The Challenge
A top-5 national bank in Spain wanted to pursue AI but lacked a coherent strategy for where to invest. With significant capital at stake and no clear framework for evaluating which AI use cases to prioritize, the bank risked misallocating resources on low-impact pilots while missing transformative opportunities. Without strategic alignment across data infrastructure, governance, people, and ethics, scattered AI investments would fail to generate meaningful ROI or competitive differentiation.
What They Built
Deloitte delivered a five-pillar AI strategy covering Strategy, Governance, Data & Technology, People, and Ethics & Regulations — plus stakeholder workshops generating 250+ use cases, a weighted prioritization matrix, business case development, four production MVPs, and a vendor-agnostic microservices architecture for future deployment.
Deloitte structured the engagement across five pillars from the outset: Strategy, Governance, Data & Technology, People, and Ethics & Regulations. Rather than prioritizing use cases based on enthusiasm, the team ran stakeholder workshops across the bank to surface 250+ AI opportunities — each then evaluated against a weighted matrix aligned to the bank's stated cost-efficiency goals. Business cases were built for the top opportunities. Four MVPs were selected and developed, with three additional MVPs initiated as the first four moved toward production scaling. In parallel, Deloitte designed a vendor-agnostic microservices-based AI architecture with ELT/ETL pipelines and RAG infrastructure — built specifically to avoid vendor lock-in and allow future use cases to deploy faster. Operating within a regulated banking environment, governance, data residency, and ethics requirements were embedded from day one rather than appended at go-live. The engagement ran approximately 6–12 months from strategy through initial production.
AI Role
Four MVP use cases were selected, scoped, and developed in phase one — three additional MVPs were then initiated, with the original four moving into production scaling.
Infrastructure
• Microservices-based AI architecture • ELT/ETL data pipelines • RAG (Retrieval-Augmented Generation) infrastructure • Docker containerization
Integration Points
• ELT/ETL pipelines connected to bank data sources for AI use case ingestion • RAG layer indexing institutional knowledge for MVP applications • Microservices API layer enabling modular AI use case deployment • Governance and ethics framework integrated into data access controls
Best for large financial institutions or regulated enterprises that have begun exploring AI but lack a structured framework to prioritize investments, align stakeholders, and build infrastructure capable of scaling beyond isolated pilots.
