2026· International journal of advanced engineering and management research· Vol 11, pp. 309-321· 0 citations· 8 references
TL;DR
The Governance Maturity Model is introduced, a five-level capability framework that evaluates an organization's readiness to implement adaptive, AI-enabled governance systems and 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.
Abstract
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.
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 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
Examining how artificial intelligence (AI) governance supports sustainable decision-making across organizational contexts in Europe reveals that governance increasingly aligns with formal frameworks through policies, dedicated structures, human oversight and Environmental, Social and Governance oriented indicators, enhancing transparency and reliability.
Fernando Almeida· Journal of Ethics in Entrepr...· 0 citations
A lifecycle-oriented socio-technical governance capacity framework through a structured synthesis of public administration, digital government, decision support systems, responsible AI, socio-technical systems, sustainability, and risk governance scholarship is developed.
Artificial intelligence has moved from a specialised technical concern to a central object of international economic and security policy, yet global governance arrangements remain fragmented across competing regulatory models. This article addresses how policymakers can reconcile innovation, competitiveness, human rights, security and sustainability within a coherent governance architecture for artificial intelligence operating across national, regional and multilateral levels. The goals include the review of the governance theory and current practices applicable to AI regulations, analysis of stakeholders' interests and influence, estimation of the possible economic, social, legal, technological and environmental effects of such an arrangement, as well as the design of a feasible multi-level governance system with monitoring and evaluation mechanisms. The article utilises a qualitative comparative policy analysis based on primary legal documents, which include Regulation (EU) 2024/1689, OECD Recommendation on Artificial Intelligence (as amended in 2024), UN Resolution A/RES/79/325 of 2025, and the Global Digital Compact of 2024, in addition to the academic literature on the subject from peer-reviewed sources and books. This paper proposes a framework that takes into account the multi-level and adaptive governance approaches with a particular emphasis on digital sovereignty, thereby creating a three-tier architecture that will include global normative coordination, regional and plurilateral regulatory clusters, and national or sectoral implementation, illustrated by the case study of Kenya, which has created its National Artificial Intelligence Strategy 2025-2030. The main findings in the paper suggest that regulation fragmentation among the European Union, the United States, and China is growing rather than converging, that multilateral instruments do not possess any kind of binding enforcement mechanism, and that low- and middle-income countries like Kenya experience capacity limitations even when developing a proactive national strategy. This paper argues that a layered subsidiarity approach, with specific financing for capacity-building and technical standards that work across the board, is more likely to deliver effective global AI governance than calls for a binding treaty.
Asher Odhiambo Ojuok, Julius Murumba, E. Micheni· East African Journal of Info...· 0 citations
The rapid integration of artificial intelligence (AI) into public governance systems has profoundly impacted how government agencies make administrative decisions, develop policies and deliver services in the context of democratic organizations. However, existing governance theories are unable to articulate how human cognition and machine intelligence cooperate in a co-decision making environment within government. A pervasive problem in existing governance literature and theories about AI and government is the lack of clarity or the fragmentation of theory regarding the interaction between human intelligence and machine intelligence in such hybrid governance systems particularly when concerning the accountability, the legitimacy, and the quality of decisions in an AI public administration setting. This study attempts to bridge this void. More specifically, this study proposes Cognitive Governance Systems (CGS) Theory as an original explanatory framework of the interaction between human intelligence and machine intelligence in a co-decision making context of democratic government. The research follows a theory building and theory validation-oriented design that is achieved via systematic literature review synthesis. Within this new CGS Theory, we present a cognitive system-based view of governance, conceptualized as a distributed cognition system that includes six elements: human intelligence, machine intelligence, human and machine learning cognition, the interface among those two intelligences (interaction mechanism), the framework on which governance is situated (democratic principles), and the capability of that governance system to perform, respond and evolve (resilience and adaptiveness). This new paradigm in cognitive governance theory explains why the interplay among these six elements influences the quality of decision making, the effectiveness of public policies, and the sustainability of public trust in the age of AI enabled governance systems. Our approach is built with a view to enable future empirical testing, with mixed-method techniques, such as the Delphi study method, survey instruments, and structural equation modeling (SEM).
K. Kunwar· Journal of Advances in Socia...· 0 citations
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