Governance and Economic Sustainability of AI-Driven Decision Intelligence: A Case Study of Bank of America

In the banking sector, the adoption of Artificial Intelligence (AI) is often driven by a deterministic narrative 
that associates automation with direct increases in productivity and risk quality. However, empirical evidence 
signals a persistent "execution gap": value does not emerge automatically, remaining tied to fragmented 
initiatives and structural limitations of the operating model and governance. This paper addresses this gap by 
proposing a decision-making framework for the adoption of AI-driven Decision Intelligence, establishing 
theoretical continuity between integration technologies (SOA) and new paradigms of algorithmic decision
making. Through a qualitative design based on an archival single case study and the application of pattern 
matching logic, the research analyzes the case of Bank of America as an empirical contrast to demonstrate 
how the transformation of AI into a scalable decision-making capability requires systemic alignment between 
infrastructure and control frameworks. The analysis identifies five mechanisms of value generation: strategic 
anchoring (enterprise-by-design), the establishment of proprietary information assets (data moat), 
governance as an accelerator of institutional trust, socio-technical integration of personnel, and the 
decoupling of operating costs (operational leverage). The study offers a threefold contribution: theoretical, 
by evolving adoption models for integration technologies; empirical, by validating industrial scalability 
mechanisms; and managerial, by providing a strategic roadmap for the transition from experimentation to 
operational Decision Intelligence.