Data Fluency: The Foundation for AI in Insurance

Legacy systems don’t just slow insurers down; they’re locking insurers out of the AI-driven future. Every day, the gap is growing between insurers that can act with agility and those still waiting on reports, between companies that can launch AI agents in weeks and those that need months just to scope the project.

The uncomfortable truth is that most insurers are sitting on an ocean of data they can’t fully use. Their legacy systems excelled at processing transactions, but were never designed to serve up complete, accurate, and instantly usable data to every part of the business.

In an era defined by AI, embedded distribution, and ever-shifting customer expectations, this is no longer sustainable. That’s why insurers need data fluency, or the ability for people and systems to find, access, and use high-quality data in real time. There are three pillars to achieving data fluency: availability, accessibility, and quality. Without these pillars, even the most promising digital and AI initiatives will fall short.

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Availability: Can you get your data whenever you need it?

Data availability starts with the core system. Insurers need an open, API-first platform capable of exchanging data in real time. This is a stark departure from many legacy systems, which rely on scheduled batch exports, proprietary data formats, or vendor-controlled reporting tools.

When data is available in real time, it changes how the business operates. Underwriters can see the full picture of a risk while they’re still evaluating it. Claims teams can act on new information immediately rather than waiting for a nightly refresh. Executives can monitor live KPIs and respond to market changes without relying on reports that are outdated before they’re printed.

Legacy constraints don’t just slow the pace of business; they also limit strategic agility. If data is only available through manual exports or vendor-managed queues, projects like deploying an AI agent, integrating with a partner ecosystem, or testing a new product variant take months instead of weeks. In contrast, when data is available on demand, experimentation and innovation become part of the everyday workflow.

Accessibility: Can you use your data however you need to?

Availability is meaningless if people and systems can’t easily access the data they need. Too often, insurers rely on IT teams to extract, transform, and deliver data. Business users must file a request, wait days or weeks, and receive a static report that may or may not answer the original question.

Modern systems make data accessible throughout the organization in multiple ways:

• Standardized APIs – Complete, documented, and consistent access to both data and system functionality
• Data lakes – Query both structured and unstructured data in real time without lengthy ETL processes
• Connectors – Move bulk data to enterprise analytics platforms without custom engineering
• Event streaming and webhooks – Broadcast every relevant action across systems in real time, enabling automation and faster decision-making
• User-friendly interfaces – Empower non-technical staff to generate reports and view operational metrics

Having a variety of data access methods enables both technical teams and business stakeholders to retrieve data on their own terms. They make it possible to capture not only transactional business data but also metadata and user activity; context that is invaluable for optimizing processes or orchestrating AI agents.

Accessibility also means portability. Organizations should be able to export their data in full, without intermingled vendor intellectual property, and move it to whatever environment best serves their needs. This openness is critical to avoiding vendor lock-in and ensuring long-term strategic flexibility.

Quality: Can you trust that your data is complete and accurate?

Even the most available and accessible data won’t deliver value if it’s incomplete, inconsistent, or inaccurate. Data quality requires intentional design, starting with the architecture of the core system itself. Modern platforms enforce consistent data models, validate inputs at the point of entry, and ensure that updates sync across the ecosystem in real time.

Legacy systems, by contrast, often maintain multiple versions of the same data in different modules or downstream systems. Updates made in one place don’t always make it to another, leading to mismatched records and manual reconciliation. These inconsistencies erode trust in reporting and make it harder to adopt advanced analytics or AI-driven processes.

High-quality data also depends on transparency. Decision-makers should know when and how data was last updated, what transformations it has undergone, and whether it meets the completeness thresholds required for a given use case. Without this visibility, it’s impossible to distinguish between a reliable insight and a flawed assumption.

Looking Ahead

The future of insurance will be shaped by those who treat data as a living, strategic asset—not a byproduct of transactions. This means moving away from legacy core systems and embracing modern, open, API-first systems that make data instantly available, easily accessible, and inherently trustworthy.

Generative AI, real-time underwriting, predictive claims management, and embedded distribution are within reach, but only if the underlying data is ready. For insurers, the question is no longer whether to modernize their data capabilities. It’s how quickly they can do it before competitors fluent in their data pull ahead.