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Policy Administration Manager

Manage system configuration for product changes

Enhances◐ 1–3 years

What You Do Today

When underwriting introduces new products, endorsements, or rating changes, you configure the policy admin system, test the changes, and coordinate the rollout.

AI That Applies

Configuration testing — AI generates test scenarios based on the change specification, runs regression tests, and identifies edge cases the manual QA process would miss.

Technologies

How It Works

The system ingests change specification 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 output — test scenarios based on the change specification — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Testing that took 3 weeks takes 3 days. The AI generates 10,000 test cases covering edge combinations that a human tester wouldn't think to try.

What Stays

Understanding the business intent behind the product change, configuring it correctly, and managing the deployment — that's policy admin expertise.

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 system configuration for product changes, understand your current state.

Map your current process: Document how manage system configuration for product changes works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Understanding the business intent behind the product change, configuring it correctly, and managing the deployment — that's policy admin expertise. 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 system configuration for product changes 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 VP Operations or COO

What data do we already have that could improve how we handle manage system configuration for product changes?

They're prioritizing which operational processes to automate

your process improvement or lean lead

Who on our team has the deepest experience with manage system configuration for product changes, and what tools are they already using?

They understand the workflow dependencies that AI tools need to respect

a frontline supervisor

If we brought in AI tools for manage system configuration for product changes, what would we measure before and after to know it actually helped?

They see the daily reality that AI tools need to fit into

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.