How AI improves automation
AI helps when decisions are not purely rule-based: text classification, data extraction, summarization, intent detection, and suggested replies.
Real-world use cases
- Ticket classification from message content.
- Data extraction from emails (orders, invoices, addresses).
- Conversation summaries and automatic task creation.
- Smart routing to the best agent or team.
Typical architecture
- Trigger (email/ticket/form)
- Pre-processing (clean text, validate)
- AI step (classify/extract/summarize)
- Action (update CRM, create ticket, notify)
- Control (logs + human review for critical cases)
Best practices
- Start with classification, not high-stakes decisions.
- Store outputs and sources for auditability.
- Use confidence thresholds and a human fallback.
FAQ
Does AI replace rules?
Not always. Many teams combine rules for obvious cases and AI for ambiguous ones.