Start with each field and its downstream use
Before trying AI, list the values the workflow needs and how they will be used. A delivery note might require an order number, item code, quantity, and unit. Each field has a different downstream role: a wrong item code may need a different response from a missing note.
Define the boundary too. Will the tool prepare a draft for review, or will selected results enter another system without re-entry? These modes need different acceptance conditions. A successful demonstration document does not approve the complete workflow.
Make the sample resemble incoming work
Include different suppliers, page layouts, and multi-item cases. Distinguish text-based files from scans and photographs. Check language, image quality, and format support for the specific tool.
List operational cases such as an incomplete attachment, rotated page, handwritten amendment, or repeated item across rows. Decide whether each is supported, returned for completion, or passed to a person. Use approved environments for test material and remove unnecessary information.
Prepare verified values before evaluation
Create checked expected values for the test documents. Compare predictions with this reference field by field rather than relying on a processed-document status. Track wrong values separately from values not found.
Agree what constitutes a match. Leading zeros may matter in order numbers; units and decimal separators matter in quantities. In our model workflow, ten packs do not automatically mean ten individual units. The process owner must approve conversions rather than letting them disappear into text cleanup.
An overall score cannot explain every error
Review results by field and document type. Correct names alongside incorrect item codes can produce an overall assessment that obscures operating impact. Record an example and downstream consequence for each issue.
Set automation boundaries from measured results and error impact. If the tool returns confidence scores, verify their meaning and behavior on the sample. Low confidence can route work to a person; high confidence alone does not validate the workflow.
- An incorrect value.
- A missing required field.
- A value assigned to the wrong row.
- A quantity and unit mismatch.
- A case outside the agreed scope.
Give reviewers the underlying evidence
Define the exception view: original document, highlighted field, proposed value, and review reason. This avoids searching another inbox for an attachment. Specify who may correct data and what happens if the source cannot establish the value.
In the model receipt process, staff may confirm a quantity, request a replacement document, or defer a case with a reason. Each action should produce a clear state. Include this review time in the pilot: a quickly generated draft is not automatically an operational gain.
Check the path into the receiving system
Correct extraction does not necessarily create a correct operating record. Test order and item matching, destination requirements, and repeated attachment submission. Distinguish drafts, confirmed writes, and cases awaiting correction.
Compare the current and proposed workflows including review, repairs, and exception handling. Record manually completed cases too. This supports a decision to expand, restrict automation to suitable documents, or improve inputs first.
Expand after reviewing the evidence
Keep pilot results with the solution version, sample description, and agreed rules. Recheck relevant cases when the tool, configuration, or document types change. Retain examples that exposed errors so later changes do not overlook the same issue.
For a DAVOND consultation, bring anonymized documents, required fields, and the destination workflow. Together, we can review what AI should prepare, where people retain decisions, and what evidence is needed before launch.