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.
Abstract
Artificial Intelligence (AI) is transforming organisational operations and strategic decision-making, but its rapid adoption has outpaced the development of governance mechanisms capable of supporting sustainable growth and societal outcomes. Organisations face increasing challenges in translating ethical principles and regulatory requirements into actionable governance processes that align with business strategy and stakeholder expectations. 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. The proposed framework integrates strategic alignment, risk management, governance-by-design, organisational capability, continuous monitoring, and legal assurance into a structured and repeatable model. By positioning AI governance as a strategic capability rather than a compliance function, the framework provides a practical roadmap for organisations to embed responsible AI practices that enhance transparency, accountability, and trust. The study contributes to the field of human factors in business management by demonstrating how governance can enable sustainable organisational performance while supporting broader societal impact.
As artificial intelligence (AI) systems become increasingly embedded in the structures of knowledge-based organizations, the governance of AI-related risks is emerging as a critical factor for long-term systemic sustainability. This paper explores how AI risk governance can be effectively integrated into the epistemological and structural foundations of such organizations through the lens of fourth-order cybernetics. This theoretical framework emphasizes reflexivity, ethical co-construction, and multilevel feedback involving both human and technical agents. Rather than treating governance as a static set of compliance measures, the proposed model presents it as a dynamic and participatory process. Four core principles are introduced: multilevel feedback, contextual ethics, recursive governance, and the inclusion of marginalized perspectives. These principles support the embedding of AI governance into decision-making and knowledge management systems. The paper contributes to responsible innovation discourse and offers a conceptual pathway for resilient and ethically aligned AI implementation in complex organizational environments.
Ludmila Jiříčková, Petr Doucek· International Scientific Con...· 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
Despite the strategic priority of digital transformation and Artificial Intelligence (AI), many organizational initiatives fail to achieve sustainable outcomes due to insufficient institutional readiness and fragmented governance. To address this gap, this paper introduces the Abuhaimed Digital & AI Excellence Model (ADAIEM), a comprehensive conceptual framework designed to foster institutional readiness and guide enduring transformation. The framework integrates three interdependent pillars: Institutional Foundation: Governance, strategy, organizational structure, processes, knowledge management, and talent development, Digital Enablement: Core digital systems, data infrastructure, automation, analytics, and platforms, and AI Enablement: AI governance, intelligent agents, decision-support mechanisms, and enterprise-wide adoption. Central to the framework is the ADAIEM Conditional Transformation Logic (ACTL), which utilizes capability gates to enforce progression only when prerequisite maturity levels are met. Unlike traditional static maturity models, ACTL actively facilitates continuous capability development, mitigates execution risks, and reinforces operational sustainability. Grounded in Business Engineering and organizational capability theory, ADAIEM advances the literature on digital transformation and AI governance by offering a structured, risk-mitigated pathway toward high-maturity, AI-enabled enterprise operations. Building on the concepts of Business Engineering and based on a variety of organizational and transformation theories, ADAIEM brings together governance, organizational design, knowledge management, talent development, digital capabilities and AI enablement under a single transformation architecture. The proposed framework offers a real-world action plan for sustainable AI transformation and a theoretical understanding of the phenomenon of AI transformation.
M. Abuhaimed· Journal of Intelligent Decis...· 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.