When claims backlogs spike, the solution seems obvious: hire more adjusters. More hands should mean faster closures, shorter cycle times, and happier policyholders.
And it works. For about six weeks.
Then reality sets in. Volumes normalize, but cycle times don’t. New hires need shadowing. Handoffs multiply. Claims still stall in the same predictable places – waiting on medical records that arrived three days ago but nobody flagged, sitting in queues because the liability determination is technically complete but never triggered the next workflow, or ping-ponging between adjusters who each need 20 minutes just to reconstruct what’s already happened.
If this sounds familiar, you’re not alone. And here’s the hard truth: You don’t have a staffing problem. You have an execution problem.
Why Claims Stall Even When Staffing Increases
Let’s be honest about where claims actually get stuck.
It’s rarely because adjusters are idle. It’s because a document arrives but is never surfaced in the claim file. A task is technically ready, but no one notices. A claim is reassigned, and work moves backward because context was lost and has to be rebuilt.
Adding more adjusters does not remove those gaps. It multiplies them. More people means more handoffs, more rechecks, and more opportunities for work to fall between cracks.
That is why staffing increases deliver short-term relief followed by long-term disappointment. You get a throughput bump for a quarter, then watch it erode as coordination overhead quietly consumes the gains.
Sustainable improvement only happens when execution itself is controlled, not just resourced.
What Makes for a Great Claims Execution
Here’s the thing: elite claims operations don’t run on superhuman adjusters. They run on execution systems that eliminate the need to constantly reconnect context.
When execution works, claims move because conditions are met, not because someone remembered to check their inbox.
In practical terms, that means:
- The system knows what’s blocking progress. Is it a missing police report? An outstanding coverage question? A liability determination that’s 80% done? The claim shouldn’t move until those gaps close and everyone should know exactly what those gaps are.
- Arrivals trigger action automatically. When that medical record hits the system, the claim state updates in real time. The next task surfaces. The right adjuster gets notified with full context, not a cryptic queue entry.
- Context travels with the claim. When a file gets reassigned or escalated, the receiving adjuster sees a complete picture instantly, not scattered breadcrumbs across five systems.
- Claims don’t stall in silence. If a claim hasn’t moved in 72 hours and all inputs are present, that’s a signal that the system recognizes and acts on.
This is how the best-performing claims teams already operate. They build execution discipline into the workflow itself instead of depending on individual heroics.
How Modern AI Changes Claims Execution
In legacy operations, coordination lives entirely with adjusters. They monitor inboxes, interpret partial signals from disconnected systems, remember what’s pending, and decide when claims are ready to advance. It’s exhausting. And it doesn’t scale.
Then comes claims automation which focuses on tasks: extract data from this document, score this injury severity, flag this potential fraud case. Useful, sure. But it doesn’t fix execution.
Here’s where AI gets interesting for claims.
Modern AI claims tools relocate that coordination burden into the workflow itself. They:
- Maintain a live operational view of every claim. Not just data fields in a core system, but actual claim state. What’s happened? What’s missing? What’s possible now?
- Observe activity across all channels continuously. Documents, emails, vendor updates, examiner notes, adjuster actions. The AI understands how each event changes the claim’s readiness.
- Surface claims proactively when conditions align. You don’t hunt for ready-to-close files. The system tells you “these 42 claims can close today” because it knows settlements are approved, releases are signed, and payments are ready.
- Pull in adjusters with full context, only when judgment matters. For routine coordination? The system handles it. For coverage disputes, negotiation strategy, or complex liability calls? Adjusters step in already knowing everything relevant.
This is orchestration, not just automation. Claims don’t move without human judgment, they just don’t stall waiting for humans to manually discover they’re ready to move.
What this Means for Your Claims Operation
If you’re a Chief Claims Officer, this shift changes your math on headcount completely.
In well-run claims operations, headcount stops being the default solution to volume spikes. Teams absorb 15-20% more claims without adding heads, because fewer hours are lost to rework, missed steps, and stalled files.
Cycle time becomes predictable and you find that you’re no longer explaining to the CFO why Q3 average days to close spiked despite hiring six adjusters.
Your adjuster retention improves because no one got into claims to spend half their day reconstructing context and chasing missing documents. When adjusters spend their time on actual claims decisions – liability analysis, negotiation, customer conversations – they stay engaged, and stay put.
And your loss adjustment expense ratio stops creeping up because you’re not layering headcount on top of broken execution, which is the fastest way to bloat LAE while throughput flatlines.
Where to Start
Fixing execution doesn’t require ripping out your core claims system or embarking on a three-year transformation program. The highest-impact changes often come from addressing coordination gaps first.
Start by diagnosing where execution actually breaks down:
Look at claims that closed in the last 30 days. Calculate actual “touch time” – hours adjusters spent making decisions, negotiating, talking to claimants. Compare that to total cycle time. If claims averaged 45 days to close but only got 8 hours of actual adjuster attention, the other 44 days and 16 hours reveal your execution gaps.
Identify your silent stalls. Pull a list of claims that have been open for 5+ days with no activity despite having no documented dependencies. These are claims dying from execution failure, not legitimate complexity.
Map your context breaks. Shadow three adjusters for a day and count how many times they need to hunt for information that should have been immediately available. Every instance is a coordination tax your operation pays repeatedly.
The solutions vary. Some teams build better workflow visibility into existing systems, others adopt new AI solutions like Clive ™, a multi-agent claims expert that sits on top of current infrastructure, and some redesign handoff protocols to preserve context. What matters is recognizing that execution problems need execution solutions, not staffing band-aids.
Looking to diagnose execution gaps in your claims operation? We’ve built tools specifically for this challenge. Learn more about how Clive approaches claims orchestration.
Frequently Asked Questions
How do I know if I have an execution problem vs. a staffing problem?
Look at your claims that closed in the last 30 days. Calculate actual “touch time” – hours adjusters spent making decisions, negotiating, talking to claimants. Now compare that to total cycle time. If claims averaged 45 days to close but only got 8 hours of actual adjuster attention, you have an execution problem. The other 44 days and 16 hours? That’s waiting, handoffs, and coordination friction.
What’s the fastest way to identify execution gaps in my operation?
Start with “silent stalls” – claims open for 5+ days with no activity and no documented blockers. Then track context reconstruction time—how long adjusters spend gathering information that should be immediately available when they open a claim. These two metrics reveal execution breakdowns better than any operational dashboard.
Won’t AI just create more problems for adjusters to fix?
It depends on the AI. Task automation without execution awareness often does create more work. But AI built specifically for orchestration – maintaining claim state, coordinating across systems, surfacing work when conditions are met -removes coordination burden rather than adding to it. The key is whether the AI understands the full claim context or just individual tasks.
Does fixing execution require replacing our core claims system?
Usually not. Most execution problems live in the coordination layer between systems, not in the systems themselves. Teams can often address execution gaps by adding orchestration capabilities on top of existing infrastructure rather than replacing it.
How much volume increase can a well-executed operation handle before needing headcount?
It varies by line of business and complexity, but operations that address execution gaps typically absorb 15-25% volume increases without adding staff. Some teams have grown claim volume 30%+ while reducing average cycle time and maintaining team size.
What’s the difference between claims automation and claims execution improvement?
Automation replaces specific tasks such as extract data, score a claim, send a template email. Execution improvement coordinates how work flows across people, systems, and tasks. Automation handles steps. Execution ensures claims actually progress through all the steps without stalling. You can have lots of automation and still have terrible execution.
