Responsible AI Frameworks for Enterprise Adoption: A Stakeholder-Centric Approach

Abstract

The integration of Artificial Intelligence (AI) into core business processes presents profound opportunities for innovation alongside significant ethical risks, including systemic bias, lack of transparency, and accountability deficits. While numerous high-level principles for Responsible AI (RAI) have been proposed, enterprises universally struggle with the practical operationalization of these concepts, creating a critical "principles-to-practice" gap. This research addresses this gap by developing and analyzing a stakeholder-centric framework for enterprise RAI adoption. The central thesis is that effective RAI implementation cannot be achieved through purely technical or top-down governance mechanisms; it necessitates a socio-technical approach that deeply integrates the requirements of diverse stakeholders (e.g., employees, customers, regulators, and data scientists). This study employs a conceptual development and mixed-methods validation methodology, simulating a comparative case analysis of enterprises to assess implementation strategies. We propose the Stakeholder-Centric Responsible AI Integration Model (SC-RAI-M), structured around four critical pillars: Ethical Governance & Accountability (EGA), Technical Robustness & Transparency (TRT), Continuous Stakeholder Engagement & Education (SEE), and Regulatory & Contextual Alignment (RCA). Simulated findings from the comparative analysis demonstrate that organizations prioritizing the SEE pillar in conjunction with technical and governance structures achieve significantly higher RAI maturity, reduced audit flags, and greater stakeholder trust. The research concludes that stakeholder integration is not merely an ethical addendum but a core functional requirement for mitigating risk and ensuring the sustainable, scalable adoption of responsible AI systems. This work provides a practical, validated model for enterprises seeking to embed responsibility throughout the AI lifecycle.