Financial services firms are under pressure to accelerate AI adoption and deliver measurable results. But many still rely on fragmented, aging data systems that can’t support the scale, speed, or accuracy that AI demands.

A new report by Indicium, based on a survey of nearly 700 IT professionals, highlights this gap. Over half of respondents in financial services say their data infrastructure is outdated. And while AI is high on the agenda, too many firms still lack the data foundation required for enterprise-scale AI adoption.

Here’s what the data reveals and what needs to happen next.

AI Adoption Takes Priority, But Foundation Falls Short

Financial institutions want to deploy AI. But their data systems can’t keep up.

• 52% of IT professionals in financial services say their data infrastructure was outdated or aging before launching a modernization effort.
• In non-financial industries, 44% said the same—highlighting deeper legacy challenges in financial services.

And readiness for AI? Still low before modernization began.

• 44% of financial services IT pros said they were only somewhat prepared to use their data in AI tools and apps.
• 46% admitted they were unprepared due to poor data quality or governance.
• In comparison, 48% of non-financial respondents felt somewhat prepared, and 40% felt unprepared.

For many, AI adoption isn’t being delayed by strategy. It’s being blocked by systems that weren’t built to handle it.

Firms Target Modernization to Enable AI

For most firms, data modernization isn’t just an IT upgrade, it’s a business move to support AI.

• 72% of financial services firms say the top reason for modernizing data systems is to prepare data for AI tools and applications.
• Another 72% say improving data integration across platforms is critical to supporting AI.

Non-financial services organizations report similar priorities:

• 70% focus on preparing data for AI tools.
• 65% aim to improve integration across systems.

This shift shows that AI adoption drives strategic change across architecture and governance. Firms that redesign their data foundation around AI move faster and unlock more value.

AI Use Is Growing, But Scaling Remains a Challenge

Adoption is underway—but scale remains limited.

• 67% of financial services firms have AI in production across departments.
• 59% use AI for back-office automation, including Finance and HR.
• 56% apply AI to improve data governance and quality.

By comparison:

• 61% of non-financial firms report AI in production.
• 59% use it for governance and quality.

Growth plans are clear:

• 74% of financial services firms aim to scale AI across departments in the next 2–3 years.
• 68% focus on AI for select business functions.

These goals require modern systems, integrated data, and strong cross-team execution.

Training and Partnerships Fuel AI Acceleration

Internal expertise and trusted partners speed up AI adoption.

• 70% of financial services IT leaders say better training would accelerate their progress.
• 46% say external partners play a key role.

In other industries:

• 62% say training is the top need.
• 47% rely on stronger collaboration with vendors.

AI adoption depends on data that’s accurate, integrated, and ready to scale. As Matheus Dellagnelo, CEO of Indicium, puts it:

“Companies want to adopt AI, but outdated systems block them. Data quality and architecture set the pace. Firms that fix the foundation move faster and lead with confidence.”

Explore the full findings, industry benchmarks, and strategic insights in Indicium’s latest research. Download the full report and build a stronger path to enterprise AI.