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As of 13 August 2026, AI can score leads before a sales demo.
Most people should hand this to a purpose-built tool.
Can you do it?
5 minutesto a draft.
30 minutesto something you’d act on.
Cost, all in£0
Skill neededchat-fluent
Who has to check ita colleague
What the alternative costsNo priced alternative is supplied in the available commercial data.
If this goes wrong: your team spends demo time on weak leads or ignores a good lead, and the error may only become clear after a missed opportunity.
What to actually do
Use a tool built for this
The route this page recommends
Do it yourself
Second choiceA chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.
How to actually do it
- Open your CRM export or lead list and remove unnecessary personal data, keeping a stable lead ID and the fields relevant to demo priority.
- Write the scoring criteria your sales team already uses, such as problem fit, buying timeline, decision-maker involvement, engagement and budget evidence, and assign weights that add up to the full scoring range.
- Paste the prompt into a chatbot, replace the bracketed criteria and lead-data sections, and run the scoring without allowing the model to fill missing fields.
- Check a sample of the returned rows against the CRM record, confirming that every reason and score uses the correct lead data and follows your rubric.
- Ask a salesperson to inspect the flagged low-confidence leads and any high-priority lead with missing or contradictory evidence.
- Send the checked ranking to the sales team as a prioritisation aid, not as an automatic rejection or booking rule.
- After demos take place, compare the scores with attendance, qualified opportunities and outcomes, then adjust the rubric rather than silently trusting the next ranking.
Prompt
Score the leads below for priority before a sales demo. Use only the information supplied here and do not invent facts. Give each lead a score from 0 to 100, a priority band of high, medium or low, and a short explanation tied to specific evidence. Use this rubric: [PASTE YOUR SCORING CRITERIA, INCLUDING WEIGHTS]. If a field is missing, mark it as unknown rather than guessing. Separate evidence from assumptions. Flag leads where the score is unreliable because important information is missing, contradictory or ambiguous. Do not treat company size, location, job title, name, age, ethnicity, gender or any other personal characteristic as evidence of buying intent unless I explicitly justify a lawful and relevant business reason. Return a table with these columns: lead ID, score, priority band, evidence, missing information, recommended next action, confidence. Then list the scoring rules you applied and the three leads whose scores should be checked by a salesperson first. Lead data: [PASTE ANonymised CRM EXPORT OR LEAD LIST HERE].
Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.
Hand it to a person
The distant thirdA person who owns the outcome does this end to end, worth it when the failure is dear.
What it gets wrong
- AI cannot know whether a prospect's stated interest is genuine, politically important or merely exploratory.
- It cannot replace a salesperson's context about account history, internal relationships or a current commercial conversation.
- It produces a precise-looking score even when the underlying CRM data is incomplete or stale unless you force it to expose uncertainty.
- It cannot validate whether your scoring criteria unfairly exclude leads or reward convenient but weak signals.
- It cannot take responsibility for the revenue lost when the ranking is wrong.
Even on a YES, the friction has a name: judgement under ambiguity, context depth and stakes of error.
How we scored this
Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.
| Axis | Score (0–2) |
|---|---|
| Output | 2 |
| Inputs | 2 |
| Verification | 1 |
| Liability | 1 |
| Effort delta | 2 |
| Total | 8 / 10 |
The methodology and its thresholds are published in full.
FAQ
- Can ChatGPT score my sales leads?
- Yes. Give it a defined rubric and a clean, relevant lead list, and it can rank leads, explain the evidence and identify missing information. Check the results against the CRM and have a salesperson review uncertain or high-impact cases.
- What information does AI need to score a lead?
- It needs the fields that genuinely relate to your buying process, such as problem fit, timing, engagement, authority and budget evidence. It also needs your scoring rules, because a model cannot infer what your team means by a good lead reliably.
- Is AI lead scoring accurate?
- It can apply your rules consistently, but consistency is not the same as accuracy. Test the ranking against real demo and opportunity outcomes, and treat missing, stale or ambiguous CRM data as a reason to lower confidence.
- Should I let AI decide which leads get a sales demo?
- Use the score to prioritise review, not to make an automatic yes or no decision. A salesperson should check the evidence before a lead is excluded, especially where the data is incomplete or the potential opportunity is significant.
Nearby answers
Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.
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