Most insurers, brokers and finance-adjacent employers in Canada and the US now have an AI pilot somewhere. Fewer have a team that can run it when claim volume doubles or a regulator asks how a decision was made. The gap is rarely the software. It is who owns the output, who checks it and who can be added when the workload jumps.
This post is for operations leaders, controllers and owners who are being asked to scale AI in document-heavy, rules-heavy work. It covers where the people questions sit and what to hire for.
Treat it as an operating change, not a tool purchase
Document intake, summarization, triage and anomaly flagging are the uses that tend to pay back first, because they save time on work that already exists. Each one changes who does what. An adjuster who used to read 40 pages now reviews a summary and signs off. An underwriter who used to sort submissions now handles the exceptions the system routes up.
That shift changes job content, so it changes hiring. If you buy the tool and leave the job descriptions alone, you get a tool nobody trusts and a team that quietly works around it.
Name an owner for every automated decision
Before a use case goes live, write down one person who is accountable for its output. Not a committee. Not the vendor.
That owner needs to:
- Understand the work being automated well enough to spot a wrong answer
- Have authority to pause the process
- Report on error rates and exceptions on a fixed schedule
- Know what your regulator, auditor or insurer expects in terms of records
In practice this is usually an experienced operations manager or senior claims or underwriting lead, not a data scientist. Hire or promote with that in mind.
The roles that matter most
In our experience, employers scaling this kind of work end up short in the same few places.
| Role | What it does | Where it fits |
|---|---|---|
| Process or operations analyst | Maps the workflow, measures cycle time and error rates | Operations |
| Data analyst or engineer | Cleans and moves data between systems | AI and data |
| Quality and controls reviewer | Samples outputs, documents exceptions | Operations, finance |
| Experienced domain specialist | Reviews flagged cases, owns decisions | Claims, underwriting, finance |
Notice what is missing: a large team of model builders. Most mid-sized employers are configuring and governing tools, not inventing them. The scarce people are those who know the business and can work alongside the technology.
Fix the data before you hire for the model
Many projects stall because information does not move cleanly between policy, billing and claims systems, or because the same field means different things in different departments. A strong data analyst who spends six months standardizing inputs will often do more for results than a more advanced tool.
If your data work is sitting on one overloaded person, that is the first hire to look at. A contract analyst is often a sensible way to start, since the clean-up has a clear scope and end date.
Plan for surges with flexible capacity
A storm, a rate change or a month-end close can push volume up overnight. Automation absorbs some of it, but the cases that need judgment still land on people. If your only answer is overtime, quality slips exactly when scrutiny is highest.
Build a bench before you need it:
- Keep a short list of contract adjusters, claims processors, underwriting assistants or accounting staff who already know your systems
- Train them on the AI-assisted workflow, not just the old one
- Agree rates and onboarding steps in advance so a start date is days, not weeks
- Review the bench each quarter against actual volume swings
Contract and temporary staff suit this well, provided the review and sign-off steps stay with accountable permanent employees.
Customer-facing automation needs people behind it
Chat and voice assistants can take routine servicing requests. They also create new work: setting escalation rules, reading transcripts, fixing wrong answers and handling the customers who ask for a person. Staff a small quality team for this from the start. An unclear handoff to a human damages trust faster than a slow reply does.
Fraud and risk work needs explainable decisions
Pattern detection can surface suspicious cases earlier, but a person has to be able to explain why a file was flagged and what was done about it. Hire investigators and risk analysts who are comfortable questioning a score, not just accepting it.
A short checklist
- One named owner for each automated process
- A written exceptions and escalation path
- A data owner for each source system
- A reviewed, trained surge bench
- A quarterly check that job descriptions still match the work
What to do next
Pick one AI-assisted process and ask three questions: who owns the output, who can step in when volume jumps, and who is cleaning the data feeding it. The weakest answer tells you which hire or contract placement to make first. A Traverse recruiter can help you find the analysts, operations leads and flexible capacity to fill it.