Jul 2026· International Journal of Engineering Science and Information Technology· 0 citations· 37 references
TL;DR
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.
Abstract
The rapid integration of artificial intelligence (AI) into enterprise decision-making systems has fundamentally transformed organizational governance across sectors, enabling automated decisions in credit assessment, healthcare resource allocation, workforce management, pricing strategies, and public-sector services. As AI increasingly influences decisions with significant social and economic consequences, the need for robust governance mechanisms has become as important as technological innovation itself. However, governance frameworks, accountability mechanisms, and equity assessment practices have not advanced at the same pace as AI deployment, creating substantial risks related to transparency, fairness, regulatory compliance, and organizational trust. 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. Drawing upon implementation experiences and governance practices across telecommunications, financial services, and healthcare, the study synthesizes evidence from engineering, policy, ethics, and critical social science literature to develop a comprehensive perspective on responsible AI architecture. The analysis demonstrates that effective AI governance requires integrating technical controls with organizational accountability, continuous monitoring, auditability, risk management, and human oversight throughout the AI lifecycle. Furthermore, the study argues that technical governance alone cannot eliminate algorithmic bias or inequitable outcomes unless accompanied by structural policy interventions addressing the underlying institutional and societal conditions embedded within training data and decision processes. The proposed governance perspective positions responsible AI as a foundational engineering discipline that enhances regulatory compliance, organizational resilience, stakeholder trust, and long-term business sustainability while reducing legal, operational, and reputational risks. The findings provide practical guidance for enterprises seeking to modernize AI-enabled decision systems through governance architectures that balance innovation with accountability, ethical responsibility, transparency, and equitable value creation across increasingly complex digital ecosystems
Artificial intelligence (AI) has become essential to corporate decision‐making, yet current environmental, social, and governance (ESG) frameworks offer limited tools for assessing algorithmic responsibility. This paper examines whether AI's distinctive features, namely, lack of transparency, delegated agency, and ongoing adjustment, challenge the organizational reasoning of ESG frameworks. Drawing on organizational theory and science and technology studies (STS), we argue that ESG frameworks, which evolved through incremental adjustment, may prove insufficient for governing algorithmic systems. While integration succeeded for issues such as cybersecurity and climate risk, AI differs because algorithms operate as social and technical systems that distribute responsibility across human and non‐human networks. We propose adding a fourth pillar, Algorithmic Governance, within an extended ESGA framework to address risks that transcend traditional governance categories. It is intended as a conceptual extension of investor‐facing ESG architectures rather than a replacement of existing standards. This pillar highlights fairness, transparency, responsibility, and robustness as core dimensions of corporate responsibility. The paper contributes to organizational theory by conceptually examining conditions under which established governance architectures require structural extension and to technology governance by rethinking responsibility in mixed human‐algorithmic systems. We further discuss how algorithmic risks may vary across environmental, social, and governance domains and outline conceptual approaches for handling heterogeneity, sectoral differences, and data constraints.
Pitabas Mohanty, Supriti Mishra· Business Strategy and the En...· 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
Artificial intelligence is increasingly embedded in critical digital infrastructure across emerging markets, supporting applications in telecommunications, financial inclusion, healthcare, agriculture, fraud detection and public service delivery. While global responsible AI frameworks have established widely accepted principles for fairness, transparency, accountability, and human oversight, translating these principles into operational safeguards remains uneven, particularly in contexts characterized by institutional fragmentation, evolving regulatory regimes, vendor dependence, and constrained governance capacity. This paper examines structural implementation gaps that arise when globally flexible responsible AI frameworks are applied without contextual integration. It advances a governance by design model that operationalizes trust through measurable proportional risk tiering, lifecycle-based oversight, and structured ecosystem accountability. Governance controls are embedded at decision points spanning design, procurement, deployment, and continuous monitoring, reducing governance debt and strengthening institutional resilience. An applied case illustration demonstrates how proportional governance can be integrated into high impact deployments while preserving scalability. Aligned with emerging international standards, including ISO/IEC 42001, the models offer a scalable, context-aware pathway for implementing trustworthy, inclusive and sustainable AI under real world constraints.
Derick Ohmar, Adil· 2026 ITU Kaleidoscope - AI a...· 0 citations
The widespread application of artificial intelligence (AI) in corporate resource planning and public decision-making has provided impetus for improving management efficiency and creating social value. However, the complex structure and opacity of algorithms have led to a crisis of trust, posing challenges to traditional public management accountability mechanisms. Drawing on socio-technical systems theory, this paper provides a normative analysis of thirteen recent studies on the challenges of technology implementation, ethical trust, and legal regulation. The findings suggest that the current governance dilemma stems not only from technological limitations but also from institutional neglect, which enables accountability avoidance. Although the EU AI Act proposes a preliminary form of collaborative governance, it still has shortcomings in terms of procedural justice and the feasibility of human oversight. The governance logic should shift from individual oversight to an organization-in-the-loop approach, to achieve sustainable and responsible AI governance through the construction of a procedural justice framework.
Artificial intelligence (AI) is increasingly integrated into organizational governance, reshaping decision-making processes, accountability mechanisms, and stakeholder relationships. This study investigates the differences between AI-assisted governance systems and traditional governance approaches regarding accountability and stakeholder trust. A narrative literature review was conducted by analyzing ten scholarly publications published between 2021 and 2026 across diverse sectors, including public administration, healthcare, finance, corporate governance, and human resource management. The review findings reveal that AI-assisted governance systems generally enhance accountability through automated auditing, explainable decision-making, predictive risk assessment, and continuous compliance monitoring. Several studies reported improvements in governance performance, ethical compliance, and risk management compared with conventional governance models. In addition, stakeholder trust tends to increase when AI systems incorporate transparency, fairness, and explainability features that allow users to understand and evaluate algorithmic decisions. Despite these advantages, important challenges remain, including unclear responsibility attribution, the lack of standardized AI governance and auditing frameworks, and potential trust erosion caused by excessive dependence on automated systems. The effectiveness of AI-assisted governance is also influenced by organizational context, leadership commitment, governance maturity, and the extent of human oversight. Overall, AI-assisted governance offers substantial potential to strengthen accountability and stakeholder trust when supported by robust ethical safeguards, transparency measures, and clearly defined responsibility structures. These findings contribute to the ongoing discussion of responsible AI governance and provide practical insights for organizations pursuing governance innovation.
M. Mar, Ing. Nikolai Fabian Sebastián Yucra Añazco, Delia Nieves Coaquira Pari· Journal of Organizational an...· 0 citations
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