Financial services institutions are under mounting pressure to adopt AI. The promise is real: faster decisions, sharper risk models, more personalized client experiences. But for many firms, the path to production AI is blocked by something far more fundamental than model selection or compute budgets.
The barrier is data infrastructure.
Legacy systems, fragmented pipelines, and poor data quality create the kind of friction that makes AI unreliable, slow, or simply impossible to deploy at scale. These aren't edge cases. They're the norm across banking, insurance, capital markets, and asset management.
A new report by Indicium AI, based on a survey of nearly 700 IT professionals, highlights just how widespread these challenges are — and what the most AI-ready firms are doing differently.
The Infrastructure Gap Is Holding AI Back
Most FSI organizations have invested heavily in AI tools and talent. But those investments stall when the underlying data environment can't support production-grade AI. The survey found that a significant share of FSI firms are still running on infrastructure that lacks the consistency, speed, or accuracy that AI demands.
Common blockers include:
- Siloed data across business lines and legacy platforms
- Inconsistent data quality that undermines model outputs
- Slow pipelines that can't support real-time AI inference
- Governance gaps that create regulatory and compliance risk
These aren't technology limitations alone. They reflect years of underinvestment in the data layer — and they don't disappear by deploying a new AI tool on top.
What AI-Ready FSIs Are Doing Differently
The firms making real progress on AI share a common trait: they treated data modernization as a prerequisite, not an afterthought.
That means building unified data platforms, establishing governance frameworks, and creating pipelines that are fast, reliable, and auditable. It also means aligning data strategy with business outcomes — not just technical benchmarks.
As Matheus Dellagnelo, CEO of Indicium AI, puts it:
"Companies want to adopt AI, but outdated systems make that nearly impossible. The firms that move fastest aren't spending more on AI — they're spending smarter on the data foundation that makes AI work."
The Cost of Waiting
For FSIs, delayed AI adoption isn't just a missed opportunity. It's a competitive risk. Firms that modernize their data infrastructure now will be positioned to deploy AI at scale across credit, compliance, fraud, and client services. Those that don't will face increasing pressure from more agile competitors — and regulators who expect AI to be explainable, auditable, and accurate.
The window to build that foundation is narrowing.
Read the Full Report
Explore the full findings, industry benchmarks, and strategic insights in Indicium AI's latest research. Download the AI Readiness Report for FSI.
