BEAR (Behavioral Evaluation & Adoption Risk) is a 21‑day behavioral evaluation designed to diagnose why AI adoption stalls, becomes inconsistent, or fails to stabilize in real organizational workflows. The program focuses on observing how employees actually interact with an AI system under everyday conditions—how they test it, rely on it, override it, avoid it, or create shadow workflows around it. BEAR maps these behaviors to the underlying psychological and organizational dynamics that shape trust, friction, and reliance, producing a clear picture of the behavioral failure modes that emerge once an AI system leaves the pilot environment.
The evaluation uses structured observation, behavioral logging, interviews, and workflow tracing to identify where the five behavioral dimensions of the AI Trust Axis are strained: Cognitive Alignment, Autonomy Safety, Fairness Comprehension, Interaction Effort, and Failure Recovery Intelligence. BEAR does not attempt to fix these issues directly; instead, it reveals the specific behavioral conditions the organization must create for adoption to stabilize. The output is a diagnostic blueprint that explains why the system is not being used as intended and what behavioral changes are required to support appropriate reliance.
Ultimately, BEAR provides organizations with a high‑resolution behavioral map of their AI deployment. It is the earliest and most targeted engagement in the Behavieural suite, giving leaders a precise understanding of the human factors that must be addressed before scaling or governance interventions can succeed.
BEAR (Behavioral Evaluation & Adoption Risk) is a 21‑day behavioral evaluation designed to diagnose why AI adoption stalls, becomes inconsistent, or fails to stabilize in real organizational workflows. The program focuses on observing how employees actually interact with an AI system under everyday conditions—how they test it, rely on it, override it, avoid it, or create shadow workflows around it. BEAR maps these behaviors to the underlying psychological and organizational dynamics that shape trust, friction, and reliance, producing a clear picture of the behavioral failure modes that emerge once an AI system leaves the pilot environment.
The evaluation uses structured observation, behavioral logging, interviews, and workflow tracing to identify where the five behavioral dimensions of the AI Trust Axis are strained: Cognitive Alignment, Autonomy Safety, Fairness Comprehension, Interaction Effort, and Failure Recovery Intelligence. BEAR does not attempt to fix these issues directly; instead, it reveals the specific behavioral conditions the organization must create for adoption to stabilize. The output is a diagnostic blueprint that explains why the system is not being used as intended and what behavioral changes are required to support appropriate reliance.
Ultimately, BEAR provides organizations with a high‑resolution behavioral map of their AI deployment. It is the earliest and most targeted engagement in the Behavieural suite, giving leaders a precise understanding of the human factors that must be addressed before scaling or governance interventions can succeed.
