Back in 2023, we wrote an article about using ChatGPT to identify negative keywords. Cut to 2026, we thought that this topic needed to be revisited as a lot many changes have occured in the AI landscape that needs to be incorporated (if it is being put in practice and even if it is not, in near future you can expect reaching out to it for some mundane tasks)
Negative keywords has always acted as the backbone of a healthy paid search account. If you get it all wrong, then on one hand you bleed budget because of irrelevant impressions and on the other you choke your conversions, if you are too aggressive with your negative keyword strategy. As an advertiser it becomes a challenge to bridge the gap between too much and too less.
AI tools made it a tad bit easier to help with the task of identifying negative keywords. But its role has evolved over the years, to now advertisers asking harder questions such as which model, what prompts, what guardrails, and how does this sit alongside automated platforms like Performance Max that already fight your negatives at every turn?
Why the 2023 Post Needed Updating?
Majorly three things have changed since the 2023 written post:
First, Google’s own automation has become far more aggressive. Match types have deteriorated significantly — as @Josephwilliams put it in the December 2025 PPCChat session:
“The overall deterioration of match types as a whole… 2025 has given me some of the craziest ‘matches’ I’ve seen in my short 7 years of doing this.”.
Performance Max now touches search inventory with limited transparency, which means negative keywords must work harder than ever.
Second, the AI model landscape diversified. ChatGPT no longer sits alone; Gemini, Claude, and several other models each have distinct strengths for this kind of structured research task. The choice of model matters more than many guides acknowledge.
Third, a new generation of purpose-built PPC campaign management tools has emerged that integrate AI-powered negative keyword recommendations directly into your workflow — with access to your actual account data — removing the copy-paste dance entirely.
Industry context In a December 2025 PPCChat session recap published by us, practitioners described AI as the single biggest acceleration in PPC in 2025, while simultaneously noting that trust in platforms is at an all-time low. The two are connected: practitioners are using AI-assisted research to reclaim the control that Google’s match type and automation changes have stripped away.
Tactic 1: The Search Term Dump
Instead of asking an AI to imagine what negatives you might need, feed it real data. Export 90 days of search terms from Google Ads (no conversion data required), paste it into Claude or GPT-4o, and use a structured prompt to identify patterns.
Sample prompt — search term analysis
System context: You are a senior PPC analyst reviewing Google Ads search term data. Prompt: Below is a search term report for a [B2B SaaS / e-commerce / local service] account selling [product/service]. Review all terms and: 1. Identify clusters of irrelevant intent (e.g. "free", "DIY", "student", "salary") 2. Flag informational queries unlikely to convert 3. Highlight competitor brand names appearing in results 4. Suggest negative keywords at campaign AND ad group level 5. For each suggestion, note your confidence (high/medium/low) and reason Do NOT suggest any terms that could be valuable in a competitor conquest strategy. [PASTE SEARCH TERM DATA BELOW]
As per The State of PPC Global Report 2026, a survey conducted on about 1306 PPC professionals:
Only 39% of practitioners are using AI for keyword research at all. The majority are still using it only for copy. The practitioners this post is written for — those using AI for search term analysis, exclusion strategy, and n-gram pattern recognition — are operating ahead of the majority. The risk at that level isn’t adoption. It’s shallow adoption: treating AI output as a finished list rather than a first draft to be verified against real account data.
Another interesting finding was that
“AI and automation” was the #1 priority for PPC professionals in 2024. By 2026 it has dropped to #3, behind improving campaign efficiency (68%) and generating conversions (59%). The novelty has worn off. What remains is practical daily adoption — which is precisely where the risk of complacency and shallow usage lives.
Tactic 2: Targeting Audience Demography Based
For new campaigns with no data yet, ask the AI to think through who you don’t want — not just what terms to exclude.
Sample prompt — persona-based exclusions
Prompt: I'm launching Google Search ads for [product]. My ideal customer is [description]. Describe 5 distinct persona types who might search for related terms but will NEVER convert: their motivations, what they'd type into Google, and why they wouldn't buy. Then translate each persona into a list of negative keywords. Flag any terms that could also be high-intent for my actual audience so I can decide whether to exclude them carefully.
Tactic 3: The Close Variant Trap Audit
Google’s close variant matching has become increasingly liberal, serving ads on semantic neighbours of your keywords. Ask AI to stress-test this.
Sample prompt — close variant stress test
Prompt: Here are my target keywords: [list]. Google's close variant matching may serve my ads on semantically related terms I haven't considered. Generate 40 related terms that Google might consider "close variants" but which are irrelevant to my business. Explain the semantic reasoning for each so I can judge whether to add it as a negative.
Tactic 4: The N-gram Anomaly Detector
For mature accounts, use AI to identify single words appearing across many search terms that consistently waste budget — what the industry calls the “n-gram analysis.” This is a case where a dedicated tool often beats a general LLM, but if you’re doing it manually, AI can accelerate the interpretation step.
