Design review around risk: let people verify uncertain, consequential, or low-confidence results before action.
- human in the loop
- AI workflow
- AI governance
- human review
Route exceptions, not every easy case
Use clear rules to identify outputs that need attention, such as missing source evidence or high-impact changes. Do not rely on a model's self-reported confidence unless it has been validated.
AI & AutomationAI & Automation
Thoughtful decisions compound over time.
Practical product work brings technical choices back to the people and workflows they are meant to serve.
Make verification fast
Show the source text, proposed result, and a direct way to correct or reject it. Reviewers need enough context to make a decision without repeating the whole task.
Learn from corrections
Record reviewer changes with appropriate access and retention controls, then analyse recurring error types. This evidence can improve prompts, retrieval, process rules, or the decision to automate at all.
Practical application
For document extraction, route records with missing identifiers or totals that fail reconciliation to a reviewer. Show the original source next to extracted fields, capture corrections, and review error patterns weekly before changing the automation rules.
