Quality depends on manual checking
AI can add a second check without becoming the final authority. The useful pattern is to compare work with an approved standard, flag a specific exception, and direct a person to the part that needs attention.
Signs this is the problem
- Checks vary by employee or workload.
- Defects are found after delivery.
- The creator is also the only reviewer.
- Reviewers spend time on low-risk items.
- There is no record of why an item passed or failed.
Ways businesses address it
- 1Check required fields and supporting evidence.
- 2Compare an output with a policy or checklist.
- 3Detect anomalies and prioritise review.
- 4Provide a source-linked reason for the flag.
- 5Record the human decision and correction.
Before borrowing the pattern
Define the consequence of false positives and false negatives. High-consequence decisions need independent rules, qualified review, and a safe fallback.
Relevant cases
Quality depends on manual checking
1 case
Drafting support replies from shared knowledge with a fact-check before sending
Problem
Support quality depended on the specialised knowledge of individual employees.
Approach
AI prepared a response from the shared knowledge base, and an employee checked the facts before sending it.
Reported outcome
The vendor-published source reports less manual work but gives no quantified time, quality, or cost result.
Vendor-published case · Salesforce
Found a relevant pattern?
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Define the trigger, data, decision boundary, owner, and measure before choosing a tool.