The question for insurance executives is not how quickly to adopt AI. It is whether the organization has the foundation to turn AI into measurable business value.
Since 2012, Agile Insurance Analytics has consistently called for insurers to establish the foundational data management building blocks — governance, quality, integration, and accountability — required to support advanced analytics and predictive modeling. Without these essentials in place, investments in AI and predictive tools rarely deliver sustainable business value.
A 2026 study from Precisely and Drexel University’s LeBow College of Business surveyed more than 500 senior data and analytics leaders. The research found a significant gap between perceived AI readiness and the operational conditions needed to scale AI: data readiness, governance, skills, infrastructure, and measurable business outcomes.[1]
The Denodo AI Trust Gap Report adds a complementary operational perspective. Based on a global survey of 850 executives and business decision-makers responsible for AI initiatives, it identifies three conditions that increasingly determine whether agentic AI can be trusted: live data, the right data, and guardrails.[2]
AI does not replace the need for good management. It makes the need for trusted information even more important.
Denodo’s 2026 research makes the data foundation issue concrete. Agentic AI moves beyond answering questions: agents can perceive conditions, decide what to do, and execute actions against operational systems. That raises the standard for the data and controls underneath them.[2]
Say AI data must be real-time or no more than one minute old to be trustworthy.
Struggle to identify trustworthy data or prepare and integrate the data AI needs.
Struggle with AI data security and access controls; 31% describe the challenge as serious.
AI initiatives draw on an average of more than 400 data sources, with nearly one in five organizations using more than 1,000.
Even organizations using data catalogs and lakehouses report difficulty optimizing performance for AI workloads.
Insurance decisions depend on information every day — from underwriting and claims to pricing, financial management, customer relationships, and regulatory obligations. When leaders receive different answers to the same question, confidence falls and time is lost reconciling the numbers.
Executives can act with greater confidence when the organization agrees on the information that matters.
Clear ownership and consistent information reduce the recurring effort spent finding, checking, and correcting problems.
AI initiatives have a stronger chance of producing meaningful results when they are connected to reliable information and real business priorities.
The most useful executive conversation is not about algorithms or technology. It is about whether the organization can make better decisions, improve operations, reduce risk, and create measurable value.
Data decisions should have visible business ownership and executive accountability.
Fix recurring information problems before they become larger business problems.
Define the result the organization expects before selecting an AI initiative.
AI should fit within the organization's broader approach to accountability and risk.
Technology creates value only when the organization is prepared to use it effectively.
Modernization should strengthen the organization's ability to use trusted information to improve performance.
The Denodo research is especially relevant because it extends the traditional data-quality conversation into the operating environment of AI. Trust requires more than accurate historical data. Agents need access to current operational information, consistent business meaning, and controls that follow the agent wherever it can reach data or tools.[2]
Know where the current, authoritative information lives and how it can be accessed without creating unnecessary copies.
Establish consistent definitions for critical insurance concepts so systems, analytics, and AI interpret the business the same way.
Apply access controls, least-privilege permissions, auditability, and business rules wherever AI can act.
Design for AI workloads that may retrieve data repeatedly, invoke tools, and operate at enterprise scale.
Keep ownership close to the outcomes being improved. AI governance should support business accountability, not sit beside it.
Connect AI initiatives to operational, financial, customer, risk, or compliance measures that executives can evaluate.
At Agile Insurance Analytics, we believe sustainable AI value begins with alignment: business priorities, operating processes, accountability, trusted information, and measurable outcomes. The technology matters, but it is not the strategy. The strategy is improving how the business performs.
AI readiness is not achieved by adding AI to existing data and processes. The foundational building blocks — strategy, governance, data models, definitions, data quality, and data literacy — must be in place first.
For a broader perspective on why these fundamentals matter, read There Is No Shortcut to AI.
The statistics and research claims on this page are attributed to the original research organizations. AIA's insurance-specific implications and executive recommendations are interpretation based on those findings.
It is who builds the strongest foundation for turning AI into better decisions, better operations, and measurable business results.