Build an AI-Powered Lead Qualification Workflow with n8n

Lead generation becomes difficult when enquiries arrive from multiple channels and someone has to manually read, qualify, copy, and assign every lead. A simple automation can remove most of this repetitive work. In this tutorial, we'll build an AI-powered lead qualification workflow with n8n. The workflow will: Receive a lead through a webhook Extract useful information from the enquiry Calculate a qualification score Decide whether the lead is qualified Prepare the lead for CRM/sales processing Return a structured response The architecture looks like this: Lead Source ↓ Webhook ↓ AI / Lead Analysis ↓ Information Extraction ↓ Lead Scoring ↓ Qualified? / \ Yes No ↓ ↓ Sales Nurture The advantage of this approach is that AI handles the understanding, while n8n handles the business logic. What we'll build Imagine a customer sends: Hi, I'm looking for an AI chatbot for my real estate company in Dubai. We have around 500 enquiries per month and need something urgently. Our budget is around $2,000. We want the automation to turn that unstructured message into something like: { "name": "Unknown", "company": "Unknown", "industry": "Real Estate", "service": "AI Chatbot", "location": "Dubai", "budget": 2000, "urgency": "High", "leadScore": 90, "qualified": true } The sales team doesn't need to manually interpret the original message. Create the n8n workflow Create a new workflow in n8n. Add a Webhook node. Configure it as: HTTP Method: POST Path: lead-qualification Response Mode: Last Node Your webhook endpoint will look similar to: https://your-n8n-domain.com/webhook/lead-qualification For local development, you can use the test URL generated by n8n. Send a test lead You can test the webhook with cURL. curl -X POST "https://your-n8n-domain.com/webhook/lead-qualification" \ -H "Content-Type: application/json" \ -d '{ "name": "John Smith", "company": "Example Property Group", "message": "We are a real estate company in Dubai looking for an AI chatbot. Our budget is around $2000 and we need it urgently." }' The Webhook node will now receive the lead. Extract the lead information Next, add a Code node. Rename it: Extract Lead Data For this example, we'll use simple JavaScript to prepare the incoming data. const lead = $json; const name = lead.name || ""; const company = lead.company || ""; const message = lead.message || ""; return [ { json: { name, company, message, receivedAt: new Date().toISOString() } } ]; This gives the rest of the workflow a predictable structure. Add AI-powered analysis Now we can use an AI model to understand the customer's message. You can use an OpenAI node or another LLM integration available in your n8n setup. The important part is the prompt. Use something similar to: You are a lead qualification assistant. Analyze the customer enquiry below. Extract: industry service location budget urgency buying_intent Return ONLY valid JSON. Customer message: {{ $json.message }} A possible AI response would be: { "industry": "Real Estate", "service": "AI Chatbot", "location": "Dubai", "budget": 2000, "urgency": "High", "buying_intent": "High" } For production systems, validate the AI response before allowing it to trigger business-critical actions. Calculate the lead score Now we move the deterministic business logic into n8n. Add another Code node called: Calculate Lead Score Example: const lead = $json; let score = 0; if (lead.service) { score += 30; } if (lead.budget) { score += 20; } if (lead.urgency === "High") { score += 20; } if (lead.location) { score += 20; } if (lead.industry) { score += 10; } const qualified = score >= 60; return [ { json: { ...lead, leadScore: score, qualified } } ]; Now the workflow has a simple rule: Score >= 60 → Qualified Score < 60 → Nurture Add an IF node Add an IF node. Configure the condition: Value 1: {{ $json.leadScore }} Operation: larger or equal Value 2: 60 The workflow now splits into two paths. Lead Score | +------+------+ | | >= 60 < 60 | | Qualified Nurture Handle qualified leads For qualified leads, you could connect the workflow to your CRM. For example: Qualified Lead ↓ CRM ↓ Sales Notification ↓ Calendar / Follow-Up You could create a CRM record containing: { "name": "John Smith", "company": "Example Property Group", "industry": "Real Estate", "service": "AI Chatbot", "location": "Dubai", "leadScore": 90, "status": "Qualified" } You can then notify the sales team through email, Slack, Microsoft Teams, WhatsApp or another communication channel. Handle unqualified leads Not every lead should immediately go to sales. For lower-scoring leads, create a nurture path. Unqualified Lead ↓ CRM ↓ Nurture Sequence ↓ Follow-Up For example, the lead could receive useful information first and be followed up later. This prevents salespeople from spending their time manually chasing every enquiry. Return a response Finally, return the result to the system that sent the lead. For example: { "success": true, "leadScore": 90, "qualified": true, "message": "Lead successfully qualified" } Now another application can immediately know whether the lead was accepted. Complete workflow logic The final workflow can look like this: ┌──────────────────┐ │ Lead Source │ │ Website/WhatsApp │ │ Form/API │ └────────┬─────────┘ ↓ ┌──────────────────┐ │ Webhook │ └────────┬─────────┘ ↓ ┌──────────────────┐ │ Extract Data │ └────────┬─────────┘ ↓ ┌──────────────────┐ │ AI Analysis │ └────────┬─────────┘ ↓ ┌──────────────────┐ │ Lead Scoring │ └────────┬─────────┘ ↓ ┌────┴────┐ ↓ ↓ Score ≥60 Score