Claims departments are turning to artificial intelligence to assemble files faster, but insurers should keep the final payout decision in human hands. Experts warn that deploying AI for evidence gathering lowers risk while cutting cycle times, whereas handing adjudication to algorithms introduces compliance and reputation exposure.
Start with the file, not the verdict
Adjusters spend hours pulling data from multiple sources before evaluating a loss. AI tools can read documents, summarize notes, compare damage photos, and pull policy language. Forestview Insights CEO Rob Galbraith said the current workflow often involves gathering information from eight or twelve different places and consolidating it into one place.
The next step requires human judgment. An adjuster decides how to handle the account, what steps come next, and whether to recommend payment or investigation. Letting an algorithm deny a claim removes necessary oversight.
Why the human loop matters
The National Association of Insurance Commissioners already tracks AI use for estimating repair costs, but it warns staff to review generated information carefully. Aviva recently reported a spike in motor claims supported by AI-generated images and manipulated documents. The carrier now relies on advanced analytics paired with human oversight to flag suspicious activity early.
That balance prevents technology from becoming a liability. A flawed summary is easy to correct. A wrongly denied claim triggers compliance reviews, damages customer trust, and invites regulatory scrutiny.
Insurers must build deployment models around traceability. Leaders need to know exactly which sources the system used and what it flagged before an adjuster acts. "We're kind of having the AI assist the user, but we're still having that human in the loop to kind of make decisions before we, for instance, deny a claim," Galbraith said.
The infrastructure claims leaders should build first
Carriers should invest in document ingestion, image analysis, workflow integration, source tagging, and explainable summaries. Those capabilities reduce an adjuster's search burden without removing their authority. This approach fits cleanly into broader efforts to modernize AI for Operations, since it focuses on measurable workflow improvements rather than vague automation promises.
Executives can track file completeness, investigation prioritization, and cycle-time reduction. The goal is straightforward. Gather the facts, organize the record, surface what needs attention, and leave accountability with the insurer's people.
Why this matters for insurance professionals
Claims teams should deploy AI as an evidence workbench, not an autonomous adjudicator. Staff members can use automated tools to compile policy terms, loss history, repair estimates, and weather data before reviewing a file. The adjuster retains full authority to evaluate context, apply training, and determine the next steps.
This structure protects against compliance failures while delivering faster turnaround times. Teams that treat AI as a research assistant rather than a replacement will see stronger audit trails and fewer disputed payouts.
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