How to Use AI to Improve Your Ads: 3 Practical Steps

If you are asking how to use AI to improve your ads, do not start with "replace your marketing team with agents."

Start with the parts of the account that already need more attention:

1. Give AI your campaign data and ask better questions.
2. Use AI to create more creative variations.
3. Use placement analysis tools to find where your budget is being wasted.

None of this requires a custom AI stack. Most advertisers can do all three with tools they already have access to.

1. Export Your Campaign Data and Let an LLM Look for Patterns

The first low hanging fruit is simple: export your full campaign data and put it into the LLM of your choice.

Do not only export the account overview. The overview is too clean. It hides the mess. Export the detailed tables where the real patterns live.

For Google Ads, that can include:

• Campaign performance
• Ad group performance
• Ad and asset performance
• Search term reports
• Keyword performance
• Audience segments
• Device performance
• Location performance
• Time of day and day of week data
• Landing page data
• Placement reports for display and video campaigns

Include the columns that matter:

• Cost
• Impressions
• Clicks
• CTR
• CPC
• Conversions
• Conversion rate
• CPA
• Conversion value
• ROAS
• View-through conversions if relevant
• Bounce rate or engagement data if you have analytics data connected

Do not ask the model to magically "optimize my ads." That usually produces generic advice.

The better approach is to give it a specific analyst role and ask it to find patterns you might have missed.

Example prompt:

You are a performance marketing analyst reviewing this Google Ads export.

Look for wasted spend, weak segments, unusual performance patterns, and places where the campaign structure may be hiding problems.

Separate your findings into:
1. Clear issues
2. Possible issues that need verification
3. Opportunities for testing

For every recommendation, reference the campaign, ad group, keyword, placement, device, location, or creative row that supports it.

Do not give generic advice. Only comment on what is visible in the data.

The value is mostly pattern recognition. LLMs are good at reading messy tables, spotting repeated issues, and turning raw exports into a review list. They are less reliable when you ask them to do precise arithmetic from memory, so keep the source spreadsheet open and verify the numbers before making changes.

Good questions to ask after the first pass:

• Which campaigns spend the most without producing meaningful conversions?
• Are there devices with high spend and low conversion quality?
• Are certain locations generating clicks but no business value?
• Are some ad groups too broad compared with the search terms they attract?
• Which creatives are getting impressions but not engagement?
• Are we over-investing in campaigns that only look good because of one metric?
• Are there placements with suspicious CTR, low engagement, or no conversion value?

Think of it as a structured second opinion. For many accounts, it will surface issues faster than clicking through the interface manually.

One caveat before you upload anything: never upload data you are not allowed to upload. Remove customer names, emails, phone numbers, offline conversion details, and anything sensitive. If the data is commercially sensitive, use an approved company tool or enterprise account with the right data protections.

2. Use AI to Create More Creative Variations

Creative testing is another area where AI is immediately useful.

Most accounts do not suffer from too many creative ideas. They suffer from too few. One headline gets written. One banner concept gets approved. One landing page angle gets repeated across every audience.

AI lowers the cost of creating variations.

For copy, use an LLM to generate:

• Headline variations
• Short descriptions
• Benefit-led messages
• Problem-led messages
• Objection-handling copy
• CTA variations
• Audience-specific angles
• Landing page section ideas
• Responsive search ad assets
• Display ad copy variants

The output gets much better when you provide constraints.

Bad prompt:

Write better ads for my product.

Better prompt:

Write 20 display ad headline variations for a B2B SaaS product that helps marketing teams find low-quality Google Display Network placements.

Audience: performance marketers and paid media managers.
Tone: direct, practical, not hype-heavy.
Avoid: claims about guaranteed results, aggressive fear-based copy, and vague AI buzzwords.
Maximum length: 35 characters.
Focus on: wasted spend, placement quality, brand safety, and faster audits.

You can do the same for images.

The image creation tools inside products like ChatGPT and other AI platforms have become good enough to produce polished static banner concepts. They can create backgrounds, product-style compositions, illustrations, simple visual metaphors, and ad layouts that are good enough for internal review or early testing.

They are especially useful for:

• Exploring different visual directions before involving a designer
• Creating first drafts for static display banners
• Testing new color and layout directions
• Turning a text angle into a visual concept
• Producing variations for seasonal or campaign-specific ads
• Mocking up creative for stakeholder approval

I would not use this workflow to chase one perfect banner.

I would use it to create ten directions quickly, then choose what is worth refining.

Example prompt for static banners:

Create a static display ad concept for a SaaS tool that helps marketers identify low-quality display ad placements.

Format: 1200 x 628.
Audience: paid media managers.
Style: clean, modern, editorial, not cartoonish.
Message: stop wasting display budget on low-quality websites.
Include a clear headline area, a small product UI hint, and space for a CTA.
Avoid clutter, fake charts with unreadable text, and exaggerated futuristic visuals.