Sample prompt — n-gram interpretation
Prompt:
Here is a list of single words extracted from my search terms report,along with spend and conversions for each word:
Which words should I consider adding as negative keywords? Prioritise by wasted spend. Note any words where high spend but zero conversions might still deserve investigation before blocking (e.g. brand terms, navigational intent).
The Honest Limitations (Updated for 2025)
The original article flagged several limitations of ChatGPT. Two years on, the list has grown — and some limitations have shifted in significance.
No access to your conversion history
This remains the single biggest limitation. An LLM can’t know that a search term generating 40% of your leads also contains the word “free.” Always pre-filter your SQR by performance before prompting any AI — or use a platform tool that has API access.
Data privacy risk
Pasting client search term data into a public AI interface is a contractual and ethical risk most agency contracts don’t account for. Anonymise data before sharing, or use tools with formal data processing agreements.
Confident hallucinations
AI will suggest negatives that sound sensible but could suppress high-intent traffic. The more niche your industry, the more likely it is to miss context. Always verify unusual suggestions against your actual SQR.
Competitor terms flagged as irrelevant
LLMs often suggest competitor brand names as negatives. This is a serious mistake if you’re running competitor conquest campaigns. Always build a protected list of competitor terms before running any AI-suggested negatives.
Performance Max blindspot
PMax campaigns have limited negative keyword support. AI-suggested negatives added at the campaign level may not behave as expected within PMax’s inventory. Understand the current negative keyword limitations for each campaign type before implementing.
Close variant collateral damage
Adding phrase or broad match negatives on the basis of AI suggestions can over-exclude. AI tends to suggest broad terms; in practice, exact match negatives are often safer as a starting point.
It’s a moment-in-time snapshot
AI brainstorming only reflects knowledge at generation time. New product launches, seasonal intent shifts, and emerging search behaviours won’t appear. Human review of live SQRs remains essential and non-negotiable.
The complacency trap. The risk isn’t that AI is wrong — it’s that AI output looks authoritative and complete, so humans stop scrutinising it. Treat every AI-generated negative keyword list as a first draft requiring human review, not a finished deliverable.
What none of this means. It doesn’t mean don’t use it. The consequence isn’t “AI is bad for this work” — it’s that AI changes the nature of the skill required. The new skill is knowing when to trust it, when to override it, how to prompt it well, and how to audit its output with real judgment. That’s harder to teach than reading a search term report, and most teams aren’t investing in building it deliberately.
So, When Should You Trust AI Output
The answer to this lies in recognising the specific conditions under which AI output is reliable enough to act on — and the conditions where it isn’t. The core principle is simple: AI is trustworthy in proportion to how much of the relevant context it actually has access to. That narrows the use cases considerably.
Trust it more when…
Brainstorming with no stakes attached
When generating a first-draft list for a new campaign with no live data, the cost of a wrong suggestion is zero — you review everything before anything goes live. This is AI’s strongest use case. Fast, broad, and the human review step is natural because there’s nothing else to compare against.
Pattern recognition on data you’ve already cleaned
If you export a search term report, filter it to zero-conversion terms only, and then ask AI to cluster by intent — the AI is doing categorisation on data you’ve already screened. The risk of it suggesting you block something valuable is significantly lower because you removed the valuable queries before it ever sees them.
Industries and verticals you know well
If you’ve worked in a vertical for years, you’ll immediately spot when an AI suggestion is wrong. Your expertise acts as the verification layer. The less you know the industry, the more dangerous it is to trust AI output — you lack the instinct to catch errors.
Structural and match-type questions
Asking AI “should I add this as exact, phrase, or broad match negative given this context?” is a well-bounded question with defensible answers. It’s applying rules you could verify yourself — not drawing on proprietary data.
Formatting and organisation
Using AI to take a messy list of candidate negatives and organise them by theme, remove duplicates, or structure them for upload is almost entirely safe. No judgment call is happening — just structure.
Don’t trust it when…
Conversion data is the deciding factor
AI has no idea what converts in your account. A search term that looks irrelevant — “how does X work” — might be your highest-converting query because your audience researches before buying. AI will confidently suggest blocking it. Your data knows better.
Competitor and brand terms are involved
AI will frequently flag competitor brand names as irrelevant because they don’t match your product description. Whether to exclude or target them is a strategic question requiring your business context, not pattern matching.
The industry is niche or technical
The more specialised the domain, the more likely AI is to misread intent. A query that looks like a DIY search might be entirely professional in industries where professionals use informal language. AI trained on general internet text has no way to know this.
You haven’t read every item on the list
This sounds trivial but it’s the most common failure mode. If you’re approving AI-generated negative keyword lists by skimming rather than reading each item, you’re not using AI as a tool — you’re delegating decisions to it. The trust question becomes moot because no real verification is happening.
The four-question test. Before acting on any AI suggestion, ask: (1) Does the AI have my actual performance data, or is it working from pattern matching alone? (2) Can I explain the reasoning well enough to defend this decision to a client? (3) Have I cross-referenced against my protected lists — converting terms, competitor terms, seasonal terms? (4) Am I reviewing this because I expect to catch errors, or because I expect to approve most of it? If the answer to four is the latter, the review isn’t real.
