What is cognitive dependency on AI, and how should organisations actively manage the risk?

Cognitive dependency on AI refers to the systemic risk of humans surrendering cognitive agency through excessive offloading of mental tasks to AI systems, leading to automation bias, premature cognitive closure, and irreversible loss of capabilities [1][3]. This occurs as AI assumes cognitive labor, exploiting human tendencies toward cognitive miserliness and reducing practice in key domains like education, medicine, and navigation, potentially crossing critical thresholds where human skills degrade catastrophically without recovery [3]. It manifests as weakened critical thinking from over-reliance on AI tools to shorten project timelines [6] and broader "cognitive debt" where rapid AI adoption outpaces comprehension, fostering unintended dependency and skill erosion [12]. Organizations should actively manage this risk by implementing "scaffolded AI friction" in interfaces to counteract zero-friction designs, encouraging deliberate human cognition and preserving epistemic sovereignty [1]. This includes monitoring delegation levels against quantitative models to avoid surpassing critical capability thresholds (e.g., K* ≈ 0.6-0.8 across domains) and promoting balanced human-AI symbiosis through training and oversight [3][11]. Additionally, establishing agent managers to oversee AI learning and escalation, alongside robust change management to address employee resistance and ensure skill maintenance, can mitigate adoption stalls and cognitive erosion [8][9].