Use AI to review search terms without losing control

A search term export is a useful place to start when a B2B campaign is buying the wrong traffic. An AI assistant can help organize the review, provided you give it the offer, the target buyer and a way to flag ambiguous cases. It can sort hundreds of rows faster than a person can. It cannot know which leads your sales team accepts, so the decisions stay with you. This guide sets out a repeatable workflow with a clear input, a fixed output and a human approval step.

Decide what the assistant is for

Use it to classify terms, explain its reasoning and draft a list of proposed actions. Do not use it to change the account by itself. The value is speed on the first pass, and the control comes from reviewing every proposal against what you know about the business.

Prepare a short input

List the product, audience, markets and terms you need to protect. Then add the search terms and aggregate measures like clicks and spend. Include the reporting period and currency so the output has a clear scope. A template like this works:

Offer: [what you sell, in one or two sentences]
Target buyer: [role, company type, markets]
Protect these themes: [terms or meanings that must stay eligible]
Not our buyer: [job seekers, consumers, students, and so on]
Period and currency: [dates, currency]
Search terms (term, clicks, spend, leads, qualified leads):
[paste rows]

Keep the data aggregate. Remove personal information, customer names and anything your organization treats as confidential. Search terms can occasionally contain names, emails or phone numbers that people typed into a search box, so scan the export before you share it, and follow your organization’s policy on which tools may receive account data.

Ask for a review queue

Use four labels: high intent, research, irrelevant and unclear. Ask for an explanation, a confidence level and a suggested next action for each term. When the assistant proposes an exclusion, ask for any relevant meanings or offers it could accidentally block.

Ask for the output as a table with the same columns every time: term, label, reason, confidence, suggested action and risk of blocking good traffic. A fixed format makes the output easy to review and compare from week to week.

Classify each search term as high intent, research, irrelevant or unclear.
For each term return: label, one-sentence reason, confidence (low, medium, high),
suggested action, and any legitimate meaning that an exclusion could block.
Do not invent data. If a term is ambiguous, label it unclear.

Check the output before changing an account

Read every suggested exclusion against your positive keywords and landing pages. Check language variants and possible uses of ambiguous terms. Test a few obvious and borderline examples, and revise the input if the classifications drift from your actual offer.

• Pick 15 to 20 terms you already know the right answer for and compare them with the output.
• Look at the terms with the most spend first, because those decisions matter most.
• Check low-confidence labels by hand, rather than accepting them.
• Compare any proposed exclusion with terms that have produced qualified leads.

Turn approved decisions into changes

Apply exclusions with the narrowest match type that does the job. Google notes that negative keywords do not match close variants or other expansions, so add plurals, misspellings and translations separately. Account-level negative keywords apply across several campaign types and are limited to 1,000 per account, so use campaign level for anything that is not irrelevant everywhere.

Keep a change log with the term, label, decision, the person who approved it, the date, the reason and a date to review the effect. That log is also what lets you reverse a mistake quickly.

Make it repeatable

1. Save the input template and the prompt, and version them when you change them.
2. Run the same review on a fixed schedule, such as weekly.
3. Track whether irrelevant spend falls and qualified leads hold steady after each round of exclusions.
4. Update the “protect” and “not our buyer” lists as the offer and markets change.

Know the limits

• The assistant can misread ambiguous terms, especially across languages or industries.
• Its confidence is not a measurement. Treat it as a hint about where to look.
• It does not know your lead quality unless you provide it, so include qualified lead counts where you have them.
• Classifications can drift between runs, which is why a fixed format and spot checks matter.

Start with the AI prompt builder, and read what your search terms say about lead quality for the review method behind the labels.