How do you choose a useful first AI project?
Choose a repeatable task with accessible, permitted data and a result your team can evaluate. Compare an AI pilot with the current process, including review time and running costs. Keep a human review path for uncertain results and expand only when the pilot meets your agreed criteria.
The strongest place to start with AI is often a repetitive task that already has a clear definition of a good result. Document classification, information extraction, and search can offer a focused starting point.
Evaluate whether your data is suitable before selecting a model. Check quality, permissions, coverage, and the consequences of an incorrect result. Build a representative evaluation set and compare the proposed system against the current process.
Design a review path for uncertain outputs. People need to understand the limits of a system and know how to correct it. Start with a bounded pilot, observe the results, and expand only when the evidence supports it.
Example: extracting information from invoices
In an illustrative invoice workflow, a system could suggest the supplier, invoice date, and total for a person to check. Begin with representative documents, including scans, unfamiliar layouts, and missing fields. Keep the source document beside the proposed values so the reviewer can verify them. This example does not describe a measured client deployment.
- Confirm permission to process the documents and decide where they may be stored.
- Define required fields and how to handle duplicates or missing values.
- Route ambiguous output for review before downstream actions.
- Keep evaluation documents separate from examples used to tune the system.
What should you measure in an AI pilot?
Measure correctness against checked examples, the amount of human correction, total completion time, and operating cost. A quick generated answer is not useful if review and correction take longer than the original task. Define unacceptable errors before evaluating the system.
- Compare results with the existing manual or rules-based process.
- Include difficult cases as well as routine documents.
- Record failures and agree on a fallback when the system cannot provide a reliable result.
Put this into your project brief
Write down the outcome you want, the people affected, and the constraints you already know. Include examples of the current process and questions you need help answering. These details make a first project discussion more useful.
Discuss your project