Insurance companies expect artificial intelligence to transform their industry, but fragmented data and slow operational processes are preventing many from reaching production.
An Autorek survey of 250 managers in the United Kingdom and United States found that 82% expect AI to dominate insurance, while only 14% report fully integrated deployment. The gap is not primarily a shortage of model ambition. It is the condition of the systems and information beneath it.
Manual error correction consumes 14% of operational budgets among surveyed firms, almost half run settlement cycles longer than 60 days and transaction volumes are projected to rise roughly 29% over two years.
Fragmentation turns automation into overhead
The typical surveyed insurer manages an average of 17 data sources. Mergers and acquisitions often add more systems without harmonising the records inside them. Reconciliation teams then spend time resolving differences among formats, definitions and transaction histories.
Twenty-two percent of respondents identify reconciliation complexity as a significant driver of cost, while a similar share connects inefficiency with governance and audit risk. These are not separate problems. When information is fragmented, evidence is harder to trace, controls become inconsistent and employees must repair errors manually.
Placing AI on top of that architecture can magnify the problem. An automated system may process data faster without knowing that two sources define the same field differently. Costs rise as exceptions multiply and teams build more integrations around an unstable foundation.
Limited internal expertise and legacy-system integration add to the barrier. Daily operations cannot simply stop while an insurer rebuilds its data estate, so transformation competes with the workload it is meant to reduce.
Reconciliation offers a bounded starting point
The report suggests reconciliation as an early proving ground because it is structured, rules-based and connected to measurable costs. Teams know what a matched transaction should look like and can compare automated results with established controls.
AI may help structure information from fragmented sources and handle complexity that deterministic robotic process automation cannot address economically. Cloud platforms could provide more flexible integration than purely in-house systems, although deployment choices still need to reflect data governance and security.
A bounded workflow makes value visible. Leaders can measure error rates, processing time, manual intervention and settlement cycles instead of relying on broad productivity claims. Human reviewers can examine exceptions while the system proves its reliability.
Success in reconciliation can also improve the data layer for later uses. Standard definitions, traceable records and consistent controls create assets that underwriting, claims and reporting applications can reuse.
Data reform is the AI programme
The report’s central conclusion is structural: standardisation and governance must precede automation at scale. That work may appear less innovative than deploying a model, but it determines whether the model can produce dependable outcomes.
Firms that repair the foundation can widen the performance gap as transaction volumes rise. Lower reconciliation cost is one outcome; faster settlement, stronger auditability and greater operating capacity may matter just as much.
The survey also shows how awareness can coexist with slow action. Insurance leaders have recognised fragmentation and manual processing for years, yet operational pressure makes long-term repair difficult. AI raises the urgency because competitors with cleaner data can adopt useful automation faster.
There is no guarantee that integrating data will unlock every promised benefit. It does, however, remove a known source of delay, error and governance risk. Even without advanced AI, that improvement has economic value.
Insurance therefore does not need to wait for a more capable model. It needs to decide which data is authoritative, connect systems deliberately and give each process clear ownership.
The firms most likely to benefit from AI will not be those with the largest collection of pilots. They will be the ones that treat data quality, reconciliation and governance as the first phase of automation—not as cleanup work to be scheduled after the exciting technology has arrived.
