Here’s how I use AI to save hours on negative keyword mining and stop wasting ad spend.
Here’s the thing: Manually reviewing search terms is mind-numbing work. You know exactly what I’m talking about. That 10,000-row spreadsheet staring back at you. Your eyes glazing over after 30 minutes. Still 9,500 rows to go.
Stop it.
I download all my negative keyword lists and plop ’em into ChatGPT, Claude, Gemini; whatever.
I then upload my full search term report from Google Ads, and ask AI to flag irrelevant terms or budget wasters, and suggest which negative keyword lists they belong in.
What I get back is incredibly useful.
I’ll see results like: “These 15 terms go in your job seekers list,” or “These 8 belong in your free tools exclusions.”
And you can also flip this around: Ask AI to flag any existing negatives that might be blocking good traffic.
And yeah, you still need to use your actual brain. Don’t blindly add everything. Review it like the real life professional you are.
But this has saved me SOOOO many hours.
AI does the grunt work. You validate and deploy.
View this post on Instagram
Why negative keyword research kills productivity
Most PPC managers avoid this tedious work until their budgets are bleeding out.
And you wonder why campaigns underperform.
The endless search term scroll
Picture this: You’re staring at a search term report with 10,000 rows. You need to identify which terms are wasting budget, categorize them by theme, and add them to the right negative keyword lists.
So you start scrolling. Row by row. Looking for obvious budget wasters like “free,” “jobs,” “DIY,” “cheap.”
Thirty minutes in, your eyes are glazed over and you’ve only made it through 500 rows. You’re second-guessing every decision and losing focus on the bigger patterns.
Here’s the thing: This isn’t just tedious. It’s expensive. While you’re scrolling, those irrelevant terms are still triggering your ads. Still eating budget. Still sending garbage traffic that’ll never convert.
Organizing negatives across multiple campaigns
Even when you identify the problem terms, organizing them is another nightmare.
You’ve got different campaigns for different products, audiences, and objectives. Each needs its own negative keyword strategy.
Should “enterprise” be negative in your SMB campaign? Should “cheap” be negative everywhere or just in premium service campaigns? Which list does “tutorial” go in?
Manually sorting hundreds of negative keywords into the right lists and campaigns is tedious, error-prone work that scales terribly as your account grows.
Most PPC managers spend 3-4 hours per month on negative keyword research. That’s 36-48 hours per year of mind-numbing data sorting.
Believe me when I say this: There’s a better way.
The AI negative keyword process that works
Here’s how to turn hours of mind-numbing work into 15 minutes of actual strategy.
Step 1: Download and upload your data
First, grab your materials from Google Ads:
Search terms report: Go to Keywords > Search terms, set your date range (usually last 30-90 days), and download the full report as CSV.
Existing negative lists: Download your current negative keyword lists from Tools & Settings > Negative keyword lists.
Campaign structure overview: Note your campaign names and objectives so you can provide context to AI.
Don’t clean up the data first. AI handles messy CSV files just fine, and cleaning might remove context that helps with categorization.
Step 2: Let AI categorize and flag terms
Upload both files to your AI tool of choice and use this prompt framework:
*”I’ve uploaded my Google Ads search terms report and existing negative keyword lists. Please:
- Identify search terms that are clearly irrelevant or budget-wasting
- Categorize these terms by theme (jobs, free, DIY, competitors, etc.)
- Suggest which of my existing negative lists each term should be added to
- Flag any high-volume waste terms that need immediate attention
- Point out any existing negatives that might be too broad and blocking good traffic
Context: [Brief description of your business and campaign objectives]”*
Step 3: Review and validate suggestions
AI will return something like this:
High-Priority Budget Wasters:
- “free accounting software” (47 clicks, $312 spent) → Add to “Free Tools” list
- “accounting jobs” (23 clicks, $156 spent) → Add to “Job Seekers” list
- “QuickBooks alternative” (31 clicks, $203 spent) → Add to “Competitor Comparison” list
Potentially Over-Broad Negatives:
- Current negative “software” might block “accounting software solutions”
- Current negative “comparison” might block “software comparison guide”
New Negative Categories Needed:
- Student/education terms (homework, assignment, class project)
- Location-specific terms for non-service areas
Now you’ve got a real plan instead of a data headache.
Advanced AI prompts for smarter filtering
Once you’ve mastered the basic process, these advanced prompts help you find more sophisticated waste patterns.
Finding budget wasters by category
*”Analyze this search terms report and identify terms that suggest the searcher wants something we don’t offer. Group by intent category and show cost impact:
- Information seekers (how-to, tutorial, guide)
- Job seekers (career, employment, hiring)
- Students (homework, assignment, project)
- DIY solutions (free, template, example)
- Competitor research (vs, compared to, alternative)
For each category, show total clicks and spend, then recommend specific negatives.”*
This helps you see which types of irrelevant traffic are costing you the most money.
