AI can help extract and organise information from documents, but a useful system needs more than a model. You must define the fields required, validate the output and decide what happens when the result is uncertain or wrong. Start with one document type and one task rather than automating the entire office at once.
What is the difference between OCR and AI extraction?
OCR turns text in an image or scan into machine-readable text. Extraction then identifies useful fields, such as a reference number, date or line item. Classification can help route different document types to the right workflow.
These stages can fail in different ways. A clearly recognised sentence does not necessarily mean the correct field was selected. A plausible-looking answer is not evidence that it appeared in the source document.
Choose a narrow first use case
A good pilot has a clear input, required fields and a destination system. For example, staff might currently copy a small set of fields from incoming documents into an internal application. Keep that example bounded: the pilot proposes values, while a person confirms them before they are saved.
Measure the existing process first. How much time does entry take, how often are corrections needed and which documents require special handling? Without a baseline, a convincing demo can hide additional review work.
How should you test accuracy?
Use an authorised, representative sample, with appropriate handling of sensitive information. Include different layouts, poor scans, missing values and documents that should be rejected. Keep evaluation examples separate from the material used to configure the system.
Compare each required field with a checked answer. Record missing values and wrong values separately. Also measure review time: an extraction tool is not useful if verifying it takes longer than the original task.
Where does human review belong?
Route uncertain cases and high-impact fields for checking. Confidence scores can help prioritise review, but they should not be treated as guarantees of correctness. Google’s Document AI extraction documentation describes field-level confidence and review triggers; your actual thresholds still need evaluation on your documents.
Let reviewers see the source next to the proposed value. Preserve corrections and a record of what was accepted. Avoid automatically sending an unverified result into a consequential downstream action.
What about access and integration?
Decide who can upload, view, correct and export documents. Understand where the selected provider processes data, how long it retains it and whether your organisation permits that use. Do not upload confidential files into a trial account without appropriate approval.
Plan duplicate handling, failed imports and a manual fallback when the service is unavailable. Those details are part of the product, not optional finishing touches.
When should you stop or expand the pilot?
Agree success criteria in advance: useful accuracy on the important fields, manageable review effort and acceptable operating cost. If the results fall short, improve the input process or keep the task manual rather than declaring success.
Diloxy Labs can help assess a focused pilot. Our AI readiness service starts with the data and feasibility before a larger implementation.