Prepare your data—or prepare to be disappointed by AI.
That’s the risk financial firms face as they increasingly implement artificial intelligence across trading, operations, and customer support in pursuit of faster decision-making, better client experiences, and greater operational efficiency.
Broadridge’s 2026 Digital Transformation & Next-Gen Technology Study underscores the technology’s momentum, finding that 80% of the nearly 1,000 financial firms surveyed are already using generative AI or predictive AI in their operations and processes.
Yet many AI initiatives stall or are abandoned, with firms often discovering that the biggest barrier to scaling projects is not their models or tools but data fragmentation—information spread across disconnected systems, labeled inconsistently, and stored in ways that make integration difficult. Much of this fragmentation reflects the reality that firms still rely on legacy systems, acquired platforms, and point-to-point integrations built for specific business needs.
When general-purpose AI is deployed in a fragmented environment, the result is often inconsistent output with limited business value. In practice, firms may see pilots fail to reach production because the underlying data cannot be trusted, connected, or interpreted consistently across the enterprise.
For competitive financial firms looking to accelerate AI adoption, solving this problem is a business imperative.
data architecture as a strategic capability, not simply a back-office cleanup project.”
Tom Carey, Global Head of Product and Technology at BroadridgeWhere to begin
Financial firms that successfully scale AI start with common data definitions, standardized data organization, and companywide governance so information about a trade, account, or event is understood the same way throughout the company.
The critical steps include:
1. Creating a shared understanding of key data. This starts by aligning the enterprise around a shared understanding of essential data. A data ontology can provide that foundation by defining key business concepts, describing them clearly, and mapping their relationships so systems and users interpret information the same way. It creates a shared semantic framework that can connect functions such as trading, operations, compliance, risk, and client service.
2. Implementing standard data models. The semantic layer serves as a blueprint for standardized data models, providing a consistent way to organize, label, and represent data across enterprise systems.
In other words, ontology establishes the shared language. The standardized data models operationalize language in databases, workflows, APIs, and applications.
Together, a data ontology and standardized data models give AI an essential foundation for consistent, reliable outcomes. This matters not just for efficiency, but for confidence: business leaders, operators, and compliance teams all need to know that AI outputs are grounded in data the organization understands and trusts.
3. Establishing an enterprise-wide governance framework. Ontologies and standardized data models define meaning and structure, but governance provides the ownership, rules, and controls needed to maintain data consistency across the business. This is especially important in financial services, where AI and automation must operate within strict regulatory requirements.
Minus this discipline, fragmentation has a way of creeping back in as teams make new decisions about data, systems, and workflows.
Without proper governance, even the most promising AI initiatives can fail. With it, companies can turn fragmented data into a trusted asset that can support AI with the resilience and control financial firms demand.
A strategic imperative
These challenges are not unique to financial services. Any organization that lacks data quality and governance will struggle to scale AI effectively. If data is siloed, defined differently across systems, or managed without a common control structure, it cannot provide the stable foundation AI needs to operate reliably. Without that foundation, AI cannot consistently interpret information in its full context.
This problem becomes more pronounced as firms move from experimentation to production.
A model might perform well against a single dataset or in a narrow use case—helping employees summarize meetings, draft content, or write code—but production environments demand more. AI must be able to connect and interpret data across systems, workflows, and functions. When that groundwork isn’t laid, results become harder to trust.
That’s why successful AI adoption starts not with new tools, but with the often unglamorous work of improving data quality and implementing the right controls.
In financial services, the firms that scale AI most effectively treat data architecture as a strategic capability, not simply a back-office cleanup project.