A Suggested Workflow: AI + Human Judgment
We recommend trying a lean five-step loop that keeps AI in its lane and human judgment at every decision gate. It is deliberately simple. The right workflow for your account will depend on your vertical, team size, and how much live data you have. Two alternative frameworks worth comparing against are linked at the bottom of this section.
- → Set your guardrails first. Before any AI prompt, write down your protected list — competitor brands, known converters, high-intent terms. This is the one input that is entirely human and entirely non-negotiable.
- → Use AI for the first pass, not the final call. Feed it your keyword list or search term report and ask for exclusion candidates grouped by intent theme. Treat the output as a shortlist to review, not a list to publish.
- → Cross-reference against conversion data before adding anything. Filter your search term report to zero-conversion terms before any AI review. Never hand AI a report that includes your converting queries — it cannot distinguish them from waste.
- → Add, monitor, refine. Implement approved negatives, watch for conversion drops in the following week, and revert anything that looks like collateral damage. Negative keyword management is a continuous process, not a one-time task.
- → Run a monthly reverse audit. Ask AI to review your existing negative list against recent converting terms — catching over-exclusions that accumulate silently and choke campaigns over time.
Compare against these workflowsTwo practitioners have published detailed, step-by-step workflows worth reading alongside this one. Rodney Warner’s process covers data export mechanics, prompt structure, and a weekly/monthly triage cadence in depth — rodneywarner.com. NoGood’s 2026 PPC prompt guide covers advanced n-gram analysis and API-level automation for teams managing at scale — nogood.io. Neither replaces the judgment layer. Both are worth bookmarking.
What We Conclude
The question in 2026 isn’t whether to use AI for negative keyword research — it’s how to use it within a workflow that preserves human judgment on the decisions that actually move campaign performance. The major AI models each bring something genuinely useful when deployed on the right task. The dedicated PPC platforms bring something they can never have: your actual conversion data.
The practitioners doing this best aren’t picking one tool and calling it done. They’re building a layered workflow where AI handles the scale and the first pass, human expertise handles the judgment calls, and dedicated platform tools close the gap between the two.
Match types will keep expanding. Performance Max will keep pushing boundaries. The role of thoughtful, maintained negative keyword lists — and the smart use of every available tool to build them — is only going to grow.
Key takeaway Use AI to think faster and look broader, not to decide for you. The moment you trust an AI-generated negative keyword list without checking it against conversion data and your account’s specific context is the moment you hand back the control Google has been trying to take from you.
FAQs
Is it safe to upload client search term data to an AI tool?
It depends on the tool and your contract. Most standard agency agreements prohibit sharing client data with third parties — and a public AI interface almost certainly qualifies. Before uploading anything, check your data processing agreements, anonymise where possible (remove branded terms and proper nouns), and consider whether a platform tool with a formal API integration is more appropriate for client accounts. This is an active legal risk most agencies have not yet addressed in their contracts.
How is this different from just using Google’s own recommendations?
Google’s recommendations are optimised for Google’s revenue, not your conversion rate. AI tools used independently — with your data and your prompts — give you a layer of analysis that sits outside the platform’s incentive structure. The State of PPC 2026 survey found that 62% of practitioners cite black-box platform decisions as their top challenge. Using AI for negative keyword research is partly a response to that: reclaiming analytical control the platform has progressively removed.
How often should I be doing AI-assisted negative keyword research?
At minimum, once a month for any active campaign. For accounts with high daily spend or broad match keywords, weekly is more appropriate. The key is consistency — negative keyword lists degrade over time as search behaviour shifts, new competitors emerge, and match type bleed introduces new irrelevant queries. A one-time exercise is not a strategy. Continuous refinement is.
Will AI accidentally block terms that are converting for me?
Yes — if you let it. AI cannot see conversion data, so it makes intent judgments based on language patterns alone. A query containing “free” or “how does X work” might be your top converter in a vertical where researchers buy. The protection is simple: pre-filter your search term report to zero-conversion terms before any AI review. Never hand the AI your full SQR including converting queries. And always cross-reference against your protected list before anything goes live.
Should I be worried about AI making my strategy look like everyone else’s?
It is a legitimate risk. If thousands of advertisers in the same vertical use similar models with similar prompts, they converge on similar exclusion logic. The differentiation comes from what you bring that AI cannot access: your actual conversion history, your specific audience quirks, and your understanding of the business. The practitioners with the sharpest negative keyword strategies use AI for scale and speed, then apply proprietary judgment to the decisions that actually move performance.
What is the best AI model for negative keyword research?
There is no single answer — the right model depends on the task. Large-context models handle bulk search term reports best, processing thousands of rows in a single pass. Web-grounded models are better suited to understanding unfamiliar industries before you build an exclusion list. General-purpose models with plugin integrations work well when you want to cross-reference volume data in the same session. Most experienced practitioners use a combination rather than committing to one, matching the tool to the specific task rather than defaulting to habit.
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