Change Management for Underwriting Teams: Getting Adoption
InsOps
October 13, 2026
Your new underwriting tool went live months ago. Training is complete, licenses are paid, and the project team has moved on. Yet underwriters still start each submission in the old spreadsheet.
This is an adoption problem, not a technology problem. Carriers are putting tools in front of underwriters faster than they are building the plan that gets those tools used every day.
This article explains why usage stalls after go-live, how to find the exact point where it stalls, and how to fix it with a five-rung sequence called the Underwriter Adoption Ladder. It ends with a 90-day plan you can start this week.
Why Do Underwriters Stop Using a Tool After Go-Live?
Underwriters stop using a new tool when it asks for their trust before it earns it and sits outside the screens where they already work. The technology rarely fails first. The plan around it does.
Three patterns from 2026 research show how this plays out inside carriers.
Tools Arrive Before the Plan
Convr's 2026 Insurance Talent and Tech Trends Survey, covering 211 commercial insurance professionals, found that 70.6% of respondents' organizations delivered new AI underwriting tools in 2025. Only 20.4% of leaders said they are highly confident their organization has a clear, actionable AI strategy for underwriting. Insurance Business reported the findings on June 3, 2026.
That gap between deployment and strategy is where adoption stalls. Without a plan, each underwriter decides alone whether the tool is worth the effort.
Trust Lags Behind Usage
A Sixfold-commissioned study of 543 underwriting executives and underwriters, all using or piloting AI, found that no underwriter treats AI as a fully trusted part of how they work. Among underwriters, 38% use the tools when required and 33% experiment on their own, according to Insurance Journal.
Required use is compliance, not adoption. Compliance fades the day the requirement relaxes.
Leaders and Underwriters See Different Results
The same study shows a perception gap. Of the executives surveyed, 92% reported better underwriting decisions from AI, while 76% of underwriters saw the same benefit.
A sponsor who judges the rollout by executive sentiment misses a 16-point gap at the desks where decisions get made.
How Do You Find Where Adoption Is Stalling?
Compare what the system records with what underwriters do on a live submission. The difference between the two shows which part of adoption has failed.
Start with data you already own, then confirm it with conversations.
Read the Usage Signals
Each pattern below points to a specific gap and a specific rung of the ladder described in the next section.
Signal in your data | What it points to | First fix |
|---|---|---|
Logins are high, but tool output rarely shows up in file notes | Compliance without trust | Rung 3, prove it on live files |
Usage clusters among newer underwriters | Senior underwriters are not convinced | Rungs 1 and 4, sponsors and champions |
Usage drops on complex risks | The tool does not fit judgment-heavy work | Rung 2, narrow to the slowest routine task |
Usage spikes after training, then fades | Nothing reinforces the habit | Rung 5, measure by task |
Ask Underwriters Directly
Run short conversations with six to ten underwriters across experience levels. Ask the same three questions each time.
Which step did you do the old way last week, and why?
What would you need to see before relying on the tool's output?
What does the tool make slower?
The answers name your real blockers. They also tell you which underwriters will become your first champions.
What Is the Underwriter Adoption Ladder?
The Underwriter Adoption Ladder is a five-rung sequence that moves a team from required use to daily use: sponsor, shape, prove, train, and measure. Each rung depends on the one below it.
Skipping to training before sponsors and workflow fit are in place produces the spike-and-fade pattern in the table above.
Rung | Job | Done when |
|---|---|---|
1. Sponsor | Name two or three respected underwriting leaders as visible owners | Underwriters hear the reason for the change from their own leaders |
2. Shape | Pick the slowest routine task and place the tool inside it | The tool shows up in the systems underwriters already use |
3. Prove | Run the tool on live files with full override rights | Underwriters see why an output was produced and can change it |
4. Train | Train by role and appoint champions | Each role has hands-on practice, not a lecture |
5. Measure | Track usage per task and review it monthly | Usage data reaches the sponsors and changes the plan |
Rung 1: Name Visible Sponsors
Pick two or three underwriting leaders whose files other underwriters respect. Ask them to use the tool in front of the team, on real work, within the first two weeks.
A sponsor in the underwriting chain carries more weight than a project steering committee. Underwriters read the behavior of their own leaders faster than they read a launch email.
Rung 2: Shape the Rollout Around the Slowest Task
In the Sixfold-commissioned study, 83% of underwriters reported spending at least 30 minutes, and up to four hours, chasing missing information from brokers on a single risk. That is the kind of task to start with.
A tool that removes the step underwriters dislike most earns attention that a general rollout never does. Place it inside the system underwriters already open each morning, so using it costs no extra clicks.
How Do You Earn Underwriter Trust in the Output?
