Enterprise AI & Automation / Sample v1.0, synthetic fields

AI document exception evaluation sheet

Decide whether an AI extraction step is reliable enough to reduce manual review of document exceptions, and where a person must still approve.

Sample template. Fictional fields. Not a client record or result.

Workflow definition

Field	Sample entry
Workflow	Supplier invoices that fail purchase order matching (fictional)
Owner	Accounts payable lead (role, not a person)
Baseline volume	To be measured over an agreed period
Consequential action	Posting or payment release, always approved by a person

Field	Sample entry
Test set	Synthetic or authorized documents, labeled by a reviewer
Fields measured	Supplier, amount, PO reference, line items
Abstention rule	Model must flag low confidence instead of guessing
Acceptance criteria	Agreed with the owner before testing starts

Field	Sample entry
Result summary	Recorded after testing; no result is shown in this sample
Human review points	Low confidence fields, amount mismatches, new suppliers
Recommendation	Pilot / fix process first / stop

A test set is not production. Results must be rechecked on live, authorized data.
This sheet does not establish regulatory compliance.

What is an AI workflow evaluation?: A structured test of whether an AI step performs well enough on a defined workflow, measured against criteria agreed with the workflow owner before testing begins.
Why define acceptance criteria first?: Criteria written after seeing results tend to fit the results. Agreeing them first keeps the decision honest.
What is abstention?: The model flags that it is not confident instead of guessing. Abstained items go to a person, which is often safer than a confident wrong answer.
Does a good evaluation mean the AI can act alone?: No. Consequential actions such as payments remain approved by a person. Evaluation decides where AI can assist, not whether oversight is removed.

Free to use and adapt. Bridgelytic can help apply it to your situation through the contact form.