And you wonder why your CPCs keep climbing? It’s because you’re paying for traffic that was never going to convert in the first place.
Identifying over-broad negatives that block good traffic
Here’s the thing: Being too aggressive with negatives can be just as expensive as having none at all.
*”Review my current negative keyword lists against this search terms report. Identify any negatives that might be blocking relevant traffic:
- Show me good search terms that might be blocked by current negatives
- Suggest more specific negative keywords to replace overly broad ones
- Highlight any negative keywords that haven’t prevented irrelevant clicks
Goal: Reduce waste without blocking potential customers.”*
This prevents the common mistake of adding negatives that are too aggressive and hurt campaign performance.
BTW – this is where having someone who actually understands your business makes a huge difference. AI can spot patterns, but you know whether “budget” or “affordable” attracts your ideal clients or just price shoppers.
Setting up your AI negative keyword workflow
Make this part of your regular routine or watch your budget slowly bleed out through irrelevant clicks.
Weekly quick scan (15 minutes):
- Download last 7 days of search terms
- Upload to AI with prompt: “Flag any new high-spend irrelevant terms”
- Add obvious budget wasters immediately
Monthly deep analysis (30 minutes):
- Download full month of search terms and current negative lists
- Run comprehensive AI analysis
- Review all suggestions and update negative lists
- Check for over-broad negatives blocking good traffic
Quarterly strategy review (60 minutes):
- Analyze 90 days of data for bigger patterns
- Ask AI to suggest new negative keyword categories
- Review campaign-specific negative strategies
- Update account-level negative lists
Pro tip: Create a simple spreadsheet to track your AI suggestions and their impact. This helps you learn which prompts work best and validate AI recommendations over time.
What AI catches that you miss
Here’s where AI really shines. It spots patterns your tired brain glosses over.
Pattern recognition across large datasets
AI excels at:
- Spotting subtle variations of the same irrelevant intent (“free vs gratis vs no cost vs zero dollars”)
- Identifying location patterns in non-local campaigns (“near me” when you ship nationally)
- Finding brand name misspellings and variations you’d never think to check
- Categorizing terms by searcher intent at scale
- Cross-referencing existing negatives against new search terms
Example: AI might notice that terms containing “template,” “example,” “sample,” and “format” all indicate DIY intent, even if you only thought to look for “free” and “template.”
When human judgment beats AI
But here’s the thing: You’re still essential for strategic decisions.
You’re still needed for:
- Industry-specific context (is “accounting software comparison” good or bad traffic for YOUR business?)
- Campaign strategy decisions (should we exclude “cheap” from lead gen campaigns?)
- Competitive intelligence (which competitor terms should we allow vs exclude?)
- Seasonal considerations (holiday terms might be relevant in Q4 but not other quarters)
- Budget priorities (focus on high-spend waste vs long-tail cleanup)
The sweet spot: AI handles the data processing and pattern recognition. You handle the strategic decisions and quality control.
Never blindly implement AI suggestions. Always review them through the lens of your campaign objectives, target audience, and business goals.
Common negative keyword mistakes (and how to avoid them)
Even with AI help, people still mess this up. Here’s what to watch for:
Over-negating in new campaigns
New campaigns need room to learn. If you add 500 negatives on day one, you’re strangling the algorithm before it can find winning search terms.
Start conservative. Add obvious budget wasters (free, jobs, DIY) but let the campaign run for 2-4 weeks before getting aggressive with negatives.
Forgetting about match types
A broad match negative “software” blocks way more than an exact match negative [software].
Most people add everything as broad match and wonder why their traffic tanks. Be strategic about match types, especially for terms that could be part of good searches.
Not reviewing existing negatives
Negatives you added two years ago might be blocking today’s money keywords. Markets change. Search behavior evolves.
Review your oldest negative lists quarterly and ask: “Are these still relevant?”
Setting and forgetting
The biggest mistake? Thinking negative keyword work is ever “done.”
New search terms appear daily. Language evolves. Competitors launch new products that change search behavior.
This is ongoing maintenance, not a one-time project.
The bottom line
AI does the grunt work. You validate and deploy.
Because negative keyword research doesn’t have to be a monthly productivity killer. Use AI to identify the patterns and waste, then apply your expertise to make the strategic decisions.
Your campaigns get cleaner, your budgets work harder, and you get back hours of time to focus on strategy instead of spreadsheet sorting.
Stop scrolling through endless search term reports. Start using AI to do the heavy lifting.
And believe me when I say this: Once you experience how fast this process becomes with AI, you’ll never go back to manual reviews.
Your budget (and your sanity) will thank you.
