The rapid integration of artificial intelligence into corporate finance alters how capital is allocated, risk is assessed, and value is measured. Without deliberate governance, algorithmic decision systems can amplify bias, obscure accountability, and privilege short-term gains over durable performance. This paper develops a conceptual framework linking AI governance mechanisms to the quality of AI-augmented financial decisions and, ultimately, to sustainable value creation. Drawing on interdisciplinary scholarship in AI ethics, corporate governance, and sustainable finance, the framework identifies five governance dimensions: board-level oversight, algorithmic transparency, fairness audits, accountability structures, and ethical principles integration. These dimensions are proposed to enhance decision quality by reducing opacity, mitigating bias, and reinforcing long-term orientation. The model posits that decision quality mediates the relationship between AI adoption and sustainable value, while governance strength moderates this indirect path. The framework yields testable propositions and offers practical guidance for boards and regulators navigating the intersection of algorithmic capability and fiduciary duty. The paper contributes a governance-centric lens to the emerging literature on AI in corporate finance, moving the conversation from technical deployment toward institutional conditions that enable responsible, value-sustaining outcomes.