AI in financial services has crossed a threshold. It’s no longer a differentiator; it’s table stakes. The institutions moving fastest are already pulling away, and what’s separating them from everyone else isn’t the AI itself. It’s the ability to move data, all of it, from wherever it lives, fast enough and reliably enough to feed the AI and automation solutions designed to drive business transformation.
Three trends are driving this. Here’s what each one actually requires from the data layer.
Trend 1: AI Is No Longer Watching — It’s Acting
AI in financial services used to be passive, with machine learning models flagging anomalies, generative AI surfacing recommendations, and helping analysts move faster. That’s changing. Agentic AI systems don’t just advise, they act. Autonomously. Across entire workflows. Loan underwriting that gathers data, runs credit analysis, checks compliance, and renders a decision without a human in the loop. Fraud detection that investigates a suspicious transaction, cross-references account history, and takes protective action, all in real time. Gartner projects that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. In financial services, where automated workflows carry regulatory weight and fiduciary responsibility, that shift changes what AI failure actually means. When systems act autonomously, stale or incomplete data isn’t just an operational problem. It’s a legal, regulatory, and reputational one.
When AI acts autonomously, stale or incomplete data isn’t just an operational problem. It’s a legal, regulatory, and reputational one.
Agentic AI is a multi-step decision chain where errors introduced early compound at every subsequent step. An autonomous underwriting agent is simultaneously pulling from credit bureaus, transaction history, property records, income verification, and compliance databases. If any one source lags by even a few hours, the agent is operating on an incomplete picture and making decisions accordingly.
Gartner research puts the cost of poor data quality at $12.9–$15 million per year for large organizations, a figure that scales in an agentic environment where automated decisions compound across workflows. Institutions investing in data infrastructure now are the ones whose AI will be trustworthy enough to run without human checkpoints. Those that don’t will keep trading efficiency gains for oversight.
Trend 2: The Data Infrastructure Reckoning
The barrier to AI adoption in financial services isn’t ambition. It’s what’s underneath: infrastructure built for a different era. Most banks still run on systems designed when batch processing was the norm, when retail banking, commercial lending, wealth management, and treasury operated in silos never meant to share data in real time. The result: fragmented repositories, inconsistent schemas, and pipelines built for workflows that no longer exist.
Mosaic Smart Data survey research found that 83% of banks want real-time analytics, and that same 83% lack real-time access to transaction data due to fragmented systems. Separately, 66% struggle with data quality and integrity issues. These aren’t exceptions. They’re the baseline most institutions are building their AI strategies on.
83% of banks want real-time analytics. 83% lack real-time access to transaction data. That gap is where AI strategies stall.
The industry has responded with waves of modernization investment, scalable data lakes, streaming pipelines, and cloud migration. The direction is right, and adaptability to AI-driven workflows is clearly the goal. But the transition period, when some systems are modernized, and others aren’t, creates a dangerous window where data isn’t broken, just subtly out of sync. The new fraud model trains without data from a legacy core still being migrated. The personalization engine draws on behavior data not yet reflected in the on-premises CRM. These failures don’t announce themselves; they’re easy to mistake for model problems rather than data problems.
Industry analysis shows financial services still run 70% of processes in batch mode despite 83% of institutions wanting real-time analytics. The right question before any AI initiative isn’t ‘what model should we use?’ It’s ‘can we guarantee every system feeding that model is working from the same, current picture of reality?’ For most institutions today, the honest answer is no.
Trend 3: Hyper-Personalization and the Real-Time Data Imperative
Customer expectations in financial services have been permanently recalibrated. Not by other banks, but by technology companies. Consumers who receive highly relevant, contextually aware experiences from retail and entertainment platforms bring the same expectations to their banking relationships.
McKinsey estimates personalization at scale in banking could generate $1.7–$3 trillion in global value. Yet only 4% of banks are currently using AI to deliver hyper-personalized experiences at scale.Research shows relevant, timely content can increase click-through rates by up to 200%. The gap between potential and reality is hard to overstate.
A personalization engine is only as intelligent as its data is current. Context has a half-life, and most data infrastructures aren’t built to respect it.
Personalization is the most data-latency-sensitive use case of the three. The context that makes an experience feel relevant, a recent purchase, a life event, or a service interaction that just happened, has a short half-life. The failure modes are familiar:
- A customer who just experienced fraud receives a credit limit increase offer the next morning, because the personalization model and fraud system haven’t converged.
- A wealth client who just called to reduce risk exposure gets an aggressive investment recommendation through the app, because the advisory interaction hasn’t reached the personalization layer.
- A customer who closed their account receives a retention offer after they’ve already left.
McKinsey research confirms 75% of consumers report frustration when content isn’t personalized to their actual situation. For banks, which hold more customer data than almost any other industry, that gap is increasingly hard to justify.
The Common Thread: Data Velocity
Step back, and the same constraint runs through all three trends. Agentic AI needs consistent data because errors compound downstream. Infrastructure modernization needs synchronization because the transition window is where failures hide. Hyper-personalization needs data current to the hour because context decays. All three demand data that moves, quickly, reliably, completely, across whatever combination of cloud, on-premises, edge, and partner systems it needs to reach.
The question financial institutions should be asking isn’t “how good is our AI?” It’s “how fast and complete is our data, everywhere it needs to be?”
For most institutions, the honest answer is qualified at best. Silos that don’t sync in real time. Cloud and on-premises environments are on different refresh cadences. Partner sources adding latency. And modernization work introduces its own synchronization challenges while it’s underway.
What the infrastructure actually needs to do
Meeting these requirements means the infrastructure must:
- Move large volumes of data at high speed across distributed environments, data centers, cloud regions, edge locations, branch infrastructure, and partner networks, without degradation in completeness or consistency.
- Stay synchronized throughout infrastructure transitions, so modernization doesn’t introduce the inconsistencies it’s designed to eliminate.
- Guarantee every AI model is always working from the same, current picture of reality, regardless of where it’s deployed.
- Do all of this at the scale and compliance requirements of a regulated industry, without creating new single points of failure.
These are operational prerequisites, not aspirational goals. The institutions that build this infrastructure now, before competitive dynamics fully resolve, will have an advantage that compounds over time.
The Window Is Open — But It Won’t Stay That Way
The AI capabilities being built today will be enabled or constrained by data infrastructure decisions being made right now. The dynamics are asymmetric: early movers compound advantages each quarter, while institutions that move fast without fixing the data foundation end up with AI that underperforms, personalization that damages trust, and modernization that leaves things worse than before.
The difference between institutions that react and those that stay ahead comes down to data, accurate, current, and complete. Resilio Active Everywhere gives organizations that advantage. By keeping high-volume datasets synchronized in near real time across the environments where institutions need current data available, Resilio ensures the AI, risk, and personalization systems built on top always have a clear, complete picture of reality.
With more current synchronized data using Resilio, your fraud, risk, and analytics teams and systems can surface signals earlier and operate on a more complete dataset. Scalable risk management starts with data you can rely on. In a market where data delays translate directly into financial exposure, the right infrastructure isn’t a nice-to-have; it’s a competitive advantage.
Institutions that build on Resilio don’t just respond to change, they get ahead of it.
Ready to see how? Schedule a demo today.
