Financial services institutions are making unprecedented investments in AI, yet only 31% can actually track returns on that spending. According to a 2025 Harris Poll of 506 financial services executives, 78% express high confidence in future AI results without any reliable measurement methods to back it up.
Most organizations cannot distinguish between AI initiatives that are genuinely underperforming and those that are succeeding but failing to meet unrealistic expectations.
Only 12% of financial institutions have successfully implemented enterprise-wide AI strategies. The remaining 88% are cycling through pilots with no clear path to scale. This whitepaper identifies the five critical blind spots preventing organizations from closing that gap and lays out a framework for measuring AI investment that actually holds up in a regulated environment.
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Scaling AI investments in financial services: The framework for measuring ROI
Key takeaways
- Most AI revenue forecasts are built on ambition, not analysis, and the consequences compound over time.
- The institutions that have broken through share three traits: robust data governance, end-to-end platform engineering, and systematic measurement capabilities.
- Scaling AI requires intelligent orchestration across the entire software lifecycle, not point solutions stitched together after the fact.