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Claims Manager

Review litigation management for claims in suit

Enhances◐ 1–3 years

What You Do Today

Monitor claims in litigation — review defense counsel bills, approve litigation strategies, track trial dates, and manage legal expense budgets.

AI That Applies

Litigation analytics — AI tracks attorney performance, predicts case outcomes, and benchmarks legal costs against similar cases to identify outlier billing.

Technologies

How It Works

The system ingests attorney performance as its primary data source. 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

You see that Defense Firm X bills 30% more than Firm Y on comparable cases with similar outcomes. Data-driven panel management saves significant legal expense.

What Stays

Litigation strategy — when to try a case, when to mediate, how to manage defense counsel — requires legal judgment and claims experience.

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 review litigation management for claims in suit, understand your current state.

Map your current process: Document how review litigation management for claims in suit works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Litigation strategy — when to try a case, when to mediate, how to manage defense counsel — requires legal judgment and claims experience. 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 review litigation management for claims in suit 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 review litigation management for claims in suit?

They're setting the automation strategy for your unit

your SIU lead

Who on our team has the deepest experience with review litigation management for claims in suit, 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 review litigation management for claims in suit, 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.