The AI gap insurers call a data problem is a context problem

Insurers keep telling researchers that data is the reason their AI programs have not scaled. A new industry study measures that belief against the reality. Most insurance executives rate their company ahead of the pack on AI, and the same survey finds the industry clustered tighter than any other sector it covers, with a small share pulling ahead and most everyone else stuck in the same crowded middle. Asked what is blocking progress, insurers pointed at data more than executives in banking, retail, utilities, life sciences, or healthcare payer named it in their own industries. That answer is half right. Data is the label a program reaches for when nobody can say where a decision's evidence lives. The obstacle was never the data. It is that the data sits in four or five systems that were never built to answer one underwriting question together.

What the study found

The third annual EXL Enterprise AI Study surveyed hundreds of C-suite and senior decision-makers across banking, insurance, retail, utilities, life sciences, and healthcare payer. Scaling AI is a higher priority for insurers this year than last, and agentic AI is moving fastest in exactly the places it should: risk management, actuarial work, underwriting, and customer experience, coordinating across systems and roles instead of sitting inside one app. That is real movement, and it is worth naming clearly before the rest of this piece argues that the same movement stalls the moment a single decision needs more than one system to answer it.

The insurance-specific finding is the sharper one. Most insurers believe they are ahead of their competitors. The study's own measurement puts that belief next to the reality: adoption is fairly even across the industry, insurance carries the largest share of any surveyed sector sitting in the middle tier of adopters, and only a small slice qualifies as a genuine leader. Coverage of the insurance findings lands on the same split the study names directly: the gap between perceived and real progress shows up in how insurers operate, and when insurers are asked to name the single biggest obstacle, they point at their data, and cite silos as the top barrier more than executives in any other surveyed industry. Nobody surveyed named the model. That is worth sitting with, in a year when every vendor pitch still opens with the model.

Why data reads as the obstacle

An underwriting decision is never answerable from one system. Take a mid-market commercial property renewal. The policy history and prior endorsements live in the policy administration platform. The loss history lives in the claims system, usually with its own vendor and its own login. The catastrophe model and pricing indications run on a separate actuarial pipeline with its own refresh schedule. The producer's read on the account, the one thing closest to what happened on the ground this year, sits in a call transcript that nobody indexed. An underwriter piecing that renewal together today opens four browser tabs and trusts memory to connect them. Each system does its job well. None of them was built to answer a question that spans all four at once.

When a team names this "a data problem," the instinct that follows is more data work: a bigger warehouse, a new governance initiative, a dashboard that finally puts the numbers on one screen. Those projects finish, sometimes on schedule, and the same question reappears on the next renewal because the systems still do not talk to each other. Only the copy of the data changed address. A warehouse is a snapshot. The moment the source system updates, the snapshot falls behind, and keeping ten source systems synced into one duplicate is its own permanent project, running in parallel with the underwriting work it was supposed to unblock. The underwriter still opens four tabs. The warehouse became a fifth.

Compare that to where insurance AI has already scaled. Customer-facing agents, quoting tools, and first-notice-of-loss intake work because the decision they support usually needs one thing: the conversation happening right now. That is a shallow context requirement, and a point solution can hold shallow context entirely inside itself with no need to reach anywhere else. Underwriting, actuarial modeling, and portfolio risk decisions need history, and history is exactly what got split across systems that were never connected. A context graph is not a better retrieval pipeline built to fetch documents on request; it is a structure that reads across every system continuously, so the connection exists before the question is asked instead of being assembled from scratch each time one comes in.

Where more data does not fix it

The honest objection here is that a big enough consolidation project eventually solves this, and some carriers have been running one for a decade with real progress to show. That instinct is not wrong. Centralizing data has real value, and no one should read this as an argument against data quality work.

What consolidation cannot do is stay current for free. Every source system keeps producing new claims, new calls, new actuarial runs, and a warehouse only reflects what it last copied. Keeping that copy fresh across ten systems, with its own pipeline, its own failure modes, and its own policyholder records sitting in a second location that now needs its own security review, is why data keeps coming back as the top obstacle year over year in the same study. Reading across the systems where the data already lives, live, without duplicating it and without asking a security team to sign off on a new warehouse holding the same records a second time, sidesteps the whole cycle. The model stopped being the bottleneck once every vendor had access to a comparable one; the context layer became the moat instead, and that is as true for a book of commercial property risk as it is for any other decision an enterprise makes from data it already owns.

The split the study measures, leaders against a crowded middle, tracks this distinction more than it tracks budget or headcount. Insurance carries the industry's highest concentration of followers because most underwriting shops solved the shallow half of the problem and stopped. The carriers pulling ahead did not hire more data scientists. Their underwriting, claims, and actuarial systems already answer to something reading across all three, so the renewal that used to take four open tabs takes one screen with the sources named next to each line.

What an inspectable answer looks like

An intelligence layer that reads across a policy admin system, a claims system, and an actuarial pipeline earns trust in a line of business this sensitive only if two things hold. First, it cannot take the data with it. Nodes runs single-tenant, VPC-resident, with no data egress, ever: the reasoning happens inside the carrier's own environment, and the underlying systems keep the records they already hold. Second, nothing acts on its own. A human approves, edits, or rejects every cross-system workflow before it touches anything, the same discipline this site has argued belongs on the approval gate in front of a bind decision.

That combination is what turns "we have a data problem" into a question a security review can close on the spot: what did the system read, what did it recommend, and who signed off before it acted. Insurers who have run that question through their own review process already know the pattern; it is the same evidence trail a governance review asks for on any AI-assisted decision, applied here to the specific claim that data, not process, is what is holding underwriting back. The trail exists because the system that read across four platforms wrote down what it read, before anyone asked.

Insurance is not short on data. Four or five systems inside a single carrier already hold more of it than any underwriting team could read in a career, going back years before this study was ever fielded. What the underwriter working that commercial property renewal actually needs is not a fifth system holding a copy of the other four. It is one screen that already read all four, with the sources named next to each line, before the renewal ever landed on a desk.

Sources

Saad Bin Shafiq is the founder of Nodes, serving data-sensitive enterprises.