Autonomous Decision Assurance Layer for AI-Driven Enterprise Analytics: Bridging Governance, Multi-Agent AI, and Executive Decision Intelligence in Saudi Vision 2030 Organizations
TL;DR
An Autonomous Decision Assurance Layer (ADAL) is proposed for AI-driven enterprise analytics environments that bridges data governance, multi-agent AI, human-in-the-loop oversight, responsible AI controls, and executive decision intelligence.
Abstract
Enterprise analytics is shifting from descriptive dashboards toward intelligent systems that recommend, prioritize, and increasingly trigger business actions. However, most organizations lack a structured assurance layer that validates whether AIgenerated recommendations are explainable, risk-rated, auditable, policy-aligned, and suitable for execution. This paper proposes an Autonomous Decision Assurance Layer (ADAL) for AI-driven enterprise analytics environments. The proposed framework bridges data governance, multi-agent AI, human-in-the-loop oversight, responsible AI controls, and executive decision intelligence. ADAL introduces seven integrated components: data quality validation, AI recommendation generation, explainability mapping, risk scoring, human approval control, audit logging, and execution monitoring. The model is positioned for Saudi Vision 2030 organizations where digital transformation, governance maturity, cybersecurity readiness, and real-time decision intelligence are national and enterprise priorities. The paper contributes a practical governance-to-execution architecture that can be applied across IT service management, cybersecurity operations, workforce analytics, procurement anomaly detection, HSSE risk intelligence, and corporate performance management.
