Title:
Translating AI-Enabled Hazard Detection into Governance Decision-Making: From Operational Safety Systems to Board-Level Risk Oversight

Authors:
Dr. Robb Shawe, USA

Abstract:

This research examines how AI-enabled hazard-detection systems, particularly YOLO-based computer-vision technologies, can be translated from operational safety tools into strategic governance instruments for organizational decision-making. Building on the AI-Augmented Safety Governance Model (AASGM) and its associated maturity framework, the study explores how real-time hazard-detection data can be transformed into board-level risk indicators to inform capital allocation, risk management, and oversight priorities. The paper introduces a governance translation framework that bridges technical outputs with executive decision-making processes, emphasizing the role of human oversight, accountability, and regulatory alignment. The findings demonstrate that the effectiveness of AI-enabled safety systems depends not only on detection performance but also on the ability to translate technical insights into actionable governance information. The study advances Safety 4.0 by linking operational safety technologies to strategic leadership and policy decision-making.

DOI: http://dx.doi.org/10.51505/ijaemr.2026.11319