AI lead scoring: how it works and how to set it up in n8n

Lead scoring used to mean CRM points rules maintained by a sales-ops team. AI changed it: one model call can read a whole inquiry and return a qualified judgment. Here is how scoring works, why it beats rules, and how to run it in n8n in an afternoon.

What lead scoring actually is

Lead scoring assigns a number to every inquiry that measures fit: how likely this person is to become a paying customer you want. Traditional scoring sums points for signals — company size +3, budget stated +5, competitor mentioned −2. The rules are brittle, need maintenance, and miss everything the rule-writer did not think of.

AI scoring replaces the rulebook with judgment. You send the inquiry text plus a short instruction — "score fit 0–10 for a freelancer selling automation services, reply with score and a one-line summary" — and the model returns a number and a reason. The scoring criteria live in a prompt you can edit in plain language, not in a rule engine.

Why 0–10 with a threshold beats segmenting

Most teams do not need sophisticated lead tiers. They need one decision: reply now, or archive. A 0–10 score with a single threshold (commonly 7) makes that decision mechanical: 7 and above hits your inbox with the summary attached, everything else is stored without interrupting you. You can tune the threshold later without touching the workflow logic — and if the model over-scores, the prompt is the only thing to adjust.

The n8n implementation, step by step

Three nodes carry the whole feature:

1. Webhook — POST /lead accepts the inquiry as JSON: name, email, company, message.
2. HTTP Request — posts the lead to an OpenAI-compatible chat completions endpoint. The instruction asks for a strict reply format, e.g. score|summary, so the next node can parse it without code. The API key lives in an environment variable, never in the workflow.
3. IF — reads the score, routes 7+ to a Send Email alert and everything else to an archive branch.

Testing it is one curl call:

curl -X POST https://your-n8n/webhook/lead \
  -H "Content-Type: application/json" \
  -d '{"name":"Jane","email":"[email protected]","company":"Acme","message":"Need lead automation for 3 brands"}'

Seconds later, a qualified inquiry is in your inbox with the AI's one-line summary in the subject — or it was quietly archived. The cost per scored lead is a fraction of a cent on a cheap model.

Where AI scoring shines, and where it needs eyes

AI scoring is strongest on free-text signals — the actual words of the inquiry — where rules fail entirely. It is weaker on things the model cannot see: your margins, your pipeline state, whether a name matches a dream account. That is why the threshold, not the model, should be the guardrail you tune. Start at 7, watch a week of scored leads, move it if your inbox is too noisy or too quiet.

If you want the whole feature ready-made — webhook, AI call, filter, alert email, archive branch, tested and importable — it is workflow #1 in the pack below.