Leading Autonomous AI: Review of Governance Frameworks, and the Scholar-Practitioner Gap in Financial Services for C-Suite Executives

The emergence of Agentic Artificial Intelligence (Agentic AI)—autonomous systems capable of independent reasoning, planning, and executing actions across enterprise environments—presents a defining strategic paradox for Managing Directors and C-Suite executives in financial services. These leaders face an unprecedented challenge: aggressively deploying autonomous AI systems to drive operational efficiency and competitive advantage while maintaining unyielding regulatory compliance, operational stability, and risk management. This paper examines this paradox through a dual lens of practitioner job descriptions and scholarly literature. Practitioner sources reveal that executives are expected to simultaneously architect cloud-native AI platforms, operationalize Model Risk Management (MRM) frameworks for autonomous agents, define portfolio prioritization rubrics for agentic systems, establish human-in-the-loop and human-on-the-loop control mechanisms, and deliver quantifiable ROI—often without established playbooks or precedents. These roles demand deep fluency across a complex technical stack, including multi-agent orchestration frameworks, tool-use architectures, MLOps for agent monitoring, and governance systems for autonomous decision-making. Scholarly literature offers foundational insights through transdisciplinary research models, temporal perspectives on the academic-practitioner gap, and critical pragmatism as a bridging philosophy. However, a critical gap exists at the intersection of agentic AI implementation and executive strategic leadership: there is no empirical understanding of how senior leaders actually navigate the organizational, regulatory, and technical complexities of scaling autonomous AI systems in highly regulated environments. Adopting a Scholar-Practitioner approach, this proposed research will investigate how Managing Directors, CIOs, CTOs, and CDOs in banking and insurance navigate these challenges. The study will employ a qualitative multiple-case study design, integrating adaptive leadership theory with AI governance constructs to explore how executives balance innovation imperatives with control requirements in the age of autonomous AI. Findings will contribute actionable frameworks for executive decision-making, organizational design, and risk governance, bridging the gap between academic theory and practitioner need.