Managing the Generative Artificial Intelligence Capability Trap

Abstract

Generative Artificial Intelligence (GenAI) can raise productivity in knowledge work, but deeper delegation may weaken the human capabilities needed to verify, challenge, and recover from model failures. This conceptual paper develops a resilience-aware theory of GenAI deployment by integrating research on artificial intelligence, organizational learning, human-automation interaction, organization design, and risk governance. It introduces the GenAI Capability Trap: productivity gains encourage deeper automation; reduced direct task exposure erodes independent judgment, verification, and recovery capability; and weaker human control increases dependence on the technology. The framework identifies task uncertainty, consequence of error, correlated cognition, feedback quality, and delegation reversibility as key boundary conditions. It derives eight testable propositions and proposes protected human baselines, skill reservoirs, challenge rights, blind dual-running, and recurring governance. Effective GenAI management should therefore optimize risk-adjusted joint capability rather than adoption alone, while preserving independent human recovery capacity.