AI Output Needs Too Much Checking
When reviewing and correcting AI output takes as long as doing the work, the workflow may be moving effort rather than saving it.
Measure the whole task
Include preparation, prompting, retrieval, checking, corrections and escalation. Compare equivalent cases with the existing process rather than counting only generation time.
Use review rules tied to consequences
Separate low-impact drafts from actions involving money, customers or system changes. Define what evidence a reviewer must see, what they can approve and which uncertainties require escalation. A model confidence score alone is not sufficient evidence.
Reduce avoidable review work
Use constrained outputs, approved source material and clear acceptance criteria. Route incomplete or contradictory inputs to a person instead of generating a plausible answer that someone must unravel.
What to prepare for a review
Bring a short workflow description, representative examples, available evidence and the outcome you expected. Start with anonymised examples; agree a secure method before sharing sensitive records.
Get help with this workflow
Explore AI Workflow Redesign for a scoped independent review.
Measure the full task before changing the model
Separate drafting time, review time, correction time and exception handling. Compare the same task categories against the manual baseline and record rejected outputs as well as successful ones.
Keep examples of the original input, the AI output and the staff correction. These help distinguish missing information from an unsuitable task or unclear acceptance standard.
See the worked diagnostic example, then describe your workflow for a scoped review.
Check the actual cost of human correction
A model can suggest ways to reduce checking, but only case-level evidence can show who corrects outputs, what that effort costs and whether controls are working. A workflow review uses representative examples to map the correction loop.
Tell us where AI is getting stuck
Describe the workflow, what you expected and what is happening instead. We will clarify the scope and information needed for a review.
Discuss your AI workflowMeasure the full task before changing the model
Separate drafting time, review time, correction time and exception handling. Compare the same task categories against the manual baseline and record rejected outputs as well as successful ones.
Keep examples of the original input, the AI output and the staff correction. These help distinguish missing information from an unsuitable task or unclear acceptance standard.
See the worked diagnostic example, then describe your workflow for a scoped review.
Need help with an AI workflow already in use?
If AI output needs too much correction, a pilot has stalled or operating claims are disputed, start with one workflow and its available evidence. Our AI workflow diagnostic provides written findings, a findings meeting and practical next tests. Your team implements and validates the changes.