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.
Artificial intelligence is increasingly embedded in management information systems to support strategic decisions involving forecasting, resource allocation, market intelligence, risk assessment, supply-chain resilience, financial control, cybersecurity, human-resource analytics and organizational governance. Strategic decision-making differs from routine operational automation because it involves uncertainty, long-term consequences, value-laden trade-offs, reputational exposure, regulatory obligations and human accountability. In such settings, predictive accuracy alone is insufficient. Managers require explanations that clarify why an AI system recommends a course of action, what evidence and assumptions shaped the recommendation, how reliable the output is under changing conditions and who remains accountable when algorithmic advice influences organizational outcomes. Explainable artificial intelligence (XAI) has therefore become a critical capability for transforming black-box machine-learning outputs into decision-relevant, contestable and auditable knowledge. This review critically examines XAI for strategic decision-making in management information systems, focusing on transparency, trust calibration and governance frameworks. The paper synthesizes interpretable modelling, post-hoc local explanations, feature attribution, counterfactual explanations, surrogate models, causal explanation and human-centred explanation interfaces. It further evaluates how these approaches influence managerial trust, decision quality, accountability, compliance and organizational learning. The review argues that XAI should not be treated as a technical add-on to predictive modelling; it must be embedded across the complete decision lifecycle through data governance, model documentation, explanation quality controls, stakeholder participation, human oversight and continuous monitoring. A conceptual XAI-GovMIS framework is proposed to connect data governance, model transparency, explanation design, human-AI interaction, strategic decision accountability and responsible AI governance. The paper concludes by identifying unresolved research gaps, including explanation overload, performative transparency, overtrust, weak empirical validation, causal insufficiency, cross-functional accountability gaps and the need for sector-specific governance models for AI-enabled management information systems.
Sampath Kini, Shital Ajit Dumbre, Sunita Kumar et al.· Journal of Intelligent Decis...· 0 citations
This paper argues for a transition from AI Governance as Compliance to AI Governance Engineering , a systems-oriented discipline in which governance is embedded throughout the enterprise intelligence lifecycle, enabling enterprise intelligence systems that are secure, explainable, trustworthy, and governable by design.
Faruk Çelikkanat· International Journal of Res...· 0 citations
This study develops an evidence-informed framework for the strategic integration of AI through a PRISMA-guided systematic literature review and design science artifact construction and offers a rigorous and practical blueprint for scalable and trustworthy AI-enabled operations.
Self-Service Business Intelligence (SSBI) platforms have rapidly enabled business users to access, analyze and visualize data without the support of IT or Analytics team, making using these tools a game-changer for any organization and its decision-making process. But as more users gain control over the process, governance and compliance of policies, transparency, accountability and secure use of data are significant challenges. This paper proposes a framework for Explainable Artificial Intelligence (XAI) Governance for policy-controlled mapping in SSBI Environments. The methodology follows the Activity-based policy mapping, Compliance confidence evaluation, Governance risk assessment and Explainability-driven decision analysis of user actions with organizational policies. An intelligent governance layer continuously analyzes activities, makes governance decision sand explains the decisions in a comprehensible way, which will be available for policy enforcement reasons. Experimental evaluation demonstrates that whereas existing approaches attest to poorer performance in terms of good governance. Experimental evaluation shows that good governance is also improved as compared to the existing practices. The proposed solution has a Policy Mapping Efficiency of 96.5%, Compliance Assurance Rate of 97.4%, Explainability Index of 95.8% and Governance Trust Score of 96.2%, with a Risk Reduction Rate of 94.7%. It is observed that Explainable Governance Analytics data shows improvement of 4.8%, 4.6%, 6.5%, 6.1% and 5.2% for each of the following, respectively. The outcome shows that the framework works well to support good governance of modern SSBI platforms that is transparent, trusted and policy-compliant.
Asha Dass· 2026 International Conferenc...· 0 citations
The model demonstrates how AI can serve as a governance augmentation layer, generating decision-support intelligence, generating decision-support intelligence, accelerating operational awareness, enhancing adaptive oversight, and supporting real-time governance recalibration.
Dr. Robb Shawe· International journal of adv...· 0 citations
Background. Saudi Arabia’s Vision 2030 has moved from strategic planning into execution, where artificial intelligence, data governance and cybersecurity are interdependent national capabilities. The paper addresses how real-time cybersecurity analytics dashboards can connect to telemetry, AI detection, compliance evidence and executive decision-making without collapsing security operations, governance and policy oversight into one visual layer.
Objectives. The objective is to develop a PRISMA-informed design-science framework for real-time AI-driven cybersecurity analytics dashboards aligned with Saudi Arabia’s NCA, SDAIA, NDMO, PDPL and Vision 2030 priorities.
Methods. A narrative evidence synthesis was conducted using academic databases, official policy/regulatory artefacts and selected industry threat reports. The article is positioned as design-science framework development, not as empirical performance evaluation. Search strings, search dates, inclusion/exclusion rules, evidence classes and quality appraisal are documented. Screening and coding were conducted by one reviewer; inter-rater reliability is therefore not claimed and is treated as a limitation.
Results. The paper contributes to a dashboard typology, five-layer architecture, sectoral applicability matrix, operational threat-to-control mapping, KPI dictionary, Responsible-AI matrix and indicative 180-day pilot pathway. A final reference-base strengthening added thirteen additional sources on SOC maturity, SIEM/security analytics, cyber-resilience, critical infrastructure, AI assurance and cybersecurity governance.
Conclusion. Real-time dashboards may support cyber-resilience only where telemetry coverage, model governance, human oversight, compliance evidence, and response workflows are implemented and validated. The Saudi control mapping is context-specific; the layered architecture is transferable if localized.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.