Customers are losing patience with the claims process and understandably so. When a home is damaged, a vehicle is out of service or a business is interrupted, policyholders want answers. They want to know what is covered, what happens next and when they can expect a resolution.
Insurers are responding with investments in artificial intelligence designed to accelerate claims handling, reduce administrative work and improve efficiency. AI can review documentation, identify missing information, summarize complex files and help claims organizations respond faster.
But there is a risk in making speed the primary measure of success.
A claim denied in minutes rather than days is not necessarily a better outcome. Neither is a settlement that moves quickly but overlooks a coverage issue, underestimates damages or misses circumstances an experienced adjuster would recognize.
The real opportunity is not simply to automate more claims activities. It is to use AI to make better decisions sooner, while keeping professional judgment central to the process.
Speed and Quality Are Not Competing Objectives
Having spent much of my career leading claims organizations, I've seen how operational pressures influence the decisions adjusters make. Claims professionals manage large caseloads, navigate complex policy language and review extensive documentation.
Many delays aren't caused by difficult decisions themselves. They result from the time required to assemble the information needed to make those decisions.
Consider a complex commercial property claim involving disputed damages and extensive documentation. An adjuster may spend hours reviewing reports, correspondence, estimates and policy provisions before identifying the issues requiring attention.
AI can reduce that burden by organizing information, highlighting inconsistencies and surfacing potential coverage issues. This allows claims professionals to reach the point of informed decision-making sooner.
But getting to a decision faster is different from making the decision automatically.
Experienced adjusters still need to determine whether the evidence is reliable, whether the facts support the conclusion and whether additional investigation is warranted. And they must also balance strategy when the answer isn't black and white.
The greatest value of AI may not be the time it saves processing claims, but the time it gives professionals to exercise their expertise.
Human Oversight Must Mean More Than Approval
As AI adoption accelerates, "human in the loop" has become a familiar phrase. But what does meaningful oversight actually look like?
If an adjuster receives an AI-generated recommendation and clicks approve without examining the supporting evidence, has human judgment really been applied?
I would argue it hasn't.
A human signature on an automated decision provides little protection if the reviewer hasn't exercised independent judgment.
Effective oversight means claims professionals can understand what the AI evaluated, examine supporting evidence, identify uncertainty and challenge recommendations. They must retain the authority to request additional information or override the system when circumstances warrant it.
Not every activity requires the same level of intervention. Verifying policy limits or identifying missing documentation differs significantly from interpreting complex coverage provisions or evaluating disputed liability.
Claims organizations need clear boundaries defining what can be automated, what requires professional review and which decisions must remain with qualified claims professionals.
Responsible AI isn't achieved by adding an approval step to an automated workflow. It requires designing workflows where human judgment is meaningful and accountable.
From Reactive Reviews to Proactive Intervention
One of AI's most promising applications is improving oversight while claims are still active.
Traditional quality assurance often relies on retrospective reviews of a small sample of files. These reviews can identify errors, but frequently occur after opportunities to improve outcomes have passed.
A missed coverage issue, inadequate reserve or delayed investigation may not become apparent until months later, when the consequences are greater.
AI creates the opportunity to continuously review claims activity and flag emerging risks before they escalate.
Consider a liability claim where new documentation raises questions about the initial assessment of responsibility. Rather than waiting for a periodic file review, AI can flag the discrepancy for an adjuster to investigate.
The technology doesn't need to make the liability determination to deliver value. Identifying the issue early enough for an experienced professional to act may be the more important contribution.
This is where AI can fundamentally improve claims quality: making critical information available when it can still influence the outcome.
Responsible AI Requires Operational Accountability
Adding AI to an existing workflow doesn't automatically improve that workflow. An automated process can repeat mistakes consistently and at scale.
Claims leaders must establish clear decision-making responsibilities, ensure recommendations are supported by evidence and maintain auditable records of consequential decisions.
They should also measure success beyond processing speed, examining accuracy, rework, reopened claims, complaints, customer experience and financial outcomes.
Equally important is investing in claims professionals themselves.
AI can eliminate administrative work that prevents experienced adjusters from focusing on complex coverage questions, difficult negotiations and customer needs. But adjusters must understand AI's limitations, supervisors must know when to intervene, and less experienced professionals must continue developing sound judgment.
The goal isn't to remove people from claims decisions. It's to make their expertise more effective.
The Real Measure of Progress
Insurers face enormous pressure to move faster, and AI offers capabilities that can transform claims operations. But faster decisions that produce more disputes, inconsistent settlements or avoidable errors aren't progress.
The insurers that succeed won't necessarily be those automating the greatest percentage of claims activities. They'll be those that understand where automation improves outcomes, where human judgment remains essential and how the two work together.
Customers deserve faster answers. They also deserve decisions that are fair, accurate and supported by a thorough understanding of their circumstances.
The opportunity for the insurance industry is to deliver both.
