In most claims operations, policy coverage checking is not considered broken, so is not generally considered a priority for transformation.
Claims are paid. Escalations are handled. Complaints are managed. On the surface, everything appears under control.
But new independent research in The State of Policy Coverage Checking report, combined with insights from the webinar Insurance Claims Policy Checking 2025: Bottlenecks, Benchmarks & Breakthroughs, reveal a more complex reality.
While 72% of claims leaders describe their coverage approach as “fairly” or “very” comprehensive, the underlying operating model remains heavily manual:
- 50% rely entirely on manual coverage checks
- 44% report zero automation in coverage determination
- 31% say coverage delays occur frequently or very frequently
Coverage checking isn’t failing loudly. It’s succeeding expensively.
The hidden impact is friction, not failure.
Coverage breakdowns rarely appear as dramatic operational incidents that have immediate and visible impact.
Instead, they manifest as persistent and ongoing operational friction:
- Slower cycle times
- Handler rework and escalations
- Inconsistent decisions across similar claims
- Delayed indemnity accuracy
- Claims leakage through reinterpretation and delay
These inefficiencies become absorbed into daily operations, creating a system that works – but only through increasing effort and headcount, and the skill and expertise of experienced adjusters.
This friction is most visible at First Notice of Loss (FNOL).
Straightforward claims may confirm coverage quickly, but complex or multi-policy claims can take days, weeks, or even months to validate. This delay stalls downstream automation and prevents insurers from achieving true zero-touch or low-touch claims handling.
When major events occur, such as storms, floods, and catastrophe losses, the problem multiplies.
Suddenly, as a consequence of unpredictable surge events, the system must absorb thousands of additional claims without additional staff. Not only does this put adjusters under undue pressure, and can jeopardize the speed and quality of decision-making – but it also prevents them from providing customers with the best possible care at their time of greatest need.
The conclusion is obvious: manual coverage checking simply doesn’t scale.
Why scale is now the strategic issue
During the AI in Insurance: Myths, Realities & the ExCo Gap webinar discussion, Sprout.ai CEO Roi Amir highlighted a major shift in how insurers are thinking about AI in claims.
The goal is not simply automation. It is operational scale.
AI can automate intake, data ingestion, triage, and early decision-making across large volumes of claims, while still allowing adjusters to intervene when ambiguity or complexity arises.
In one example shared during the webinar, a Sprout.ai insurance carrier customer automated 70% of high-volume health reimbursement claims, improving customer satisfaction by 19% and reducing turnaround time by 23%.
In another deployment, AI-supported coverage analysis reduced decision time by 45%, while improving consistency and accuracy.
The impact is not simply efficiency. It is the ability to handle significantly greater claim volumes without increasing headcount.
This is particularly critical during surge events, where insurers must rapidly scale operations to handle catastrophe claims.
Traditional manual models of claims processing require hiring temporary staff or redeploying teams across lines of business in response to dramatic increases in claims volumes.
However, AI-driven coverage intelligence enables a different model — one where the system absorbs the surge while adjusters focus on the most complex claims.
What leading insurers are doing differently
A clear divide is emerging between insurers who treat coverage checking as an operational task, and those who treat it as strategic infrastructure.
Leading insurers and MGAs are:
- Embedding AI-driven coverage intelligence at FNOL
- Designing systems for complex, layered, multi-policy claims
- Ensuring clause-level explainability and governance
- Creating feedback loops between claims and underwriting
- Building platforms that scale with demand
As Ian Thompson observed during the webinar, the most strategic insurers recognize that claims AI is not simply about reducing expense ratios. It is about improving claim outcomes and loss performance – which ultimately combine to drive profitability.
The real competitive advantage
Early adopters of AI in insurance benefit from structural advantages:
- Faster claims decisions
- Reduced leakage
- Improved indemnity accuracy
- Higher customer satisfaction
- Greater operational resilience during surge events
Most importantly, they achieve cost-effective scale.
The strategic question is no longer whether AI has a role in coverage checking. It is whether manual approaches can keep pace with policy complexity, claim volumes, and the increasing need for surge agility.