Most finance teams have run an AI pilot by now. A fraud model, a claims triage tool, a chatbot for routine questions. The harder part comes next: running these tools every day, under audit, with real customer data. That part depends on people, and most mid-sized employers are short of them.
The work changes after the pilot
A pilot needs a few curious people and a vendor. Production needs ownership. Someone has to be accountable when a model drifts, when a regulator asks how a decision was made, or when a data feed breaks at month-end close.
In practice that means a few specific jobs that did not exist in many finance departments three years ago:
- Model owners who sit in the business, understand the process the model supports, and sign off on changes.
- Data engineers and analysts who keep the inputs clean and can trace where a number came from.
- Validation and risk staff who test models independently of the people who built them.
- Operations specialists who redesign the workflow so the tool saves time instead of adding a review step.
Smaller employers rarely need all four as full-time hires. They do need each function covered by someone, and that is where gaps appear.
Where the work is landing
The common uses are not exotic. They are high-volume, rules-heavy tasks:
- Fraud and anti-money laundering alerts, where AI helps reduce the pile of false positives analysts work through.
- Customer service, where routine questions are handled automatically and staff take the complex ones.
- Claims and policy servicing in insurance, where speed on simple claims matters most.
- Back-office work such as reconciliations, document intake and reporting.
- Investor and stakeholder communications, where finance and IR teams are asked for faster, more detailed answers.
Each of these changes what a good hire looks like. An AML analyst who can judge a model's output and challenge it is worth more than one who only clears a queue. A controller who understands how an automated reconciliation works can spot when it is wrong.
Governance is a hiring question
Regulators in both countries expect firms to explain and monitor the models they use. In the US, the NIST AI Risk Management Framework is widely referenced. In Canada, OSFI's guidance on model risk management applies to federally regulated institutions, and many provincial regulators are watching closely. Misstating what your AI does can also draw enforcement, as the SEC showed in 2024 with its actions against advisers who overstated their AI use.
Those expectations translate into a short list of questions for any employer:
- Who owns each model, by name?
- Who validates it, and are they independent of the builder?
- Who watches for drift and bias after launch?
- Who can explain a decision to an examiner or a customer?
- Where does a human review high-impact decisions such as credit or claims denials?
If the honest answer to two or more is "nobody yet", that is your hiring plan.
Skills are the bottleneck
Tools are easy to buy. People who can use them well are not. Gains tend to show up where staff are trained, not where a licence is simply switched on. That points to two moves that work together: hire a small number of experienced specialists, and train the existing team to work alongside the tools.
When screening candidates, look for:
- Experience putting a model into production, not only building one in a notebook.
- Comfort with audit trails and documentation, which finance and insurance require.
- Ability to explain a technical result to a controller or compliance officer in plain language.
- Judgment about when not to trust the output.
Be wary of candidates, and vendors, who oversell. A person who can describe a failed deployment and what they changed is usually more useful than one with a flawless story.
Contract or permanent
Much of the early production work is project-shaped: data clean-up, model validation, workflow redesign, documentation for an upcoming exam. Contract specialists suit that work well and let you test how much ongoing capacity you need. Model ownership and data stewardship, on the other hand, should sit with permanent staff who know the business and will still be there when the regulator returns.
What to do next
List every AI tool currently touching customer data, money movement or credit and claims decisions. Next to each, write the owner, the validator and the monitor. Wherever a box is empty, decide whether to hire, contract or train. Do that before the next budget cycle, not after the first audit finding.
If you want help finding AI, data, finance or operations people for that work, a Traverse recruiter can help.