend on whether it can diagnose the incentive mechanism behind that response and on the cost or feasibility of workforce adjustment. The firm may choose technological reversion, labour hoarding, workforce restructuring through voluntary separations, or employment‐preserving contracts based on negotiated working‐time reductions. Robustness benchmarks show that the displacement‐induced incentive for underuse can persist when firms and workers form more forward‐looking expectations. A Monte Carlo calibration illustrates that all four organizational responses can arise as profit‐maximizing outcomes.