AI ambition meets data reality: What every insurance executive should know

Artificial intelligence is transforming insurance from claims and underwriting to hyper-personalized policyholder experiences. Every insurer is racing to embed AI across operations. But one truth remains: your AI can only move as fast as your data.

Too many AI initiatives fail not because of flawed models, but because of flawed data—data that’s incomplete, inconsistent, or fragmented across policy, product, and claims systems. When AI acts on bad data, even the most minor errors multiply, eroding efficiency, profitability, and customer trust.

The future of insurance automation belongs to leaders who recognize that AI data readiness—not just algorithm sophistication—is the true differentiator in AI success.

The promise and peril of agentic AI

Agentic AI represents a step change for the industry. Unlike traditional AI, which assists humans with recommendations, agentic AI acts autonomously—deciding, executing, and learning in real time. It can instantly resolve claims, adjust pricing, or trigger cross-sell offers without human intervention, based on business-defined thresholds.

But with autonomy comes accountability. Agentic AI doesn’t just recommend—it acts. Every automated decision must be backed by data that is accurate, explainable, and complete.

If a claims AI agent misclassifies a loss or a pricing model pulls from stale data, the business bears the consequences.

Why “good enough” data isn’t good enough anymore

Insurers have spent decades working around data imperfections—manual corrections, static reports, and “good enough” inputs. That tolerance no longer works.

Agentic AI operates in real time, drawing on data that must be unified, current, and context-rich. When the view of a policyholder, product, or claim is fragmented across multiple systems, the results are inevitable: mispriced risk, delayed settlements, and compliance gaps.

Even minor data quality issues, such as duplicate customer profiles, outdated contact details, or mismatched household relationships, can cascade through automated workflows. A single household represented as three separate “customers” might receive inconsistent renewal offers or duplicate communications. Worse, AI-driven upsell or cross-sell models could completely miss opportunities because related policies or household members aren’t properly linked.

If you look at the AI Quarterly Pulse Survey from KPMG (Q3 2025), the message is clear: 82% of CEOs cited data quality as the top barrier to achieving AI success. In other words, the old adage still applies—garbage in, garbage out. Poor-quality data doesn’t just slow innovation; it can stop transformation in its tracks.

Data readiness: the new business imperative

Data readiness is no longer just an IT project—it’s a leadership responsibility. As AI-driven systems take on more business-critical decisions, executives must have confidence that the data behind them is governed, transparent, and trusted.

To make AI truly scalable and compliant, insurers need data that is:

• Unified, not just centralized – Connects policyholder, product, and claims data into one dynamic source of truth—free of duplicates and inconsistencies.
• Context-rich – Captures relationships and interactions (policyholder–agent–household–claim) so AI sees the full picture before acting.
• Governed and compliant – Embeds lineage, auditability, and access controls to meet regulatory standards.
• Always current – Continuously refreshes data so every decision—human or AI—is based on real-time truth..

The risk: when fragmented data drives automated decisions

The risks of poor data quality are real—and expensive:

• Mispriced policies. Outdated or incomplete risk data leads to inaccurate pricing and lost profitability.
  Wrongful claim denials – Missing or conflicting data can lead to rework, disputes, and reputational damage.
• Fraud exposure – Agents or models can’t detect anomalies across disconnected systems or entities.
• Missed upsell and cross-sell opportunities – Disconnected household or product data prevents AI from identifying related policies or new coverage needs.

Agentic AI magnifies these risks. Without clean, complete, and explainable data, automation simply scales bad decisions faster—and erodes trust in both the system and the brand.

From AI pilots to scalable success: the foundation for trusted automation

Insurers don’t lack AI ambition—they lack AI-ready data. Scaling from pilot projects to production AI requires a living, trusted data foundation that unifies every domain and continuously curates data quality, context, and compliance.

That foundation is Reltio Data Cloud™—the agentic data fabric for insurance.

Reltio Data Cloud unifies policyholder, policy, and claims data across systems to create trusted, context-rich, real-time profiles that fuel decision-making and automation. Built on a cloud-native SaaS architecture, it continuously ingests, unifies, validates, and mobilizes data in milliseconds. It assures AI agents and operational systems act on a single, reliable source of truth.

With Reltio, insurers:

• Achieve 360° policyholder views – Combine customer, policy, claims, and household data to understand relationships, risk exposure, and growth potential.
• Eliminate duplicate records automatically – Purpose-built AI agents continuously detect and merge duplicates, improving accuracy and customer experience.
• Automate governance and compliance – Built-in lineage, audit trails, and access controls assure transparency and regulatory readiness.
• Accelerate AI initiatives – Use the Reltio MCP Server to securely connect AI agents and models to real-time, governed data for faster deployment and higher trust. And use Reltio AgentFlow’s purpose-built agents to automate data and business operations.
• Increase customer lifetime value – Power accurate cross-sell and upsell recommendations through complete household and product relationships.
• Reduce cost and complexity – Replace legacy MDM and data silos with a flexible, cloud-native platform for lower TCO and faster time to value.

Leading insurers are already realizing results after unifying their data with Reltio:

• Mercury Insurance increased the accuracy of the multi-policy discount from 95% to 99%. The result: fewer pricing errors, lower call-handling costs, and faster underwriting.
• Empire Life boosted customer service productivity—achieving a 60% increase in first-call resolution.
• A leading UK insurer reduced customer policy inquiries by 40%.
• A global insurance and risk management services firm cut RFI turnaround time by 99%, from 4 weeks to 1 hour, generating an estimated $17M in annual savings.

A call to action for insurance leaders

AI is here—but without a trusted data foundation, it will fail to deliver on its promise.

For insurance executives, this isn’t a technical challenge—it’s a business transformation mandate. Unified, governed, real-time data isn’t a back-office concern; it’s the cornerstone of modern, compliant, scalable automation.

AI can’t fix bad data. But with the right data foundation, it can transform everything else.

Ready to go deeper?

Read our eBook Unlocking Agentic AI’s Advantage: A Business Playbook for Data Readiness to see how trusted, connected data turns AI ambition into measurable business impact. Download the eBook.