AI Email Classification: Auto-Route Orders, Inquiries & Support

Your shared inbox is a bottleneck. Orders, pricing inquiries, shipping questions, return requests, vendor communications, and spam all land in the same place. Someone on your team -- usually the most experienced person, because it requires judgment -- spends an hour or more each day reading emails, deciding what each one is, and forwarding it to the right person or queue. This is the definition of wasted expertise: using your most capable team members as human routers.

AI email classification eliminates this bottleneck entirely. Within seconds of an email arriving, AI reads the content, determines its intent, extracts relevant data, and routes it to the correct workflow -- no human triage required.

How AI Email Classification Works

The system operates in three stages that happen in real time as each email arrives:

Stage 1: Content Analysis. The AI reads the full email body, subject line, and any attachment metadata. It uses natural language processing to understand the message's intent, not just keyword matching. An email saying "I'd like to place a reorder of what we got last month" is correctly identified as a purchase order even though it contains no order-specific keywords.

Stage 2: Classification. Based on the analysis, the AI assigns one or more categories. Common classifications include: new order, reorder, pricing inquiry, order status check, return/exchange request, complaint, shipping question, general inquiry, and vendor/supplier communication. You define the categories based on your business needs.

Stage 3: Data Extraction and Routing. For each classification, the AI extracts the relevant data and triggers the appropriate workflow. An order email gets its line items extracted and sent to your order processing pipeline. A return request gets the order number and reason extracted and routed to your returns team. A shipping question gets the tracking information looked up and an auto-response generated.

Figure 1: AI classifies incoming emails by intent and routes each to the appropriate workflow and team.

Why Keyword Filters Don't Work

You might be thinking: "I already have email rules that filter by keywords." Here's why that approach fails for business email:

• An email containing "order" might be a new order, an order status question, an order cancellation, or a discussion about ordering process changes. Keywords cannot distinguish these.
• Customers don't use consistent language. One writes "I need to return this," another writes "The product arrived damaged," and a third writes "Can you take these back?" All are return requests, but no single keyword catches all three.
• Context matters. "Invoice" in the subject line could mean a customer is sending their PO, asking about a bill, or complaining about a pricing error. Only understanding the full content reveals the true intent.

AI classification uses the full context of the email -- subject, body, sender history, and even attachment types -- to make its determination. It doesn't match keywords; it understands meaning.

Setting Up Your Classification Categories

The categories you define should map directly to your workflows. Here's a proven starting framework:

• New Order / Reorder: Routes to order processing workflow. AI extracts product details and quantities.
• Order Status Inquiry: Triggers automated lookup of tracking information and generates a response without human intervention.
• Return / Exchange Request: Routes to returns workflow. AI extracts order number, reason, and items to return.
• Pricing / Quote Request: Routes to sales team with extracted product details and customer information.
• Complaint / Escalation: Flags as priority, routes to account manager, and triggers sentiment analysis for trend tracking.
• Shipping Question: Auto-responds with delivery timeframes or tracking links when sufficient data is available.
• Vendor / Supplier Communication: Routes to procurement or AP team based on content.

Building the Classification Workflow

The technical implementation connects your email service to an AI classification step, then branches into category-specific workflows. In Make.com, this looks like:

• Trigger: New email received in your orders@ or info@ inbox.
• AI Module: Send the email subject and body to a GPT or Claude API call with a structured prompt that defines your categories and expected output format.
• Router: Based on the returned classification, branch into the appropriate workflow path.
• Action modules: Each branch executes the category-specific actions -- creating records, sending notifications, generating responses, or updating your CRM.

The prompt engineering is critical. Your classification prompt should include clear category definitions, 2-3 examples per category, and instructions for handling ambiguous cases. Specificity in the prompt translates directly to accuracy in classification. For more on leveraging GPT in automation, see our guide on using GPT for business automation.

Measuring Success

Track these metrics to validate your AI email classification system:

• Classification accuracy: What percentage of emails are correctly categorized? Target 92-97% in the first month, improving to 95-99% with prompt refinement.
• Response time reduction: How much faster are emails being acted on? Most businesses see 60-80% reduction in average first-response time.
• Triage labor savings: How many hours per week has your team recovered from manual email sorting? Even a small team of 3-5 people typically saves 5-10 hours per week collectively.
• Misrouting rate: How often do emails end up in the wrong workflow? This should be under 5% and decreasing.

The compounding benefit of AI email classification is that every minute saved on triage is a minute your team can spend on the actual work each email represents -- processing the order, solving the problem, closing the deal.

Handling Edge Cases

No classification system is perfect, and planning for edge cases is what separates a production system from a demo. Build these safeguards into your workflow:

• Low-confidence fallback: When the AI's confidence is below your threshold, route the email to a human reviewer instead of guessing.
• Multi-intent emails: Some emails contain both an order and a complaint. Configure the system to handle dual classification, triggering both workflows simultaneously.
• Feedback loop: Give your team a simple way to mark misclassified emails. Use these corrections to refine your prompt and improve accuracy over time.

AI email classification is one of the highest-impact, lowest-risk automations you can implement. It touches every incoming communication, saves time across every department, and provides the structured data foundation that powers downstream automation. Start with your busiest inbox, define your categories, and let AI handle the sorting so your team can focus on the doing.