AI Support Deflection (illustrative 7–14-day plan)
Deploy an AI knowledge base and chatbot with clear escalation, then measure answer quality, deflection, handling time, and support load against a baseline. Time: Illustrative scenario: 7 days for an MVP and 14 days for an initial knowledge-base iteration; adjust for source quality, policy review, volume, and QA..
By InnovaAI Research
How do you implement it?
AI Support Deflection (illustrative 7–14-day plan)
Deploy an AI knowledge base and chatbot with clear escalation, then measure answer quality, deflection, handling time, and support load against a baseline.
- Existing FAQs/policies or ability to create them
- Support ticketing/helpdesk access
- Knowledge base tool
- Escalation process to humans
- 1.Collect top 30 repetitive questions
- 2.Define deflection metric: tickets avoided
- 1.Create knowledge base structure
- 2.Write policy-aligned answers
- 1.Deploy chatbot grounded in KB
- 2.Add 'handover to human' triggers
- 1.Test with real conversations
- 2.Fix unclear answers and add missing KB entries
- 1.Install reporting: deflection rate + escalations
- 2.Train support team on escalation SOP
Use deflected tickets × an agreed cost-per-ticket input as an illustrative gross-savings scenario, then account for platform, maintenance, review, and escalation costs before discussing ROI.
- Knowledge base starter set
- Chatbot deployment + escalation rules
- Support team SOP
- Deflection reporting snapshot
- Continuous improvement checklist
Users get correct answers from KB chatbot, complex issues escalate to humans, and deflection is measured weekly.