Rules-based review and AI-assisted drafting are already described in current estimating products: Cotality documents configurable validation rules, while Verisk describes generated roofing estimates that users review and edit. Neither description establishes the quality of your complete claim file. (cotality.com)
The table below is a proposed task-allocation comparison.
| Approach | Candidate tasks to test | Required buyer check | Do not infer |
|---|---|---|---|
| Human-led production | Scope interpretation, unusual assemblies, ambiguous evidence and exceptions | Named competence, documented reasoning and reviewer calibration | Human preparation is automatically error-free |
| Deterministic checks | Required fields, arithmetic, duplicate items and explicit rule conditions | Rule ownership, test cases, overrides and version control | A passed rule set establishes evidence completeness |
| Generative assistance | Draft narratives, document summaries and evidence organisation | Source references, unsupported-statement testing and abstention behaviour | Fluent language means supported content |
| Combined workflow | Drafting assistance, rules checks and accountable human escalation | Responsibility at every hand-off and measured retained effort | Combining methods automatically produces a superior result |
NIST identifies confidently generated false content—including false supporting logic or citations—as a generative-AI risk. Consequently, our recommended test is not whether a narrative sounds plausible, but whether every material statement can be checked against the supplied record. (nvlpubs.nist.gov)
Reject the wrong denominator. Document-field accuracy, line-item accuracy, file-level first-pass acceptance and escaped material-error rates answer different questions. Require separate measures rather than allowing one to stand in for the others.
Sources cited on this page
- cotality.comcotality.com
- nvlpubs.nist.govnvlpubs.nist.gov