Title:
Translating Technical Risk Signals into Executive Governance Intelligence: A Framework for Board-Level Oversight in Adaptive Governance Systems

Authors:
Dr. Robb Shawe, USA

Abstract:

Modern governance systems increasingly rely on artificial intelligence, real‑time sensing, and event‑validated learning to monitor complex operational environments. However, the value of these systems depends on executives' and boards' ability to interpret technical risk signals and translate them into governance‑relevant insights. This manuscript introduces the Governance Translation Framework (GTF), a structured model for transforming technical outputs—such as anomaly alerts, predictive analytics, and performance deviations—into decision‑ready intelligence for senior leadership. The GTF integrates the Adaptive Governance Systems Framework (AGSF), the AI‑Enabled Governance Oversight Model (AIGOM), and the Governance Maturity Model (GMM) to define how organizations can bridge the gap between technical complexity and strategic oversight. The framework supports executive decision‑making, strengthens accountability, and enhances organizational resilience by aligning technical signals with governance priorities, risk thresholds, and institutional objectives. The framework further establishes governance translation as a critical executive orchestration capability through which operational intelligence, governance observability, and event-validated learning are transformed into adaptive board-level decision intelligence across interconnected socio-technical environments.

DOI: http://dx.doi.org/10.51505/ijaemr.2026.11328