Underwriters trust an output when they can see what produced it, change it, and compare it with their own judgment on a file they already know. Trust comes from evidence on familiar work.
Convr's survey describes the same need. 56.9% of respondents called their organization's attitude toward AI cautiously open, interested but wanting proof before a stronger commitment.
Rung 3: Prove It on Live Files
Run a two-week shadow period. Underwriters work files as they always do, then compare their result with the tool's output on the same submission.
Log every disagreement in a shared document and review it weekly with the sponsors. Show the factors behind each output, and keep the underwriter as the final decision maker. Every output stays a recommendation until an underwriter accepts or changes it.
Rung 4: Train by Role and Appoint Champions
In Convr's survey, 47.4% of respondents cited AI tool training as the thing that would most help underwriting teams. Generic sessions fall short of that need.
Build one short, hands-on session for each role: new underwriters, senior underwriters, underwriting managers, and assistants. Then appoint champions inside the underwriting team to answer questions at the desk. Edana's 2026 underwriting guidance recommends the same pairing, a training roadmap plus champions within business teams.
How Do You Keep Adoption From Fading After Month One?
Adoption holds when usage is measured per task, reviewed monthly by the sponsors, and fed back into both the tool and the training. Without that loop, habits return to the old process within weeks.
Rung 5: Measure Usage by Task, Not by Login
A login count says nothing about whether the tool changed a decision. Track the signals that show use inside the work.
Metric | What it tells you |
|---|---|
Share of submissions where the output was opened before a decision | Whether underwriters rely on the tool or ignore it |
Override rate, with a reason captured each time | Where the tool misses and where trust is forming |
Time from submission received to first underwriter action | Whether the slowest task actually moved |
Open blockers reported by champions | Whether the team feels heard |
Edana's guidance also recommends tying adoption indicators to employee objectives, which keeps the numbers in front of managers.
Treat Overrides as Signal
A low override rate does not prove trust. It can also mean underwriters never looked at the output.
A moderate override rate with a stated reason each time is the healthier pattern. Those reasons feed the next round of tool tuning and the next training session.
Run a 90-Day Plan
Days | Focus | Output |
|---|---|---|
1 to 30 | Sponsors and baseline | Sponsors named, six to ten interviews done, one task chosen, current time on that task recorded |
31 to 60 | Shadow and train | Two-week shadow period complete, disagreement log reviewed, role-based sessions delivered, champions active |
61 to 90 | Expand and measure | Task-level metrics reported monthly, one second task selected, blockers closed |
How InsOps Helps
InsOps builds an insurance-trained AI that assists with getting underwriting data into the systems your underwriters already use. LiLa, our insurance-trained LLM, runs inside your own environment, so PII and PHI never leave controlled infrastructure. A person reviews and validates every field mapping before it is deployed.
Our Integration Gateway connects to Guidewire PolicyCenter, UnderwritingCenter, and other Guidewire systems, so data flows directly into your underwriting workflow without custom engineering. That supports Rung 2 of the ladder, because the tool lives where underwriters already work.
InsOps migrates legacy data into Guidewire and keeps it flowing in real time.
If you are evaluating how to give underwriters the data they need inside their current systems without a long engineering build, contact us to talk through what this looks like for your operation.
Frequently Asked Questions
Q: What is change management for underwriting teams?
A: Change management for underwriting teams is the structured work of preparing underwriters to use a new tool or process. It covers sponsorship, workflow fit, training, and measurement, so use continues after go-live.
Q: Why does adoption matter more than the tool itself?
A: A tool that underwriters open only when required delivers none of the consistency or time savings in the business case. The license cost is spent, and the old process keeps running beside it.
Q: How do you get underwriters to actually adopt a new underwriting tool?
A: Start with the slowest routine task and place the tool inside the system underwriters already use. Prove it on live files, keep the underwriter in final control, and back it with visible sponsors and champions. Follow the five rungs in order.
Q: What are the most common challenges?
A: The three that recur are a missing strategy at deployment, low trust in outputs, and a gap between how leaders and underwriters judge results. Each has a direct fix on the ladder: sponsors, proof on live files, and task-level measurement.
Q: Which metrics show adoption is working?
A: Track the share of submissions where the output was opened before a decision, the override rate with reasons, time to first underwriter action, and open champion blockers. Logins alone do not count as adoption.
Q: How does InsOps assist underwriting teams?
A: InsOps assists with moving and mapping underwriting data into Guidewire, with a person validating every mapping before deployment. Because LiLa runs inside your environment, sensitive data stays in your controlled infrastructure.
Q: How long does adoption take, and what goes wrong?
A: Plan for 90 days from naming sponsors to steady, measured use. The most common mistake is training before the tool fits the workflow, which produces a short spike in usage and a fade.