How One Spanish Bank Surfaced 250+ AI Use Cases

A Spanish bank's strategy team bottled stakeholder input into a weighted prioritization matrix — surfacing 250+ AI use cases and launching four MVPs on a vendor-agnostic stack.

250+

Ranked against strategic goals

4–6 months

Implementation Time

Juan Alfageme Puga

AI Strategy Consultant

Deloitte Spain

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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 led a five-pillar AI strategy engagement covering Strategy, Governance, Data & Technology, People, and Ethics & Regulations. Through stakeholder workshops, the team generated over 250 AI use cases, applied a weighted prioritization matrix aligned to the bank's cost-efficiency goals, and built business cases for the top opportunities. Four MVPs were selected and developed. Simultaneously, an agnostic microservices-based AI architecture was designed to accelerate future use case deployment and avoid vendor lock-in. Three additional MVPs were then initiated, with the original four moving toward production scaling.

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

AI systems serve two functions in this implementation: RAG-based retrieval and analysis powering the MVP use cases selected from the prioritization process, and the microservices AI architecture acting as the deployment infrastructure enabling future use cases to be built and scaled without vendor lock-in. The specific AI models within the MVPs are not disclosed.

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

Impact

250+ AI Use Cases Identified

Stakeholder workshops generated more than 250 AI use cases across the bank, then ranked using a weighted prioritization matrix tied to the client’s strategic objectives.

4 MVPs Launched, Now Scaling

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.

250+ AI Use Cases Identified

Stakeholder workshops generated more than 250 AI use cases across the bank, then ranked using a weighted prioritization matrix tied to the client's strategic objectives.

Technology Utilized

Implementation Complexity

Revenue Growth

Cost Reduction

Time Savings

The engagement required building a five-pillar AI strategy, developing business cases for 250+ use cases, designing a microservices-based AI architecture with ELT/ETL pipelines and RAG infrastructure, and developing four production MVPs — all within a regulated banking environment. This is a substantial, multi-month strategic and technical undertaking requiring deep consulting expertise and engineering capability.

Best Fit For

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.

Juan Alfageme Puga

AI Strategy Consultant

Deloitte Spain

Focused on all aspects of AI, from strategic plans to scaling AI Apps

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Industry:

Financial Services

Business Function:

Executive & Strategy

Finance & Accounting

Company Size:

5,001+

Project Cost:

Cost not disclosed

Ownership:

Public Company

Organization Type:

Public Company

AI Pattern:

AI Workforce Enablement

Decision Support & Scoring

Value Type:

Revenue Growth

Cost Reduction

Time Savings

AI Model:

Frequently Asked Questions

How did a large financial services institution surface 250+ AI use cases with a structured enablement strategy?

The experts structured the engagement across five pillars, Strategy, Governance, Data & Technology, People, and Ethics & Regulations, and ran stakeholder workshops across the bank rather than prioritizing by enthusiasm. More than 250 AI use cases were surfaced, each scored against a weighted matrix tied to the bank's cost-efficiency goals, with business cases built for the top opportunities.

What AI tools and approach were used at the financial services institution?

The team designed a vendor-agnostic, microservices-based AI architecture using ELT/ETL pipelines, RAG infrastructure, and Docker, built to avoid vendor lock-in and let future use cases deploy faster. The approach combined AI workforce enablement with decision support and scoring through a weighted prioritization matrix.

What results did the financial services institution achieve?

More than 250 AI use cases were identified and ranked, four MVPs were selected and developed, and three additional MVPs were then initiated as the original four moved toward production scaling.

How long did the engagement take?

Approximately 6–12 months, running from strategy through initial production.

Who is this AI strategy approach best for?

Large financial institutions and regulated enterprises that have begun exploring AI but lack a structured framework to prioritize investments, align stakeholders, and build infrastructure that scales beyond isolated pilots.