There are still limitations. AI-generated banners can miss brand guidelines, produce weak typography, or create text that needs correction. Treat the output as a draft, not final production artwork.

A sensible workflow looks like this:

1. Use AI to generate concepts.
2. Pick the strongest direction.
3. Have a designer or marketer clean up the final asset.
4. Test it against your current control creative.
5. Keep the winner, then generate more variations around that direction.

Used this way, AI speeds up creative iteration. It still needs taste, brand judgment, and performance measurement around it.

3. Use Placement Analysis to Improve Where Your Ads Run

AI can help write better ads and find patterns in campaign data, but there is one part of display advertising that still needs direct attention: placement quality.

Your ads can have strong targeting, good creative, and a clean landing page, and still waste money if they run on low-quality websites.

This is common in display campaigns because the network is huge. Broad targeting can push impressions into:

• Made-for-advertising websites
• Clickbait sites
• Ad-heavy pages
• Low-quality content farms
• Bot-heavy environments
• Brand-unsafe content
• Irrelevant apps or sites
• Placements with accidental or low-intent clicks

The problem is that platform metrics often make these placements look acceptable.

A bad placement can have cheap clicks. It can have a decent CTR. It can even generate conversions if the conversion action is too soft, like a page view, engaged session, or low-quality lead form.

That does not mean the placement is valuable.

Placement analysis tools are built for this problem.

Instead of only asking "which campaign performed best?", you also ask:

• Where did the ads actually appear?
• Are those websites real editorial environments?
• Is the content relevant to the brand?
• Is the page overloaded with ads?
• Does the site exist mainly to monetize traffic?
• Is the domain suitable for the advertiser?
• Should this placement be excluded account-wide?

You can start manually by exporting the placement report from Google Ads and reviewing the highest spend domains. Sort by cost first. Then look at placements with high clicks and weak conversion quality. Then look at unusual CTRs and placements that do not fit the audience.

This catches obvious waste, but it does not scale well.

A larger account can have thousands of placements. Reviewing them manually is slow, inconsistent, and easy to postpone. That gap is where tools like DisplayGG fit.

DisplayGG analyzes placement reports and helps identify domains that are likely to be poor fits for display advertising. It gives advertisers a cleaner inventory pool to optimize inside without trying to replace every platform decision.

Useful placement checks include:

• Made-for-advertising signals
• Ad density
• Content depth
• Clickbait patterns
• Brand safety concerns
• Adult, gambling, political, or otherwise sensitive content
• Low-quality or irrelevant domains
• Sites that should be added to exclusion lists

The useful output is practical: a review list, a score, and an exclusion list you can actually apply.

Since Google's January 2026 account-level exclusion rollout, applying that list once covers all your Display and PMAX campaigns automatically — including any new campaigns added after the list was created. You apply it at the account level, not per campaign. This makes placement analysis directly actionable in a way it was not before.

For display campaigns, this is one of the highest leverage AI-adjacent workflows. You are not asking AI to guess who your customer is. You are using analysis to remove the worst places your ads can appear.

Better placements also make the rest of the account easier to read. Campaign data gets cleaner, automated bidding gets better signals, and creative performance becomes easier to interpret. The same ad can look weak on low-quality inventory and strong on relevant inventory.

A Simple AI Workflow for Better Ads

If you want a practical starting point, use this workflow once per month:

1. Export your campaign data.
2. Export your placement report.
3. Ask an LLM to review the campaign data for wasted spend and weak segments.
4. Ask it to generate new ad copy angles based on the strongest and weakest patterns.
5. Use image generation to create several static banner concepts for the best angles.
6. Analyze placements and build an exclusion list.
7. Launch a controlled test instead of changing everything at once.
8. Review results after enough data has accumulated.

Running this once a month keeps the work grounded in actual account data.

The point is to move faster on the recurring work that already improves ad performance:

• Better analysis
• More creative testing
• Cleaner placements
• Fewer wasted impressions
• Better decisions from the data you already have

What to Avoid

AI can also make advertising worse if you use it lazily.

Avoid these mistakes:

• Uploading incomplete data and trusting broad recommendations
• Letting AI rewrite ads without brand or compliance constraints
• Testing too many variables at once
• Treating AI-generated banners as final artwork without review
• Optimizing only for CTR
• Ignoring placement quality
• Assuming platform automation will fix bad inputs

The last item is the one that quietly does the most damage.

AI will not rescue a bad campaign setup. If the data is messy, the creative is generic, and the placements are low quality, automation can simply optimize the wrong thing faster.

Start With the Inputs

You do not need to start with a complicated tool stack.

Start with the work that is already sitting in front of you:

• Export the data.
• Ask better questions.
• Generate more creative options.
• Review where your ads actually run.
• Remove the placements that should never have received budget in the first place.

For most advertisers, the gains will come from faster analysis, faster creative iteration, and cleaner campaign inputs.