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Underwriting Manager

Review team's pending submissions queue

Enhances✓ Available Now

What You Do Today

Check the queue depth, aging, and distribution across your team. Identify submissions that need to be reassigned, expedited, or escalated based on producer priority and complexity.

AI That Applies

Intelligent queue management — AI prioritizes submissions based on premium potential, producer tier, renewal date, and complexity to optimize team workflow.

Technologies

How It Works

The system ingests premium potential 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

Your team works the highest-value submissions first instead of FIFO. The AI routes straightforward renewals for auto-processing while flagging complex new business for your senior underwriters.

What Stays

Knowing your team's strengths — who handles the tough accounts, who needs the mentoring cases — and managing workload to prevent burnout.

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 team's pending submissions queue, understand your current state.

Map your current process: Document how review team's pending submissions queue 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's strengths — who handles the tough accounts, who needs the mentoring cases — and managing workload to prevent burnout. 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 team's pending submissions queue 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 chief underwriting officer or VP Underwriting

What data do we already have that could improve how we handle review team's pending submissions queue?

They're setting the AI strategy for risk selection

your actuarial lead

Who on our team has the deepest experience with review team's pending submissions queue, and what tools are they already using?

They build the models that AI underwriting tools are measured against

a senior underwriter with deep book knowledge

If we brought in AI tools for review team's pending submissions queue, what would we measure before and after to know it actually helped?

Their judgment is the benchmark — AI should match it, not replace it

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.