4 use cases · Last updated 11 August 2026
AI use cases for professional services cover the workflows this sector runs every day. Each entry below is scored for impact, implementation effort and the data you need before you start.
| Use case | Lever | Impact | Effort |
|---|---|---|---|
| Answer sales questions from a product knowledge base | Revenue growth | 3/5 | 3/5 |
| Extract terms from supplier contracts | Risk & compliance | 3/5 | 3/5 |
| Flag anomalous expense claims before payment | Risk & compliance | 3/5 | 3/5 |
| Screen inbound CVs against a structured scorecard | Time savings | 3/5 | 4/5 |
Sales question answering lets sellers get an accurate product answer in seconds instead of waiting on a specialist, by retrieving from approved internal documentation and citing the source passage, typically removing the queue in front of a small team of experts rather than replacing the experts themselves.
Contract term extraction lets legal and procurement teams see the obligations buried across a supplier portfolio, by reading each agreement and pulling renewal dates, liability caps, indemnities and price mechanisms into a structured register, typically producing output around the standard of a junior reviewer that still needs a qualified check.
Contract term extraction lets legal and procurement teams see the obligations buried across a supplier portfolio, by reading each agreement and pulling renewal dates, liability caps, indemnities and price mechanisms into a structured register, typically producing output around the standard of a junior reviewer that still needs a qualified check.
Expense anomaly detection lets finance teams examine every claim instead of a sample, by scoring each submission against policy rules and against patterns in the organisation’s own spending history, then holding only the suspicious ones for a human decision before the payment run, typically shortening the gap between a problem and its discovery.
CV screening lets recruiters rank a high-volume applicant pool against a written scorecard, by extracting evidence for each criterion from every application and scoring it consistently, typically cutting first-pass reading time while creating an audit and bias-testing obligation that must be settled before it goes near a live vacancy.
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