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When Regulation Meets Innovation: How AI Governance Shapes Compliance and Organizational Performance

Sep 2026 · Journal of Business Practice and Academic Research · pp. 2 · 0 citations
Ethics and Social Impacts of AI

TL;DR

The proposed framework treats AI governance as a strategic capacity of an organization, with accountability, transparency, explainability, human oversight and risk management integrated into the AI lifecycle, ensuring responsible innovation, continuous regulatory compliance and sustainable value creation for the organization.

Abstract

With the rapid advancement of Information Technology, including Artificial Intelligence (AI), organisations are innovating much faster, yet at the same time are also dealing with the complexities of regulation and governance. But as AI regulations continue to develop, organizations are faced with a challenge to implement responsible AI and to meet regulations and sustain organizational performance. Although there is previous research on the governance of AI, organizational performance, and regulatory compliance, some have considered them individually and fewer have considered how they can be linked in a cohesive conceptual structure. This research paper aims to fill this gap by presenting an integrated AI Governance Framework that was systematically identified, selected and synthesized from the existing body of scholarly work. Using a conceptual research design approach, the study draws on literature from AI governance, responsible AI, regulatory compliance, and organizational management to create the conceptual pillars of the interrelationships between AI governance, AI compliance, AI innovation, and organizational performance. The proposed framework treats AI governance as a strategic capacity of an organization, with accountability, transparency, explainability, human oversight and risk management integrated into the AI lifecycle, ensuring responsible innovation, continuous regulatory compliance and sustainable value creation for the organization. By bringing together disparate governance perspectives into a coherent conceptual model and offering practical advice for managers and policymakers who are working on designing adaptive AI governance strategies, the framework makes a step toward contributing to AI governance scholarship. It also provides a theoretical basis for future empirical studies to test and improve the suggested framework in various organizational and regulatory settings.

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