DOI: 10.36347/sjet.2023.v11i12.001

Pages: 315-326

As artificial intelligence (AI) continues to shape decision-making across industries, concerns about opaque algorithmic systems have sparked demands for greater transparency, fairness, and accountability. This study investigates algorithmic transparency as a strategic enabler in AI-driven enterprises, examining its role in fostering trust, enhancing brand equity, and ensuring regulatory and operational readiness. Grounded in Stakeholder Theory, Trust Theory, Signaling Theory, and Responsible AI principles, the research adopts a mixed-method approach, combining survey data from industry professionals with qualitative insights from case studies in finance, healthcare, and e-commerce. The findings reveal that algorithmic transparency significantly mediates the relationship between AI implementation and key organizational outcomes. Transparent AI systems were shown to increase consumer trust, improve perceptions of ethical leadership, and offer a competitive branding advantage. Furthermore, firms that proactively embraced explainable AI and documentation frameworks were better positioned to comply with emerging regulatory standards and reduce operational risks associated with algorithmic failures. This paper contributes to the growing body of literature on ethical and strategic dimensions of AI by positioning algorithmic transparency not as a compliance burden but as a competitive edge. The study recommends that organizations embed transparency into system design, governance, and communication practices to unlock the full trust-building and branding potential of AI technologies. Implications for policy, cross-sectoral collaboration, and future research directions are also discussed.