1. The business problem
Life-insurance applications arrive with different levels of complexity. A complete application with no relevant disclosures may be able to move on quickly. A current medical condition, medication, pending investigation, unusually high coverage amount, hazardous activity, or missing information needs an underwriter.
Intake staff make the first routing choice. Delaying straightforward applications wastes time. Sending a complex application ahead without review creates risk.
This illustrative case models an AI-enabled intake workflow that could exist within a primary life insurer. It uses fictional data and does not describe any insurer’s or reinsurer’s systems, data, or process.
The opportunity is deliberately narrow: help intake staff decide whether an application can continue or should go to an underwriter. The underwriter still makes every insurance decision. The assistant does not approve coverage, price risk, or set terms.
2. Scope and stakeholder decisions
I started with the business need and the decision that the workflow had to support. I then translated that into expected behaviour and acceptance criteria for a small, testable solution.
The first version had to:
- show the facts used for the recommendation;
- return one of two clear routes;
- explain which facts led to that route;
- refer uncertain or incomplete cases to an underwriter;
- make the source of every result clear.
Before a real pilot, underwriting, operations, risk, privacy, security, and governance owners would agree the route definitions, approved data, risk thresholds, stop criteria, and decision rights. They would also own the baseline and decide how any released capacity should be used.
The public demo stays within a tighter boundary. It uses eight fictional applications and accepts no personal text. Visitors choose a case; they cannot upload a document or enter an applicant’s details.
3. The first workable solution
The demo lets a visitor choose a fictional applicant, inspect the application, and request a routing recommendation. The result explains:
- why the application received that route;
- which application facts were relevant;
- what information an underwriter would need next.
Every result is checked before it appears. When a safe live recommendation is unavailable, the demo shows a reviewed example and labels it clearly. It never presents a reviewed example as a live AI response.
Try the illustrative workflow →
Technical note (optional)
The current demo can ask Claude Opus 5 for the route. Server-side checks validate the response against a fixed policy, and the explanation uses wording reviewed in advance. The interface records whether the current result came from the live model or from a reviewed reference.
4. Controls and governance
The interface calls the output a recommendation and keeps the underwriter accountable for every insurance decision.
The assistant only receives the fictional application selected by the visitor. It does not receive the expected route. Checked policy rules sit outside the AI response, so a conflicting or invalid answer cannot pass straight to the screen.
Visitors cannot enter personal information, and the demo does not save their choices. Request limits protect the public service from abuse.
A real deployment would need approved data, access controls, retained evidence, monitoring, support, and formal review. The company’s underwriting, privacy, security, legal, risk, and technology owners would set the controls and approve each release stage.
5. What I tested
On 16 August 2026, I ran all eight fictional applications through the configured live AI model. Every application followed the intended demo route: four straightforward cases continued, and four cases went to an underwriter.
I also forced a missing model connection, a slow response, an invalid response, and too many requests. In each situation, the demo showed a clearly labelled reviewed example or sent the application to a person.
These checks confirm how this small demonstration behaves. Eight fictional examples cannot establish accuracy on real applications, unseen cases, or different groups of people.
6. Pilot and adoption
I would begin with shadow mode. Intake staff would continue their normal work while the project team compared the assistant’s recommendations with human decisions.
The underwriting, risk, and governance owners would review that evidence and agree the thresholds, stop criteria, and decision rights for assisted use. If they approved the next stage, a small group of trained users could see the recommendations, override them, explain why, and report confusing results.
The pilot would include daily safety review, user coaching, a support route, and a tested switch back to human-only work. Expansion would depend on the agreed quality thresholds, user readiness, operational support, and evidence that the workflow adds value.
7. Measuring value
The main measures are time from intake to route, referral quality, user overrides, adoption, and the use of any capacity released. Shadow mode would establish the baseline before targets were approved.
The targets in the delivery plan are hypotheses for discussion with business and control owners. They are not client results. A pilot should continue, change, or stop based on evidence and decisions made by the accountable owners.
8. My role and the limits
I defined the business problem and scope, translated them into expected behaviour and acceptance criteria, directed the implementation, established safety and fallback controls, tested failure scenarios, and designed the pilot, adoption, and value-measurement approach.
My role was to connect the business need with a workable solution, coordinate the technical and governance choices, and make the route to adoption explicit.
This is an illustrative case I developed with fictional data. No insurer commissioned it, supplied data, or approved the method. It demonstrates a delivery approach, not a production underwriting service.