Enabling Prudent, Scalable AI Investment

Enabling Prudent, Scalable AI Investment: A Top 10 US Bank’s Approach to Generative AI Business Cases

A leading bank moved beyond AI hype and experimentation by adopting a structured economic modeling framework—helping leaders evaluate cost, risk, and value with clarity and discipline.

The Challenge

Traditional business case methods were not equipped to capture the unique economic profile of generative AI. The Bank risked:

• Underperforming pilots unable to scale
• Budget overruns from unforeseen lifecycle costs
• Difficulty distinguishing hype from meaningful value
• Heightened vendor lock-in and regulatory risk

The challenge was clear: AI projects carry costs such as model tuning, explainability, oversight, and token-based consumption that do not exist in conventional IT initiatives.

The Solution

The Bank implemented Rational Exponent’s Generative AI Business Case & Economic Modeling Framework, purpose-built to address the full complexity of AI economics.

Framework Delivered

• AI-Specific Costing — Captured drivers such as data curation, drift management, compliance audits, and tokenization.
• Value Quantification — Measured both tangible outcomes (savings, efficiency) and intangible benefits (decision quality, personalization).
• Prioritization Model — Ranked AI use cases by feasibility, value realization, and strategic alignment.
• Long-Term Risk Modeling — Exposed risks like vendor dependency and escalating cloud costs before they materialized.

Strategic Impact

This framework became a cornerstone of the Bank’s enterprise AI strategy:

• Leaders gained a clear, holistic view of AI’s cost structures and value pathways.
• AI adoption shifted from opportunistic pilots to intentional, scalable investment.
• Funding decisions now balance innovation with governance and risk control.

Results

The framework revealed a deeper truth: the long-term cost of explainability, fairness, and operationalization often exceeds the initial build. By surfacing these commitments early, the Bank avoided shortsighted investments and created a foundation for responsible, high-impact AI deployment.