It is a Tuesday in June. Overnight, a hailstorm has crossed three provinces, and by eight in the morning the claims director of a property insurer is looking at a queue of notifications ten times the normal size. Car roofs, greenhouses, solar panels, conservatories. Policyholders want to know when an adjuster will come, repair companies want to know who pays, and the contact centre is already full. This is the moment when AI in claims surge handling either proves itself or turns out to be a slide in last year's strategy deck.
For a claims department, the storm passes in a night, but the extra work arrives within hours and keeps coming, while the team and its working hours stay the same size.
Surges are becoming the normal workload
For insurers, peaks in claims volume are now part of every year. Swiss Re Institute estimated total insured natural catastrophe losses in 2025 at 107 billion US dollars, the sixth year in a row above 100 billion. Severe convective storms, which include hail and thunderstorms, accounted for 50 billion US dollars of that total. In Europe, Swiss Re noted notable hailstorms in May and June, although insured losses there were limited [1].
For a claims department, the economics of a surge are simple and painful. The work arrives in days, while the team is sized for an average week. Temporary staff need training, adjusters cannot be in two places at once, and every day of delay increases costs, complaints and the risk of mistakes. The question is not whether the next surge comes, but how much of it your existing team can absorb.
What the Library says about surge AI
The MetroMinds Library holds 20 insurance use cases that deal directly with storms, catastrophes, surges and peaks. They cover a wide range of work: splitting claims capacity after a storm, rerouting claims across a strained queue, matching licensed adjusters to a catastrophe, resequencing adjuster visits after hail and testing catastrophe exposure before a reinsurance renewal.
What stands out is the kind of benefit these use cases promise. Across the whole Library, 13 percent of use cases include a throughput increase in their benchmark metrics. Among the surge use cases, it is 35 percent. Manual effort reduction is also more common, at 40 percent against 30 percent. Handling time and review time, the benefits that dominate most AI business cases, appear at roughly the usual rate.
That difference tells you something important about surges. The bottleneck in a surge usually sits outside the individual file: whether the right claim reaches the right person, whether adjusters drive efficient routes and whether the team is planned around the actual peak. Throughput, the number of claims the whole system can finish per day, is the measure that matters.
The best surge ideas are about planning
The behind the 20 surge use cases confirm this. Six use simulation, such as modelling how a hailstorm will flood the claims queues or testing a changed claims operation before staff are moved. Four use optimisation, such as building adjuster schedules or resequencing visits. Together that is half of all surge ideas. Only one is agentic and only one is conversational.
This is a useful correction to the market conversation. Many insurers look first at chatbots and voice assistants to take pressure off the contact centre during a peak. EIOPA's 2026 survey of generative AI found that 36 percent of insurers reported developing customer-facing applications such as voice or chatbots [2]. Those tools help, but they mostly move the queue from the phone to the claims system. The ideas that actually shorten the queue are the ones that plan capacity, assign work and route people better.
Planning ideas also have a practical advantage. They can be tested in quiet periods, using the data of previous storms. A simulation of last summer's hailstorm shows whether a new assignment model would have cleared the backlog faster. That evidence is far more convincing for a steering committee than a vendor's benchmark.
How much extra capacity can you expect?
The honest answer depends on the size of the event. AI can absorb a moderate surge within the existing team, and it can shrink the gap during a large one. It will rarely make a major catastrophe disappear from your staffing plan. The worked example below shows the difference.
The example leaves out the planning benefits, which come on top. Better adjuster assignment raises the number of inspections per day, and better routing reduces files that bounce between teams. In the Library, the use case for matching adjusters to a catastrophe surge carries a benchmark throughput increase of 1.1 to 1.3 times. For field work, that can matter more than any desk-based saving.
Prepare in calm weather
The biggest mistake with surge AI is timing. An insurer cannot introduce a new assignment model or a new triage tool in the middle of a storm. People fall back on what they know, and new tools are switched off at the first sign of trouble. Everything that should help during a peak must be live, trusted and part of normal work before the peak arrives.
That has consequences for the business case as well. A surge tool that is only used a few weeks a year looks expensive if its value is counted only in those weeks. Several of the surge use cases in the Library, such as adjuster capacity planning, are just as useful in a normal week. Count both, and be clear which part of the value belongs to the peak.
A short preparation checklist helps:
- Do you have the data of your last three peaks, with arrival times, handling times and backlog per day?
- Can you simulate a storm of twice that size against your current team and planning rules?
- Which planning or assignment idea would have reduced the backlog most, according to that simulation?
- Is that idea used in normal weeks, so that the team trusts it when the peak comes?
- Have you agreed in advance who may override the plan during a surge, and how that is recorded?
How MetroMinds helps you plan for the next peak
MetroMinds holds the 20 surge use cases with their benchmark ranges, feasibility scores and workforce impact, so you can compare planning, routing and contact centre ideas on the same basis. In your portfolio, you see which surge ideas are feasible now and which are strategic bets that need data and integration first. The business case shows the throughput and effort benefits separately, so the peak value and the everyday value are both visible.
If you are also deciding between other claims ideas, our article on AI in claims handling shows how to choose between them.
Want to see which surge ideas fit your claims operation? Book a demo
Sources
- Swiss Re Institute, "2025 marks sixth year insured natural catastrophe losses exceed USD 100 billion, finds Swiss Re Institute", press release, 16 December 2025.
- EIOPA, "EIOPA survey on Generative AI shows swift but cautious adoption among Europe's insurers", 2 February 2026.
- MetroMinds Library, 4,980 AI use cases with benchmark ranges based on real-world implementations, August 2026.
