Major aviation group:
35 000 employees
$10 Bn revenue
High-risk operational environment
The company was facing recurring incidents, hidden losses, and limited visibility into the causes of deviations.
Errors were treated as isolated cases or as problems of specific employees and departments.
Management lacked a tool capable of linking task data, process flows, participants’ actions, and the business costs associated with human factor risks.
Built a human factor risk analysis using multimodal data, root cause logic, human error models, and AI interpretation.
Aggregated organizational and operational signals to spot anomalies, likely cause-and-effect relationships, and recurring patterns.
Applied AI to structure weakly connected signals and generate decision-support scenarios and contextual prompts for operational action.
Established a management shift from a “who is to blame” logic to a “signal → cause → cost → action” model.
10% reduction in hidden operational losses.
Implemented a decision-support system designed to operate in a high-volume flow of weakly structured data with dynamic prioritization of criteria in real time.
Enabled managers and operators to translate human factor issues into the language of management decisions, losses, and preventive action.
Laid groundwork for further AI-driven anomaly detection system development to identify early warnings and precursors against major failures.