How do you implement AI-powered email automation workflows?

AI-powered email automation combines NLP classification with workflow orchestration. Pipeline: email ingestion (IMAP, Graph API, Gmail API) captures incoming emails, AI classifier determines intent (support request, inquiry, complaint, purchase order, spam), entity extraction pulls key data (order numbers, product names, amounts, dates), and routing logic directs to appropriate workflow. AI implementation: fine-tune a BERT classifier on labeled emails for intent classification (typically 500+ examples per intent class), use NER models for entity extraction, and implement sentiment analysis for priority scoring. Workflow automation per intent type: support requests auto-create tickets with extracted details, purchase orders trigger procurement workflows, complaints escalate to management with sentiment context, and routine inquiries trigger auto-response with personalized templates. Auto-response: generate contextual responses using RAG against a knowledge base, implement confidence thresholds (auto-send above 0.95, queue for review below), and maintain tone consistency using prompt engineering. Safeguards: never auto-respond to emails involving legal, financial commitments, or sensitive topics; implement daily volume limits on auto-responses; and provide easy opt-out mechanisms. Metrics: response time reduction, classification accuracy, customer satisfaction scores, and escalation rates.