This piece is by Errol Rodericks, Director of product marketing EMEA and LATAM at Denodo.
AI adoption across the UK insurance sector continues to accelerate, yet the impact is falling short of the industry’s expectations. The narrative is now shifting beyond systems that analyse data and generate recommendations towards agentic AI, that is capable of taking action, whether that means initiating claims processes, identifying potential fraud in real time, refining underwriting decisions or triggering next-best actions. Many see this as the next major leap forward for insurance, and they may be correct, but not for the reasons most people think.
While AI development certainly doesn’t lack pace, most of the insurance industry remains constrained by a more fundamental issue: AI cannot make trusted business decisions until it first understands the business. That understanding depends on trusted, relevant data being delivered in the right context and at the right time. Without it, insurers will struggle to give AI systems meaningful autonomy, however advanced the models become.
This is not because insurers lack data or technology. Over the past decade, the industry has invested heavily in data lakes and lake houses, advanced analytics and AI tools, and integration and data engineering pipelines. Yet despite these investments, many organisations continue to struggle to move AI beyond proof-of-concept projects and into day-to-day operations.
The challenge is not data availability. It is ensuring data is trusted, relevant and readily accessible when decisions need to be made.
Few industries experience this challenge quite like the insurance industry. Decision-making depends on information drawn from multiple policy, claims and customer systems, alongside external data sources such as telematics, weather and credit data. When that information is fragmented or delayed, even the most advanced AI systems are left working with an incomplete picture, making it difficult for AI to understand the wider business context behind every decision.
Agentic AI does not solve this problem. It exposes it.
Access Alone Is Not Enough
Many organisations respond by building a shared data foundation consisting of a unified layer where humans and AI agents can access the same information. While this is directionally right, it is incomplete. The challenge is not that organisations lack a shared data layer; it is that they struggle to deliver the right version of data for each decision, at the moment it matters.
Insurance operates on multiple, decision-specific views of data, each with distinct requirements:
· Claims decisions depend on real-time, enriched incident data.
· Underwriting relies on forward-looking risk models and external signals.
· Fraud detection requires cross-entity patterns and behavioural analysis.
· Customer servicing depends on a simplified, current policyholder context.
These are not variations of the same dataset; they are purpose-built representations of data, shaped by different latency, governance, and semantic needs, which becomes even more critical with agentic AI. Different agents operate at different points in the decision lifecycle, and require different data, in different forms, at different times.
A shared data layer can improve access to information, but access alone does not guarantee better decisions. Context transforms data into business understanding, and business understanding is what enables trusted decisions.
Turning Data Into Action
This is where many AI strategies stall. Most architectures are designed to store, process, and analyse data, but not to activate it at the point of decision. There is a fundamental gap between data being available and data being usable within real-time workflows.
Agentic AI operates directly in this gap. Without access to live, governed, and contextually aligned data, agents operate with partial understanding, and their outputs become unreliable. This is why many AI initiatives remain stuck in experimentation.
To move forward, insurers need to rethink how data is delivered. Not as raw datasets or reports but as data products. A reusable, governed, and outcome-aligned data asset designed to support a specific decision or workflow is what’s needed. Instead of exposing raw data, insurers should deliver contextualised, decision-ready views, with embedded governance and policy controls, consistent business semantics, and real-time access to internal and external sources.
For example:
· A claims data product unifying FNOL, policy data, repair estimates, and external signals.
· A fraud data product combining claims history, network relationships, and behavioural indicators.
· An underwriting data product integrating internal risk data with third-party enrichment.
These are not static datasets. They are decision-ready data assets, designed to deliver the right information, in the right context, for a specific business outcome.
Why Data Quality Matters Now More Than Ever
For agentic AI to deliver value, data must be live, governed at access, semantically consistent, and traceable. This is where a logical data layer becomes critical, not just as an integration approach, but as a way to connect distributed data in real time, apply governance dynamically, and deliver consistent, business-ready views across systems. This enables both humans and AI agents to act with confidence, without introducing further fragmentation.
The insurers that lead in 2026 will not be those with the most advanced models. They will be the ones that connect AI directly to business outcomes. That means starting with the outcome, such as reducing claims cycle time, improving fraud detection, increasing underwriting precision, or enhancing customer experience, and working backwards to define the decisions, data and systems required to support them.
This is how AI moves from experimentation to operational impact.
Where AI Initiatives Succeed or Fail
The next phase of AI in insurance will not be defined by advances in model performance. It will be determined by how well AI understands the business it is supporting. That requires trusted, relevant data, but it also needs the business context that allows AI to interpret that data correctly and make decisions insurers can trust.
Agentic AI accelerates this realisation. It makes clear that data must be trusted, contextual, available at the moment of decision, and aligned to outcomes. The goal is not simply to give AI more information. It is to help AI understand how the business works well enough to make confident, consistent decisions. Those who solve this will scale AI successfully, and those who do not will continue to pilot without transformation.
The future of insurance will not be defined by whether humans and AI agents share the same data. It will be defined by whether they have the right data, in the right form, to make the right decisions. That requires a shift from shared data to decision-ready data, from access to activation, and from experimentation to measurable outcomes. The real inflection point for AI in insurance will come when organisations enable AI to understand the business well enough to make decisions that customers, employees, and regulators can trust.
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