As catastrophe losses grow more frequent and complex, insurance carriers face an unprecedented capacity challenge: experienced claims talent is leaving the industry faster than it can be replaced. While traditional strategies rely on aggressive hiring or expanding contractor pools, carriers can no longer recruit their way out of a shrinking knowledge base.

AI for insurance claims offers a far more sustainable path forward giving seasoned adjusters the tools to automate routine document processing, surface critical file discrepancies, and focus their time where it matters most: on expert judgment and defensible decision-making.

In this article:

• What the claims capacity crunch is and why it's accelerating
• Why losing adjuster expertise is bigger than a staffing problem
• Limitations of traditional approaches to claims capacity
• How AI extends adjuster expertise across complex claim files
• Why human-in-the-loop AI matters for high-stakes claims decisions

For years, capacity conversations in insurance centered almost entirely on capital. But as catastrophe activity grows more frequent and complex, a different constraint is coming into focus: expertise.

Claims capacity is a carrier's ability to handle claim volume with qualified people, especially during the surges that follow catastrophe events. That capacity is shrinking rapidly.

According to Insurance Business, Andrew McCallum, Vice President, Specialty Operations at Sedgwick, estimates that as much as 40% of the catastrophe claims resources available in 2024 may no longer be deployed by 2027.

The broader labor market tells a similar story. Claims employment was down 20.9% year-over-year in July, according to The Jacobson Group's September Labor Market PULSE, which draws on Bureau of Labor Statistics data.

McCallum put the stakes plainly: "The diminishment of not only the resource but also the knowledge base is going to be significant."

At the same time, events like wildfires and hurricanes increasingly compete for the same pool of adjusters. Fewer experts are handling more complex losses, often at the same time.

The challenge isn't only having fewer people to process claims. It's the loss of professionals with the judgment and pattern recognition that only come from years of handling complex losses.

Claims professionals don't just process information they interpret it. Experienced adjusters:

• Recognize patterns across similar losses and claim histories
• Understand the broader context and nuance behind a claim
• Spot discrepancies and red flags, often with incomplete information in front of them

That kind of expertise is difficult to build and hard to replace. When an experienced adjuster leaves an organization, that institutional knowledge rarely leaves behind documentation.

Carriers facing capacity shortfalls typically respond by trying to add headcount or scale contract teams. But traditional playbooks are reaching their limits:

1. Experience Takes Years to Build: New hires can quickly learn systems, rules, and operational workflows. But the judgment required for complex large losses only comes from handling them—taking time carriers don't have.
2. Catastrophe Events Compete for the Same Adjusters: When wildfires, storms, and floods overlap, carriers and independent adjusting firms draw from the exact same limited talent pool. Surge capacity becomes scarcest exactly when it's needed most.
3. Experts Spend Too Much Time on Low-Value Work: Experienced adjusters often spend much of their day searching for information, reviewing document packs, navigating systems, and managing administrative tasks. That is valuable time taken away from the high-stakes decisions only they can make.

The Bottom Line: You can't hire your way out of an expertise gap. The alternative is increasing what each expert can accomplish. AI for claims processing helps adjusters spend less time searching and sorting and more time on evaluation and judgment.

Conversations about AI in insurance usually center on simple automation: what can be made faster, and how much routine work can be taken off someone's plate.

While speed matters, a better question is: How can AI help an experienced professional do more?

Here are four ways AI for insurance claims extends adjuster expertise across complex claim files:

Large loss files can run to hundreds of pages across medical reports, estimates, photos, and correspondence. Finding the right detail takes time adjusters don't have.

OwlVision reads complex claim files, sorts hundreds of pages into labeled documents, and produces cited summaries and claim timelines. AI document automation gives adjusters an organized, structured file from day one.

Conflicting accounts, missing information, and timeline inconsistencies are easy to miss when files are long and workloads are high.

Powered by OwlVision's file-wide document intelligence, OwlSignal automatically flags conflicting accounts, missing records, and gaps that deserve a closer review. This ensures consistent scrutiny across files regardless of adjuster workload or experience level.

Conversational research with OwlAssist lets adjusters ask direct questions about a file in plain language. Instead of reading through every PDF to find a date, statement, or injury note, they receive fast, factual answers backed by exact source citations.

Owl drafts narrative reports, such as large loss reports, for the adjuster to review and finalize.

In a recent pilot across five lines of business, Owl generated roughly 70% to 80% of the content needed for large loss reports. Adjusters were left to focus on review, validation, and judgment rather than writing from scratch.

In high-stakes claims decisions, speed alone doesn't create better outcomes. AI brings scale, speed, and the ability to organize vast amounts of information. Humans bring context, empathy, accountability, and judgment.

With explainable AI for insurance, every insight links directly back to its source page. Adjusters can verify any finding in a click, keeping claims decisions transparent, compliant, and defensible.

Technology structures information and presents facts. Humans make the final determinations. Owl handles the searching, sorting, and first drafts enabling adjusters to review every insight, trace sources, and make the final call with confidence.

As experienced professionals retire or get stretched thin by rising demand, domain-specific AI helps capture and extend the value of their expertise, rather than simply attempting to compensate for its absence.

Insurance leaders need to be proactive about claims capacity. Experienced adjusters are leaving, catastrophe activity is rising, and hiring alone cannot close the gap.

AI for insurance claims offers a clear path to extend the reach of the experts carriers still have. But the industry doesn't just need more technology it needs technology designed specifically around the people making decisions.

The Claims Intelligence framework delivers on three core AI principles:

• Accountable AI: Every insight traces back to its source page with audit trails, so adjusters can verify and defend each decision.
• Effective AI: Faster file review and automated report drafting enable experts to oversee more complex losses effectively.
• Ethical AI: Outcomes are grounded in documented facts, with human professionals making every final determination.

The question isn't whether AI can replace the expert. It's how much more your best experts can accomplish with the right technology behind them.

See what Owl looks like on your own claims. Book a private briefing to see how the Claims Intelligence toolkit extends the reach of your experienced adjusters