Most mid-sized employers in insurance, lending, healthcare and other regulated sectors are past the question of whether to use AI. The harder question is who owns it once it is running. A model that triages claims, scores applicants or drafts customer letters is making decisions all day. Someone has to be able to show a regulator, an auditor or a customer that those decisions are controlled.
That is a staffing problem before it is a technology problem.
Why the old compliance team is not enough
Most compliance functions were built around periodic review. A policy is written, a control is tested once a quarter, a report goes to the committee. That works for systems that change slowly.
AI systems change as data changes. They pull from several sources, often sit inside vendor products, and can act without a person looking at each result. A quarterly check tells you how the model behaved three months ago. It does not tell you how it behaved last Tuesday.
The practical shift is from proving compliance at intervals to showing it continuously. That needs people who understand both the controls and the systems being controlled, and those people are scarce on both sides of the border.
The roles that close the gap
You do not need a large team. You need the right few seats filled by people who have done the work before.
- Data governance lead. Owns data lineage, access rules and quality standards. Without this person, every model question turns into a search for where the data came from.
- Model risk analyst. Validates models, documents assumptions, tests for bias and drift. Often comes from banking or insurance risk backgrounds.
- Data or ML engineer with production experience. Builds the monitoring, logging and testing that make continuous oversight possible rather than a spreadsheet exercise.
- Compliance or privacy specialist who can read technical documents. Translates regulation into controls the engineers can build. In Canada that means familiarity with provincial privacy law and federal guidance. In the US it often means state-by-state rules.
- Operations or process owner. Someone from claims, underwriting, lending or servicing who owns the business outcome and can say when a model result does not make sense.
Where contract talent fits
Modernization work comes in waves. A data migration, a model inventory, a documentation backlog or a monitoring build can need five specialists for six months and none afterward.
Contract talent suits that work well, with two conditions.
- Screen for regulated experience. A strong engineer who has never worked under audit will slow you down. Ask for examples of documentation they produced and who reviewed it.
- Plan the handover. Contractors should leave behind runbooks, decision logs and a named internal owner. Institutional knowledge that walks out the door is its own compliance risk.
Keep permanent seats for the roles that hold accountability: governance lead, model risk and the business owner. Use contract capacity for the build.
Retention matters more here than elsewhere
In a regulated setting, turnover is not only a cost. Every departure takes with it knowledge of why a control exists, which exception was approved and what the last auditor asked. Replacements spend months rebuilding that context.
Three things help keep these people:
- Clear scope, so they are not asked to be engineer, auditor and project manager at once.
- A seat in decisions, not only in sign-off at the end.
- Pay that reflects the scarcity. Hybrid skill sets in risk and data command a premium, and underpaying them is a common reason pilots stall.
Vendor risk is a hiring issue too
Much of your AI exposure sits with third parties: the software vendor, the data provider, the outsourced team. Someone in-house needs to read the contracts, ask about model changes and keep an inventory of where AI is embedded. That is often a procurement or vendor management person with enough technical grounding to ask good questions. It is a role many mid-sized employers forget to staff.
A sensible order of hiring
- Name an accountable executive, even if the role is part-time.
- Hire or contract the data governance lead and take inventory of every model and AI-enabled tool in use.
- Add model risk capacity to validate the highest-impact use cases first, such as anything touching pricing, eligibility or claims decisions.
- Bring in engineering support to automate monitoring.
- Review the team every six months as regulation and use cases change.
Trying to buy a platform before assigning ownership is the most common mistake. Tools help, but they only report. People decide what to do about what they report.
What to do next
List every place AI touches a customer, an employee or a financial decision in your business, then mark which ones have a named owner and a way to check behaviour between audits. The unmarked ones tell you which roles to fill first.
If you need data, AI, finance or operations people who have worked under regulatory scrutiny, a Traverse recruiter can help you scope the roles and find them.