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Claims Manager

Manage team workload and assignment

Enhances✓ Available Now

What You Do Today

Distribute new claims across your adjusters based on complexity, specialty, and current caseload. Reassign when someone gets overwhelmed or when a claim needs escalation.

AI That Applies

Intelligent assignment — AI matches claims to adjusters based on complexity, adjuster expertise, current caseload, and historical performance on similar claims.

Technologies

How It Works

For manage team workload and assignment, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Your best auto physical damage adjuster gets the complex total losses. Your developing adjuster gets the coaching-opportunity claims. Assignment is strategic, not round-robin.

What Stays

Knowing your team — who's burning out, who needs a challenge, who can handle the difficult claimant — and managing them accordingly.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for manage team workload and assignment, understand your current state.

Map your current process: Document how manage team workload and assignment works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Knowing your team — who's burning out, who needs a challenge, who can handle the difficult claimant — and managing them accordingly. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Guidewire tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long manage team workload and assignment takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your claims director or VP Claims

What data do we already have that could improve how we handle manage team workload and assignment?

They're setting the automation strategy for your unit

your SIU lead

Who on our team has the deepest experience with manage team workload and assignment, and what tools are they already using?

AI fraud detection changes how investigations are triggered and prioritized

a claims adjuster with 15+ years experience

If we brought in AI tools for manage team workload and assignment, what would we measure before and after to know it actually helped?

Their judgment sets the benchmark that AI tools are measured against

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.