The Challenge
A $2B regional bank knew they needed AI but didn’t know where to start. They’d seen competitors deploy chatbots and fraud detection, but lacked the internal expertise to evaluate vendors, assess build-vs-buy tradeoffs, or prioritize use cases. Previous attempts at “AI strategy” had produced consultant slide decks that gathered dust.
Our Approach
We delivered a focused AI Advisory engagement designed to produce an actionable roadmap — not a theoretical framework:
AI Readiness Assessment: Evaluated the bank’s data infrastructure, regulatory posture, talent capabilities, and technology stack using BenchMark. Identified that their data warehouse was AI-ready but their feature engineering pipeline was a critical gap.
Use Case Prioritization: Scored 25 potential AI use cases across ROI potential, implementation complexity, regulatory risk, and data readiness. Narrowed the focus to 5 high-impact, low-risk opportunities for the first year.
Build-vs-Buy Analysis: For each prioritized use case, evaluated vendor solutions against custom development, factoring in the bank’s specific regulatory requirements (OCC, FDIC, BSA/AML compliance).
3-Year Roadmap: Delivered a phased implementation plan with clear milestones, budget requirements, talent acquisition needs, and governance framework — designed to deliver measurable ROI by Q1.
The Results
- $4.2M in first-year savings: The top 3 use cases (fraud detection, document processing, and loan underwriting assistance) delivered measurable ROI within 9 months.
- 25 use cases evaluated, 5 prioritized: Clear, data-driven prioritization replaced the “boil the ocean” approach of previous strategy efforts.
- Board-approved AI governance framework: Established clear policies for model risk management, bias monitoring, and regulatory compliance.
- Internal AI team hired and operational: The roadmap included a talent plan that guided the bank in building a 6-person internal AI team.