Business technology
AI in bookkeeping: prepare the work, but who approves it?
Define AI permissions, separate suggestions from committed records and build a review queue with evidence and accountability.
Before applying AI to accounting documents, ask what happens when it reads something incorrectly, who sees the error and who may correct it—not only how quickly it reads.
The following is Linda’s proposed design approach, not a claim that every Linda application already implements every capability or an endorsement of a particular model.
Make review evidence-based
Show the original, extracted fields, differences and reasons for suggestions together. Request evidence when an image is unclear or incomplete instead of treating a guessed value as fact.
Retain who reviewed the record, when, what changed and why. Keep the original suggestion distinct from approved values so later changes do not make the system appear correct from the beginning.
Define risk and measurement before scaling
NIST’s AI RMF is a voluntary framework for AI trustworthiness and risk management, organised around Govern, Map, Measure and Manage. Referring to it does not mean that NIST has certified a system.
Linda’s proposed application is to assign ownership, identify consequences, choose tests and define stops or human escalation. It is not an accounting-approval formula prescribed by NIST.
References: [1]
Do not rely on confidence alone
Changed supplier details, unfamiliar documents, unusual amounts, duplicates and contradictory pages should trigger further review according to impact, even when an interface reports high confidence.
Consider an illustrative misread of 8 as 3. A proposed value checked against the original remains a preparation error. If that proposal immediately triggers payment, the same error can have a financial consequence. Separate authority and states at design time.
Test known answers and preserve failure cases
Begin with appropriately authorised or anonymised examples. Include angled images, currencies, amendments, duplicates and unreadable text. Assess data-handling terms before sending confidential client information to a service.
Measure field accuracy, corrections, review time and error categories. Keep previous failures as regression cases when changing models or instructions. Verify performance before increasing authority or volume.
A practical starting checklist
- Specify permitted and prohibited AI actions.
- Separate proposals from approved records.
- Provide source evidence and decision history.
- Assign owners to an exception queue.
- Test accuracy and data-handling arrangements before expanding.
Apply it to your business
The objective is not to remove people from every step. It is to focus their time on important review and decisions without losing control or evidence.
References
- National Institute of Standards and Technology. (n.d.). AI Risk Management Framework. Retrieved October 1, 2026.
Numbered references support the attributed statements. Scenarios and recommendations are Linda’s examples, not verified client outcomes.
This article provides process-design guidance and illustrative examples, not an accounting, tax or legal determination or certification of every software module. Apply it with regard to your business, permissions and actual system scope.