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Director of Claims

Drive process improvement and automation initiatives

Automates◐ 1–3 years

What You Do Today

Identify claims processes that are slow, manual, or error-prone. Lead automation and improvement projects that reduce cycle time and cost while maintaining or improving quality.

AI That Applies

Process mining that reveals how claims actually flow through the system, identifying bottlenecks, rework loops, and automation opportunities from actual process data.

Technologies

How It Works

The system ingests actual process data as its primary data source. The automation engine executes each step in the process sequence — validating inputs, applying business rules, generating outputs, and routing exceptions to human review queues. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Improvement targeting becomes data-driven. AI shows you exactly where time is lost and where automation will have the biggest impact.

What Stays

Leading process change in a claims organization requires buy-in from experienced adjusters who are skeptical of changes that might compromise quality.

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 drive process improvement and automation initiatives, understand your current state.

Map your current process: Document how drive process improvement and automation initiatives works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Leading process change in a claims organization requires buy-in from experienced adjusters who are skeptical of changes that might compromise quality. 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 Celonis 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 drive process improvement and automation initiatives 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

Which steps in this process are fully rule-based with no judgment required?

They're setting the automation strategy for your unit

your SIU lead

What's the error rate on the manual version, and what would "good enough" look like from an automated version?

AI fraud detection changes how investigations are triggered and prioritized

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.