When AI gives wrong answers in customer service, it is necessary to immediately stop the harm, apologize and correct the customer, correct errors in the system, and then add precautions. The 6-step process helps handle errors quickly, with little damage and to prevent recurrence. Zenify noted the AI was 98.4% accurate, but the remaining 1.6% needed processing.
TL;DR
- AI is wrong, it needs process, not denial.
- 6 steps: detection → treatment → correction → prevention.
- Customer gave wrong information: sorry, corrected immediately.
- All errors are recorded for AI to learn.
- Browse at risk point to reduce harm.
Why can AI answer wrongly?
Cause | Example
Knowledge base is lacking | New policy not updated yet
Misunderstanding requirements | Misunderstanding the customer's intention
Conflicting information | Various sources
Out of scope question | Ask AI without data
Language, region | Wrong local context
6-step process for handling wrong AI
Step 1: Detection
Auto QA scores 100% of conversations, warns of poor scores.
Guests complaining, staff discovered when transferring (see Transfer to person).
Step 2: Stop the harm
Temporarily block AI errors (for example, close AI refund function).
Transfer all such conversations to that person.
Step 3: Apologize and correct the guest
Send an apology + correct information immediately to affected customers.
Customers remember AI errors more than they remember apologies, so be quick and honest.
Bước 4: Sửa nguồn lỗi
Update knowledge base, edit scripts, edit data (see Knowledge base chatbot).
Step 5: Record to learn
Include error cases in the training sample.
AI learns from human correction (see Human-in-the-loop).
Step 6: Prevention
Add reviewers at risk points.
Add automatic checking before AI answers sensitive questions (see AI Security).
Principles when AI is wrong
Don't hide: Hiding AI errors makes customers lose more trust than AI errors.
Response within 24 hours: customer waits too long with AI error = twice as bad.
Responsible person: AI is wrong, the manager handles it and takes responsibility, don't blame AI.
Measure error rate: Monitor each week, target gradually decreasing.