Avoiding the AI knowledge trap: A simulation-based forecast of entry-level automation, talent pipelines, and innovation capacity

Artificial intelligence is increasingly capable of automating professional tasks traditionally performed by entry-level staff. While such automation may drive short-term efficiency and profitability, it risks disrupting the experience-based learning pipeline essential for developing a firm's long-term innovation capacity. This paper introduces the Knowledge Trap: a structural condition where immediate gains from AI adoption lead to the gradual degradation of the tacit knowledge and adaptive search capabilities required for future performance. Using an NK agent-based simulation model, the study examines how different sectors and governance horizons influence this risk. The model distinguishes between three adoption strategies - substitutive, hybrid, and complementary - to track their long-term effects on a firm's experience pool and innovation fitness. The findings demonstrate that the Knowledge Trap is most severe under substitutive adoption, particularly in sectors with high task interdependency. Conversely, adopting AI in a complementary mode can substantially mitigate these capability losses. This research contributes a forecasting framework to help organisations and policymakers identify structural risks and develop AI implementation strategies that protect the human talent pipeline.