AI-Assisted Data Quality & Exception Workflow

AI-Assisted Data Quality & Exception Workflow Use structured prompts and business rules to turn a repetitive data-cleanup task into a reviewable workflow. Illustrative workflow demonstration using synthetic data and prewritten responses. Human review is explicit. No client results, live-model benchmark, measured ROI or production AI deployment is claimed. Synthetic input: SYN-001 / Acme / 1,200; SYN-001 / Acme / 1,200; SYN-002 / Beta / 800; SYN-003 / Gamma / missing. Prompt specification: propose normalization rules; flag duplicate IDs and missing amounts; preserve original values; never invent missing financial data. Return original value, proposed change, reason and review status. Expected review result: SYN-001 duplicate enters review; SYN-003 remains incomplete; unique populated records total $2,000 only if the duplicate is confirmed to be the same transaction. Human checkpoint: confirm duplicate treatment and source authority before downstream reporting. Retain an exception log and original input. Acceptance criteria: every transformation has a reason; missing values remain flagged; record counts reconcile; rerunning approved rules does not create new changes. Business benefit intended: fewer repeated manual steps and a clearer review queue. This is a workflow specification, not a production integration or measured improvement.