Where this industry usually has AI opportunity
Start with work that repeats often, has a recognizable input and output, and can be reviewed before it affects a customer, candidate, client or financial record.
| Workflow | Why assess it | Control |
|---|---|---|
| Candidate research & enrichment | Repeated sourcing context and profile summarization. | Human decides relevance. |
| Interview follow-up | Summaries, actions and CRM notes repeat every call. | Recruiter approves candidate-facing messages. |
| CRM / ATS updates | Structured updates can be extracted from calls and emails. | Approval for sensitive status changes. |
| Job-to-candidate matching support | AI can surface likely matches from approved data. | Recruiter makes shortlist decisions. |
| Client progress updates | Recurring pipeline summaries and activity reports. | Human reviews external communication. |
Prioritize repeated operational work, not novelty
Count how often the workflow occurs across the whole firm, how many people touch it, how long it takes, and how frequently exceptions appear. A modest improvement to a daily cross-team workflow is often worth more than an impressive demo used twice a month.
The best first pilot often removes the repetitive middle of a workflow while keeping judgement and irreversible actions human-controlled.
Example: interview follow-up
The exact boundary should depend on the data available, error cost and approval requirements. Start narrow enough that quality can be measured against the current process.
What not to automate first
- Automated rejection decisions without appropriate review.
- Inferring sensitive traits from candidate data.
- Sending candidate or client messages without quality controls.
- Replacing reference checks or human evaluation with an opaque score.
- Automating a broken ATS process before standardizing it.
How to start
Select one workflow that happens every week, measure its current frequency and duration, define the smallest useful AI-assisted step, and run the pilot with explicit approval and exception rules. Compare time, quality and rework after the pilot—not just model output quality.