2026· International journal of research and scientific innovation· Vol 13, pp. 1650-1669· 0 citations
TL;DR
The proposed framework provides a practical and theoretically grounded approach for advancing responsible AI adoption and strengthening board-level governance oversight and contributes to theory by positioning AI governance as a dynamic organisational capability rather than a collection of compliance activities.
Abstract
Artificial Intelligence (AI) governance has emerged as a critical organisational and board-level concern as AI systems become increasingly embedded in business operations and decision-making. Although existing AI maturity models assess technological capability and deployment readiness, they provide limited mechanisms for evaluating governance effectiveness, accountability, and board oversight. Consequently, organisations lack structured approaches for assessing whether AI governance practices are achieving their intended objectives.
This study addresses this gap through the development of an AI Governance Capability Maturity Model that reconceptualises AI governance as a measurable organisational capability. Using a qualitative integrative synthesis of regulatory frameworks, legal doctrine, governance standards, and academic literature, the study identifies key governance mechanisms and integrates them within a six-phase governance framework. These governance phases are subsequently transformed into a five-level maturity model supported by a multi-dimensional measurement architecture comprising input, process, output, and outcome metrics.
The analysis demonstrates that existing maturity models focus primarily on AI deployment capability, while governance-oriented frameworks emphasise operational controls but provide limited support for performance evaluation, strategic governance, and board-level accountability. To address these limitations, the proposed model links governance processes to measurable indicators and maturity levels, enabling organisations to assess governance effectiveness, identify capability gaps, and monitor continuous improvement.
The study contributes to theory by positioning AI governance as a dynamic organisational capability rather than a collection of compliance activities. It contributes to practice by providing a structured framework that supports governance assessment, performance monitoring, and board oversight. The model aligns with emerging governance expectations reflected in the NIST AI Risk Management Framework, ISO/IEC 42001, and the European Union Artificial Intelligence Act.
By integrating governance processes, capability development, maturity assessment, and performance measurement, the proposed framework provides a practical and theoretically grounded approach for advancing responsible AI adoption and strengthening board-level governance oversight.
Governance systems across sectors vary widely in their ability to integrate artificial intelligence,
real-time monitoring, and adaptive oversight. While advanced organizations increasingly rely on
continuous sensing, data-driven decision-support, and event-validated learning, many institutions
remain anchored in reactive, compliance-centric governance models. This manuscript introduces
the Governance Maturity Model (GMM), a five-level capability framework that evaluates an
organization's readiness to implement adaptive, AI-enabled governance systems. The GMM
extends the Adaptive Governance Systems Framework (AGSF) and the AI-Enabled Governance
Oversight Model (AIGOM) by defining progressive stages of governance capability—from
reactive oversight to fully adaptive, intelligence-augmented governance ecosystems. The GMM
further establishes governance maturity as a dynamic institutional capability involving
governance observability, operational intelligence integration, adaptive recalibration, and crossdomain governance coordination within complex socio-technical environments. The model
provides a structured pathway for organizations seeking to modernize governance practices,
strengthen accountability, and align oversight mechanisms with the demands of complex,
dynamic risk environments.
Dr. Robb Shawe· International journal of adv...· 0 citations
Artificial Intelligence has evolved from a collection of analytical technologies into an operational capability that increasingly shapes enterprise decision-making, business processes, and strategic management. Organizations now deploy Large Language Models, AI agents, predictive analytics, and intelligent automation across critical operations. While these technologies expand organizational capabilities, they also introduce governance challenges that cannot be addressed solely through traditional security controls or regulatory compliance. Existing approaches largely treat governance as an external oversight function rather than an integral component of enterprise system architecture. 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. Rather than governing isolated AI models, the proposed approach governs how Artificial Intelligence is accessed, how knowledge is utilized, how decisions are formed, how risks are managed, and how human oversight is maintained. To support this perspective, the study introduces the AI Governance Engineering Framework (AIGEF) , consisting of seven architectural layers: Identity and Access Governance, Knowledge Governance, Decision Policy Management, Explainability and Transparency, Risk and Trust Evaluation, Governance Orchestration, and Continuous Governance Learning. Together, these components transform governance from a compliance activity into an operational capability that actively participates in enterprise reasoning. The framework introduces decision-level governance , where the primary object of governance is the organizational decision process rather than the AI model itself. The framework is further informed by enterprise AI implementations addressing governance challenges in infrastructure management, decision-support environments, and industrial AI systems. These implementations demonstrate that trustworthy enterprise AI requires governance mechanisms supporting transparent reasoning, human oversight, adaptive risk management, and organizational accountability. The study concludes that trustworthy enterprise AI cannot be achieved solely through more accurate models or stricter regulations. Instead, governance must become an architectural capability embedded throughout the decision lifecycle, enabling enterprise intelligence systems that are secure, explainable, trustworthy, and governable by design.
Faruk Çelikkanat· International Journal of Res...· 0 citations
This article examines three interconnected dimensions of responsible AI for enterprise modernization: governance infrastructure for accountable AI deployment, algorithmic equity in high-impact decision environments, and the evolving international regulatory landscape shaping enterprise AI governance.
M. Modi· International Journal of Eng...· 0 citations
This study develops a six-phase human-centred governance framework for responsible AI adoption through an integrative synthesis of academic literature, international standards, and regulatory frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001, and the European Union Artificial Intelligence Act.
This study aims to examine how artificial intelligence (AI) governance supports sustainable decision-making across organizational contexts in Europe, focusing on six Portuguese firms in energy, urban mobility and finance.
Adopting a sociotechnical perspective, this research uses a qualitative multiple case study design with semi-structured interviews of Chief Information Officers across diverse organizational contexts. It integrates technical and social dimensions to capture how digital infrastructures, governance practices and human factors interact in decision-making processes.
The findings reveal that governance increasingly aligns with formal frameworks through policies, dedicated structures, human oversight and Environmental, Social and Governance (ESG) oriented indicators, enhancing transparency and reliability. However, maturity varies by sector, resources and technology and challenges such as data limitations, organizational resistance and regulatory uncertainty persist. Furthermore, AI governance emerges as an adaptive, iterative capability for navigating sustainability complexities.
This study provides original insights by linking AI governance to sustainable decision-making through a sociotechnical lens, an area still underexplored in empirical research. It advances theory by integrating ESG considerations into AI governance and offers practical value by identifying mechanisms that enhance transparency, accountability and sustainability outcomes.
Fernando Almeida· Journal of Ethics in Entrepr...· 0 citations
Artificial intelligence (AI) is rapidly transforming governance systems across sectors, yet most
institutions continue to rely on oversight models designed for pre-digital environments. As AI
becomes embedded in cyber-physical systems, organizational decision processes, and regulatory
infrastructures, governance must evolve from static compliance to adaptive,
intelligence-augmented oversight. This manuscript introduces the AI-Enabled Governance
Oversight Model (AIGOM). This layered decision-support intelligence architecture integrates
AI-driven sensing, operational observability, analytics, and adaptive decision-support into
governance systems while preserving human accountability, governance interpretation, and
ethical control. The model demonstrates how AI can serve as a governance augmentation layer,
generating decision-support intelligence, accelerating operational awareness, enhancing adaptive
oversight, and supporting real-time governance recalibration. AIGOM extends the Adaptive
Governance Systems Framework (AGSF) by specifying how AI capabilities interface with
governance processes across diverse sectors, including critical infrastructure, healthcare, finance,
and public administration. This manuscript establishes a theoretical and operational foundation
for AI-enabled governance across complex socio-technical environments.
Dr. Robb Shawe· International journal of adv...· 0 citations