How FSIs Can Accelerate AI Readiness: Survey Insights
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Financial Services Institutions (FSIs) have always moved fast when the reward justifies the risk. Today, that reward is AI. From fraud detection to predictive analytics, it promises faster decisions, sharper insights, and greater operational efficiency.
But here’s the paradox: 67% of FSIs already run AI in production, yet only 8% consider their data infrastructure “state-of-the-art.” That gap between ambition and readiness isn’t just a technical issue. It’s a business risk.
Indicium’s 2025 AI Readiness Report reveals how FSIs are racing toward AI while dragging legacy systems, fragmented architectures, and unresolved governance challenges behind them. We surveyed over 670 IT leaders — including 165 from financial services — to understand how organizations are preparing for enterprise-scale AI.
The takeaway? FSIs aren’t short on ambition. But they are short on what AI actually needs to succeed: modern, trustworthy, and integrated data.
Outdated Infrastructure Still Blocks AI Readiness in Financial Services
Before they even begin to scale AI, most FSIs are already facing internal headwinds. More than half (52%) of IT leaders in financial services admit their data infrastructure was outdated or aging before modernization started. Just 8% felt confident enough to call it state-of-the-art.
That’s not a minor detail, it’s the ground floor. And when the foundation is brittle, the structure above starts to crack under pressure.
These legacy platforms weren’t built to handle what AI demands today. Slow processing, rigid pipelines, and scattered storage turn real-time insight into a long shot. Integration takes too long. Trust erodes quickly. Innovation starts to look like a risk.
Yet modernization often stalls at surface level. Teams migrate to the cloud, adopt new tools, but keep the same fragmented architecture, assumptions, and bottlenecks.
So the real question becomes: what kind of infrastructure are you actually building? One that patches pain temporarily, or one that unlocks AI as a core capability across the business?
FSIs need to move from modernization to reinvention. That means designing platforms for interoperability, speed, and control so data can actually move, serve, and scale.
Big Goals, Fragile Foundations
There’s no doubt about intent. 72% of FSIs say AI enablement drives their data modernization efforts. The same percentage points to improving data integration. The ambition is loud and clear.
But then reality sets in: 46% of FSI leaders say they weren’t prepared to use their data in AI tools before those efforts began.
Behind every ambitious AI initiative is a tangle of issues too familiar to ignore: poor data lineage, inconsistent standards, limited visibility, and governance models that kick in only after something breaks. No matter how aggressive the roadmap, if your data house is disorganized, the finish line keeps slipping away.
Here’s what most teams miss: Good data doesn’t just appear because AI is a priority. You have to design for it. Plan for it. Fund it.
What Shifts the Game
Start treating data readiness as a core business capability. Build discipline around data contracts, ownership, and observability. Automate quality checks. Don’t wait for governance to catch up. That’s how you turn AI ambition into something real, repeatable, and trusted.
The First Wins Are In. Now Comes the Hard Part
AI isn’t hypothetical anymore. FSIs have moved beyond theory into deployment: 67% already run AI across departments, and it’s showing up everywhere:
- Automating back-office operations
- Managing data quality
- Supporting fraud detection and business decisions
These are real systems, in production, delivering value.
But what worked in one department doesn’t automatically work everywhere else. Scaling AI across an enterprise brings a whole new set of challenges.
Different teams use data differently. Compliance rules shift. Pipelines that were fine for one model break with added volume or complexity. And most critically: governance and architecture rarely scale at the same pace as enthusiasm.
So while 74% of FSIs say their top priority is scaling AI, many are stuck reworking what they’ve already built just to keep it from falling apart.
Organizations that scale well build infrastructure that’s modular, reusable, and monitored end-to-end. They treat AI not as isolated tools, but as connected capabilities. When that happens, scale becomes a process, not a gamble.
Tools Don’t Build Readiness. People Do
AI readiness in financial services depends as much on people as it does on platforms. The data makes that clear: 70% of FSIs point to internal training as a top accelerator, and 46% say stronger partnerships are critical.
Tech alone doesn’t create capability. AI success happens when teams understand how to connect infrastructure, models, governance, and business impact. But in many organizations, skills and context are uneven. Engineers build without visibility into regulatory risk. Analysts depend on unreliable data pipelines. Leaders push for AI results without clarity on what “ready” actually looks like.
The fix starts inside. Training must be structured, ongoing, and aligned to real business outcomes. And it extends outside. The best partners don’t just deliver, they educate. They leave teams better equipped to sustain and scale AI long after the engagement ends.
What FSI Data Leaders Must Do Next
Financial services have already proven they can adopt AI. The challenge now is scaling it with consistency, trust, and impact. That requires more than a roadmap, it takes AI readiness built into platforms, teams, and decisions.
Early results show promise, but the gaps are still wide. Too many architectures can’t support real-time workloads. Governance remains reactive. Skill gaps slow execution. And siloed efforts lead to wasted investment.
AI readiness means your systems can support what your strategy demands — without rework, delays, or compromises. It means data is clean, accessible, and governed by design. And it means your teams are equipped to push forward with confidence.
FSI data leaders who want to lead the next wave of AI innovation must act now. Rebuild the foundation. Align tech with business goals. Create the conditions where AI isn’t a bottleneck, it’s a multiplier.
Download the 2025 AI Readiness Report to benchmark where your organization stands and see what leading FSIs are doing differently.
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