As AI evolves from simple language tools to intelligent agents capable of complex reasoning and decision-making, data strategy has become the foundation of true AI readiness. The value GenAI can unlock—across efficiency, insight, and innovation—depends on clean, unified, and interoperable data. Understanding and investing in this foundation is now a strategic imperative for senior business leaders. The right questions can reveal whether your organization's data strategy is built for experimentation—or ready to scale.
Broadridge's 2025 Digital Transformation & Next-Gen Technology Study confirms that Generative AI has shifted from being a novel concept to becoming foundational to how financial firms operate. This year, 72% of firms report moderate to large GenAI investments, up from 40% in 2024, with 35% of firms expecting to see their GenAI investments pay off within six months. Nearly half say there’s growing internal pressure to adopt these tools more widely. Yet major data challenges persist: 47% cite persistent data silos, 40% struggle with data quality, and 52% say they’re only able to leverage a small portion of their data for meaningful insights.
The industry has long talked about building a single source of truth. But that’s easier said than done. “Just because you’ve put all your data in a big lake, it doesn’t mean we can all go swimming,” said Stephanie Clarke, Broadridge’s head of international strategy and corporate development. “The real value lies in enabling disparate data sets to communicate effectively with one another, guided by a well-defined data and governance strategy.”
To move from aspiration to measurable impact, business leaders need to ask the right questions—ones that expose gaps, unlock value, and future-proof operations.
Q1: Is our data infrastructure designed for enterprise-scale AI?
This question gets to the heart of long-term AI readiness. Many firms are building impressive AI pilots, but few are making the leap to scalable solutions that can drive value across the business. That leap almost always requires foundational upgrades—at the data level.
“Many firms are still struggling to scale AI because their infrastructure wasn’t built for it,” explains Charlie Novicki, VP, Product Management at Broadridge. “Legacy systems with rigid structures don’t support the kind of fluid, cross-application data access that modern AI requires.”
Even well-intentioned solutions can backfire. “We see firms trying to solve this, but in the process, sometimes they’re creating new silos instead of breaking old ones,” Charlie adds. The result: fragmented insights and AI tools that deliver limited value outside isolated use cases.
The real test isn’t whether AI works in a lab—it’s whether your data architecture can support clean, connected, and real-time access across the enterprise. Without that, AI application development will remain trapped in the pockets of data excellence, rather than transforming the whole organization.
Q2: How are we balancing AI innovation with strategic data governance?
As AI becomes more embedded in business workflows, governance has taken center stage—not just for compliance, but for ensuring that AI is accurate, explainable, and secure. Leaders need to ask whether their current frameworks are evolving fast enough to support responsible innovation.
That means understanding what’s feeding your AI: Is the data accurate? Do you know its origin? Can you explain the output? These questions matter—especially in financial services, where risk and regulatory expectations are high.
More firms are embedding governance directly into their systems through role-based permissions, secure APIs, and retrieval-augmented generation (RAG) that only pulls from trusted internal sources. It’s not just about guardrails; it’s about designing for trust from the start.
Good governance also depends on how well different teams—tech, compliance, legal, and risk—work together. With regulations changing fast, your framework needs to be adaptable, not just compliant. Aligning AI use cases with business priorities, and then resolving upstream data issues, is the best way to deliver value without losing control.
Strong governance isn’t about slowing innovation—it’s what enables it to scale responsibly.
Q3: Are our vendors doing their part to accelerate our data transformation?
This question challenges whether your external partners are helping you move forward—or holding you back. In today’s data-driven environment, key vendors should act as strategic collaborators, not just service providers.
“The cloud is transforming how data is shared in the financial services ecosystem,” explains Charlie. “We’re seeing a shift away from raw data feeds toward direct connections between virtual data warehouses—meaning data is streamed to its destination in a ready-to-use format.”
This eliminates many of the transformation and integration headaches that used to slow things down. “You get to AI-ready data faster—without months of prep work,” says Charlie. “Firms working with cloud-native partners are seeing their time-to-value speed up significantly.”
The best vendor relationships don’t just deliver technology—they help you align with evolving standards, avoid duplication, and establish interoperability across teams, systems and organizations.
By focusing on these three questions, business leaders can ensure their data strategy is built for more than just pilots. The answers will reveal whether your organization is truly preparing for enterprise-scale AI—or just experimenting with the idea.