Choose a first AI use case that is high-volume, repetitive and measurable, where the inputs are documents, emails or text you already have, and where a person can review uncertain results. Document capture, email triage and internal knowledge questions are common first wins. Avoid starting with rare, high-stakes decisions or with processes that are not yet defined — AI amplifies a clear process; it does not fix an unclear one.
Score each candidate
List five to ten processes where people spend time on repetitive reading, typing or searching. Score each from 1 to 5 on these criteria:
| Criterion | Score high when… |
|---|---|
| Volume | The task happens hundreds or thousands of times a month |
| Measurable cost | You can count the hours, errors or delays today |
| Data available | The inputs already exist digitally — documents, emails, records |
| Tolerance for review | A person can check uncertain cases without slowing the process |
| Clear definition | Two staff members would reach the same answer for the same input |
| Low integration effort | The output can go into an existing system easily |
The highest total is usually a good first project. A high score on volume and measurable cost matters most — that is what makes the return visible.
Common first wins
- Document processing — supplier invoices, delivery notes, applications. See AI document processing.
- Email triage — classifying, routing and drafting replies to high-volume inboxes.
- Internal knowledge assistant — answering policy, procedure and product questions from your own documents. See AI assistants.
- Summarisation — reports, meeting notes, case files and maintenance logs.
Warning signs
- The process changes every time or depends on unwritten judgement.
- Errors would be costly and nobody will review outputs.
- The data is on paper, in people’s heads or spread across systems you cannot access.
- The main goal is “to have AI” rather than a measurable outcome.
Questions to settle before you build
- What will success look like in numbers — hours saved, turnaround time, error rate?
- Which personal information is involved, and how will POPIA obligations be met?
- Which AI provider and settings ensure your data is not used for training?
- Who reviews exceptions, and how is feedback captured to improve the system?
- How will accuracy be measured before launch and monitored afterwards?
Try our free AI Process Blueprint to turn a process description into a structured automation plan, or take the Automation Readiness Check.