Log in
Back to #explainable ai
#explainable ai Open access

Autonomous Decision Assurance Layer for AI-Driven Enterprise Analytics: Bridging Governance, Multi-Agent AI, and Executive Decision Intelligence in Saudi Vision 2030 Organizations

Choudhry Bilal Mazhar
Aug 2026 · International Journal for Research in Applied Science and Engineering Technology · 0 citations

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.

DOI Semantic Scholar
Read PDF

Similar papers

#explainable ai Aug 2026

AI and Bullshit

It is argued that both AI and bullshitters are untrustworthy informants, and for similar reasons, it is natural to describe AI’s informational outputs as bullshit, as it signals their distinctive kind of epistemic deficiencies, which they share with bullshit.

Duncan Pritchard · 1 citation
#explainable ai Review Sep 2026

Advancing stroke prevention in atrial fibrillation: a systematic review of machine learning-based risk prediction models

In light of the pervasive methodological limitations identified, including high analytic risk of bias, absence of external validation, and lack of model interpretability, claims of ML superiority over CHA2DS2-VASc must be interpreted with caution.

Md. Mohaimenul Islam, Arinzechukwu Nkemdirim Okere · 0 citations
#reinforcement learning Nov 2026

Reinforcement Loads Prediction of Geosynthetic-Reinforced Soil Structures Using Explainable and Nonexplainable Machine Learning Approaches

This study presents three advanced machine learning models: the evolutionary Gaussian process inference model, the artificial satellite search algorithm–moment balance machine (ASSA-MBM), and the Operation Rain Forest (ORF), which are designed to predict the maximum reinforcement load in geosynthetic-reinforced soil structures. These models were developed to enhance both predictive accuracy and model interpretability by incorporating state-of-the-art optimization algorithms and explainable machine learning frameworks. A comprehensive evaluation was conducted using 10-fold cross-validation, and the proposed models were benchmarked against previously developed AI models from literature, as well as traditional and semiempirical approaches such as Rankine, Coulomb, and K -stiffness. Among the proposed models, ASSA-MBM consistently achieved the best performance, recording the lowest testing root mean squared error (0.617), the highest correlation coefficient ( R = 0.918 ), and the highest reference index ( RI = 0.951 ). Additionally, the ORF model offers transparency by generating mathematical regression equations, which are crucial in geotechnical engineering.

Min-Yuan Cheng, Akhmad F. K. Khitam, Jia-Wang Liou · 0 citations

Related blog posts