AI lead qualification reads an incoming enquiry, adds what it can find about the company, compares it to criteria you've defined and then scores and routes the lead. It's strongest on free text, where form rules fall short. It can't invent your criteria, and it should never quietly throw leads away.
What are the steps?
- Capture. A form, mail or chat comes in and is stored in your CRM right away, before anything else happens.
- Read. AI pulls out what's being asked: the need, the size, the timing, the budget if mentioned.
- Enrich. It adds details about the company from approved sources.
- Compare. It holds the result against your written criteria.
- Score and explain. It gives a category with the reasons in one or two sentences.
- Route. Fast to the right salesperson, into nurture, or to a review queue.
- Learn. Outcomes from sales are compared to the scores, and the criteria get adjusted.
Rules or AI: which do you need?
Often you need rules first. If your form asks for country, company size and service, plain logic can score that for free and you can explain each outcome. AI earns its place in the steps rules can't do.
| Situation | Use |
|---|---|
| Structured form fields | Rules |
| Blocking obvious spam or disposable addresses | Rules |
| Open text: "we're looking for someone who can…" | AI |
| Enquiries arriving by mail, in any format | AI |
| Judging whether a company fits, from its website | AI, with a person checking |
| Sending to the right owner | Rules, based on the AI's output |
Read more on that split in AI agent vs automation.
What do you have to define first?
Your criteria, written down, agreed by sales and marketing. This is the real work, and the warning sign is a vendor who says you can skip it. Before you build, answer these:
- Who is a good fit, and who definitely isn't? Use real examples of both.
- Which signals show someone's ready to buy, rather than just curious?
- What does each category trigger, and how fast?
- What do you do with a good company that asks at the wrong time?
- What would make sales say a lead was wasted?
Take twenty past leads, score them by hand and see if your team agrees. If they don't, AI won't fix it. It'll just disagree with some of them more quickly.
How do you avoid losing good leads?
This is the main risk, and it's silent. A bad lead reaching sales gets noticed. A good lead wrongly rejected never does. So set these rules:
- Never delete. Everything stays in the CRM, whatever its score.
- Answer everyone. A low score still gets a polite reply, so nobody is left waiting.
- Sample the rejects. Have someone read a batch from the lowest category regularly.
- Keep the reason visible. A score without its reason can't be checked.
- Watch for bias in your criteria. Scoring on past wins can favour whoever you already happened to sell to.
One legal note. GDPR Article 22 gives people the right not to be subject to solely automated decisions with legal or similarly significant effects. If your scoring decides access to something that matters to people, such as credit, get advice and keep a real person in the loop.
What does the first version look like?
Keep it advisory. Let AI score and explain while sales keeps its current process, and compare the two for a while. Only then let it route. Speed is often the quick win, as the right person sees the right enquiry sooner.
Lead handling is a typical Automation Sprint, from €2,950. When it involves AI reasoning across several systems, it becomes a Business Agent, from €7,950. Prices exclude VAT. Also see what you can automate in the CRM around it.
