AI excels at answering predictable, fact-based questions: order status, pricing, how-to instructions, policy explanations, and account information. The key is identifying your high-volume, low-complexity queries and building excellent AI responses for those specific categories.
Key Takeaways:
- Most support volume is predictable and repetitive
- Identify your top 20 questions for maximum impact
- Quality of AI answers matters more than coverage
- Build escape routes for edge cases
- Continuously expand based on actual queries
Playbook
Analyze support data to find your top 50 questions
Categorize by AI suitability (routine vs. complex)
Build comprehensive answers for high-volume queries
Test AI responses with real customers
Monitor and improve based on resolution success
Common Pitfalls
- Trying to automate everything at once
- Poor quality answers that frustrate customers
- No path to human when AI fails
- Ignoring queries that fall outside AI scope
Metrics to Track
AI resolution rate
First-contact resolution
Customer satisfaction with AI answers
Escalation rate from AI
FAQ
What questions should AI NOT try to answer?
Avoid automating: complaints, complex multi-part issues, emotionally charged situations, edge cases, and anything requiring judgment or negotiation. These need human touch.
Related Reading
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