Most claims teams do not believe coverage determination is broken. Claims are moving. Payments are made. Escalations are managed. Complaints are handled. From the outside, the process appears to work.
But “working” is not the same as resilient. A coverage process can produce acceptable outcomes while still relying on too much manual effort, undocumented expertise, and handler interpretation. That is the coverage determination confidence gap.
What the data says
The State of Policy Coverage Checking Report found that 72% of respondents describe their coverage checking approach as fairly or very comprehensive. On its own, that sounds positive.
The underlying operating model tells a different story. Half of respondents say coverage checking is completely manual, and half say it is only partially automated. No respondents report mostly or fully automated coverage determination.
Why “fairly comprehensive” is not enough
“Fairly comprehensive” often means the job gets done eventually. It does not necessarily mean the process is consistent, auditable, efficient, or resilient.
Adjusters may know where to look, which exclusions matter, when to escalate, and which policy version to trust. But if that logic lives in people’s heads rather than a repeatable intelligence layer, it is hard to scale and hard to evidence.
The customer experience risk
Process fragility becomes customer experience risk when coverage clarity is slow or inconsistent. Customers may receive different answers depending on who reviews the file, wait longer for decisions, or be asked for documents that should have been identified at FNOL. Even when the final outcome is right, the experience can feel uncertain and opaque.
The friction claims leaders should measure
Coverage fragility rarely appears as one clean metric. It shows up as repeated referrals, rework, delays in coverage confirmation, inconsistent handler decisions, unclear escalation thresholds, and claims that cannot progress to STP because coverage certainty is missing.
The report supports this: 61% cite manual, time-consuming processes as a top challenge, 55% cite scope and variability in policy wording, 39% cite incomplete information, and 35% cite limited integration between policy and claims systems.
Closing the gap
The first step is to identify where coverage uncertainty slows the operation today. Which claim types are delayed? Which policies create ambiguity? Which referrals repeat? Which decisions are revisited?
The second step is to apply policy-aware AI where coverage logic and claim evidence first meet. That allows claims teams to surface the relevant clause, understand the evidence, identify missing information, and route the claim with confidence.
FAQ
It is the gap between claims leaders’ confidence that coverage checking is working and the operational reality that the process still depends heavily on manual work and individual expertise.
It shapes triage, reserving, customer communication and experience, disputes, STP eligibility, and indemnity accuracy from the start of the claim lifecycle.
AI can surface coverage-relevant evidence earlier, reduce repeated information requests, support faster explanations, and help adjusters communicate decisions more consistently.
How Sprout.ai helps
Sprout.ai closes the coverage determination confidence gap by making policy logic repeatable, visible, and auditable. It surfaces the relevant clause, supporting evidence, and recommended action consistently across every claim.
Where coverage is clear, Sprout.ai supports straight-through processing. Where it is ambiguous, it presents an explainable, policy-grounded view for human review, with the decision rationale documented from the moment the claim arrives.