The AI Impact Report: Real Results from Real Financial Institutions
Stop reading case studies in isolation. This report brings together seven institutions’ AI deployments—with hard numbers on cost savings, automation rates, and member satisfaction—to show what the data looks like at scale.
There’s no shortage of AI case studies. There is a shortage of institutions willing to put multiple stories in the same document and let the aggregate data speak. That’s what this report does. Seven credit unions and community banks, representing a range of sizes and deployment types, with real numbers: automation rates, cost per interaction, satisfaction scores, and time-to-value. The variance is real. The patterns are also real—and they tell you what matters most when deploying AI at your institution.
60–80%
Automation rate achieved by Greater Louisville Credit Union with interface.ai
76%
Of all interactions handled by AI at Bank of Guam
$4.4M
In annual savings reported by Neighborhood Credit Union
Written for boards and the people who brief them.
“The numbers don't lie. We went from skeptical about AI to fully convinced within the first six months of seeing what it did for our members.”
interface.ai client featured in the report
Common questions
Are these results typical, or cherry-picked for the best-performing deployments?
The report includes institutions across a range of performance levels—including ones that started slower and built up over time. The goal is an accurate picture, not just the headline wins.
How do results compare between credit unions and community banks?
The report breaks down results by institution type, size, and primary use case—so you can find benchmarks that are relevant to your specific context.
What made the highest-performing institutions different?
The report identifies three predictors of top performance: strong executive sponsorship, a clear automation target set before launch, and willingness to iterate in the first 30 days post-launch.
How long did it take these institutions to see measurable ROI?
Across the seven institutions, average time to measurable ROI was 4.5 months post-launch. The fastest was 90 days; the most complex deployment took 7 months.
Is this data independently verified?
The self-reported metrics were reviewed for consistency by the interface.ai research team. Third-party verification is noted where institutions provided audit data.
Get the Report
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