AI Lead Qualification Workflow
AI should sort the queue. People should decide what happens next.
The case study
An illustrative AI lead qualification workflow for a small B2B team that needs to separate buying intent from incomplete or early-stage enquiries without hiding the reasoning behind a score.
The situation
The team was receiving leads from several pages and channels. Every enquiry landed in the same queue, so the person replying had to reconstruct the context before deciding whether to respond, ask a question, or wait.
The useful role for AI is not to make the final sales decision. It is to read the available context, surface the signals, explain the suggested lane, and leave a human with a clear next action.
What they did
- 1Collect the page, source, request, company context and timing with each enquiry so the model sees the same evidence a human would need.
- 2Ask the model to extract intent signals and return a short reason for its suggested lane instead of an unexplained score.
- 3Route the enquiry into ready to talk, needs context, or nurture, with a different next action for each lane.
- 4Keep a human approval point for high-value, ambiguous or sensitive enquiries, then feed the decision back into the weekly review.
The model, one decision loop
Before / after
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