Full automation isn't always the goal. Strategic human checkpoints improve quality, reduce risk, and build trust. This lesson covers when and how to insert human review into AI workflows.
| Risk |
Human Review Prevents |
| AI hallucinations |
Factually incorrect information reaching customers |
| Brand damage |
Off-tone or inappropriate content being published |
| Legal liability |
Incorrect advice or promises being made |
| Customer frustration |
Automated responses that miss the point |
| Data errors |
Wrong records being updated or created |
Low Stakes + High Volume = Full Automation
Example: Categorizing internal support tickets
High Stakes + Low Volume = Human Required
Example: Customer contract modifications
High Stakes + High Volume = AI Draft + Human Approval
Example: Customer support responses
WORKFLOW: Customer Refund Request
─────────────────────────────────
TRIGGER: Customer requests refund via email
↓
AI STEP: Analyze request
- Extract: Order ID, reason, amount
- Assess: Policy compliance, history
- Recommend: Approve/deny/partial
↓
ACTION: Create approval task
Assignee: Support manager
Context: AI analysis + recommendation
Options: [Approve] [Deny] [Modify] [Escalate]
↓
WAIT: For human decision (max 24 hours)
↓
CONDITIONAL: Based on decision
- Approve → Process refund automatically
- Deny → Send denial template with reason
- Modify → Allow manager to adjust amount
- Escalate → Route to senior team
WORKFLOW: AI-Drafted Customer Response
──────────────────────────────────────
TRIGGER: New support ticket created
↓
AI STEP: Draft response
Context: Customer message, order history, KB articles
Output: Professional, helpful response draft
↓
ACTION: Send to agent for review
Channel: Email or support tool queue
Display: Customer message + AI draft
Options: [Send as-is] [Edit and send] [Discard]
↓
WAIT: For agent action
↓
LOG: Track edit frequency for AI improvement
WORKFLOW: Lead Scoring with Escalation
──────────────────────────────────────
TRIGGER: New form submission
↓
AI STEP: Score lead (1-100)
↓
CONDITIONAL:
│
├── Score 80-100: Auto-route to sales (high confidence)
│
├── Score 40-79: Auto-nurture sequence (medium)
│
├── Score 0-39: Archive (low priority)
│
└── Score = "uncertain": → HUMAN REVIEW
AI couldn't confidently score
Send to sales manager for manual review
WORKFLOW: Automated with Sampling
─────────────────────────────────
TRIGGER: Each processed item
↓
AI STEP: Process automatically
↓
ACTION: Complete task
↓
CONDITIONAL: Random sample (5% of tasks)
↓
ACTION: Add to weekly audit queue
↓
[Weekly] Human reviews sample for quality
↓
Feedback loop: Adjust AI prompts based on findings
| Method |
Best For |
| Email approval |
Simple yes/no decisions |
| Slack approval |
Team visibility, quick action |
| Digest/batch |
Multiple items reviewed together |
| Custom webhook |
Integration with internal tools |
| Method |
Best For |
| Scenario pauses |
Wait for external trigger |
| Webhook listeners |
Custom approval interfaces |
| Email/Slack |
Standard notification channels |
| Google Sheets queue |
Bulk review workflows |
| Method |
Best For |
| Wait node |
Pause until webhook received |
| Form trigger |
Custom approval forms |
| Manual execution |
Review before continuing |
| External database |
Approval queue in your systems |
❌ POOR APPROVAL REQUEST:
"Approve this response?"
[Response text]
[Approve] [Deny]
✅ GOOD APPROVAL REQUEST:
────────────────────────
Customer: John Smith (Premium tier, 3 years)
Request: Refund for order #12345 ($89)
Reason: "Product didn't match description"
History: 2 previous refunds (both approved, legitimate)
AI ANALYSIS:
- Policy check: Within 30-day window ✓
- Product: Matches common complaint pattern
- Recommendation: APPROVE (high confidence)
AI-DRAFTED RESPONSE:
[Response preview]
ACTIONS:
[✓ Approve Refund + Send Response]
[✗ Deny - Select Reason]
[✎ Edit Response Before Sending]
[↗ Escalate to Manager]
────────────────────────
| Element |
Purpose |
| Summary at top |
Quick understanding |
| AI recommendation |
Guidance, not replacement |
| Confidence level |
When to trust AI |
| One-click actions |
Reduce friction |
| Context link |
Deep dive if needed |
WORKFLOW: Approval with Escalation
──────────────────────────────────
ACTION: Request approval from Manager A
↓
WAIT: Max 4 hours
↓
IF: No response
↓
ACTION: Escalate to Manager B
↓
WAIT: Max 4 hours
↓
IF: Still no response
↓
ACTION: Alert ops team + apply default action
WORKFLOW: Content Publication
─────────────────────────────
AI STEP: Generate blog post
↓
HUMAN REVIEW: Editor
↓
IF: Rejected
│
├── Rejection reason: "Factual error"
│ → Return to AI with correction instructions
│ → Re-generate with fixes
│ → Re-submit for approval
│
├── Rejection reason: "Off brand"
│ → Return with brand guidelines
│ → Re-generate with style adjustments
│
└── Rejection reason: "Topic not approved"
→ Notify content manager
→ Archive draft
→ END
WORKFLOW: Smart Email Response (Human-in-Loop)
──────────────────────────────────────────────
TRIGGER: New customer email received
↓
AI STEP 1: Classify and analyze
Outputs:
- category: complaint/question/feedback/urgent
- sentiment: positive/neutral/negative
- complexity: simple/moderate/complex
↓
CONDITIONAL: Routing decision
│
├── IF urgent OR complaint + negative:
│ → Skip AI draft
│ → Route to senior agent immediately
│ → Alert via Slack
│
├── IF simple question + neutral/positive:
│ → AI drafts response
│ → Agent reviews (15-min SLA)
│ → One-click send or quick edit
│
└── IF complex OR moderate+ anything:
→ AI drafts response with notes
→ Agent reviews (1-hour SLA)
→ Edit encouraged before sending
↓
AI STEP 2: Draft response
Include:
- Acknowledgment of issue
- Relevant KB article links
- Clear next steps
- Appropriate tone for sentiment
↓
ACTION: Queue for agent review
Display:
- Original email
- AI classification
- Draft response (editable)
- Customer history summary
- Suggested KB articles
↓
WAIT: For agent action
Timeout: Based on SLA
Escalation: To team lead if missed
↓
LOG: Track metrics
- Edit rate (how often agents change drafts)
- Time to respond
- Customer satisfaction after
- Which categories need most editing
| Metric |
What It Tells You |
| Edit rate |
How often humans change AI output (target: <30%) |
| Approval time |
Bottleneck identification |
| Override rate |
How often humans reject AI recommendation |
| Error rate (post-approval) |
Human mistakes vs. AI mistakes |
| Throughput |
Items processed per hour/day |
1. MEASURE: Track edit patterns
↓
2. ANALYZE: What types of edits are common?
↓
3. IMPROVE: Update AI prompts to address patterns
↓
4. TEST: Run new prompts on historical data
↓
5. DEPLOY: Update workflow
↓
6. REPEAT: Weekly/monthly review cycles
Goal: Reduce edit rate over time while maintaining quality. If AI drafts are rarely changed, consider reducing review requirements for those categories.
Next module: Learn how to scale your automations with proper governance and cost management.
:::
