AI Ad Quality Scorecard
Grade each ad across message, proof, brand, visuals, and platform fit
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 7
- Confidence
- 91%
Evaluate AI advertisements through separate but connected quality dimensions instead of reacting to the overall polish. Start with strategic validity: the value proposition, proof point, and copy must be accurate, specific, and relevant to audience intent. Then inspect execution: imagery should connect to the product, logos and colors must match the brand, text must remain readable, and supporting claims need a clear hierarchy. Finally, judge platform fit. A homemade-looking interruption may suit LinkedIn, a visual transformation may suit Instagram, and copy-led proof may suit a Google search ad. The decisive threshold is practical: determine whether the ad is strong enough that you would spend money distributing it. Failed dimensions become targeted revision instructions.
Origin
Extracted from Marketing Against The Grain by converting the host's repeated live ad-review criteria into a reusable scorecard.
Core principles
- 01Judge copy and creative execution separately
- 02Verify factual and brand accuracy before launch
- 03Protect legibility and message hierarchy
- 04Evaluate ads in the context of their platform
- 05Require a credible link between the product and every visual
How to run it
- 1
Check message accuracy
Verify that the product, benefit, customer result, and call to action are represented correctly.
Pro tip Favor specific, supportable results over broad promises.
Watch out Do not approve a persuasive claim merely because it sounds plausible.
- 2
Grade the copy
Assess the hook, clarity, specificity, audience relevance, and strength of the value proposition.
Pro tip Separate headline quality from the rest of the design.
Watch out Good copy does not compensate for a misleading or unusable creative.
- 3
Grade visual relevance
Confirm that the selected image or illustration supports the product and message.
Pro tip Ask what meaning the image adds that the copy cannot supply alone.
Watch out Generic AI imagery can distract from an otherwise credible advertisement.
- 4
Verify brand fidelity
Compare logos, colors, typography, and overall treatment with approved brand assets and guidance.
Pro tip Use supplied brand files rather than relying on model recall.
Watch out A near-match logo is still an incorrect logo.
- 5
Test legibility and hierarchy
Check for overlaps, low contrast, isolated supporting text, and unclear reading order.
Pro tip Inspect the ad at the approximate size users will encounter it.
Watch out A message that cannot be read instantly will not function as intended.
- 6
Assess platform fit
Judge whether the format and creative treatment match the context, behavior, and intent of the target network.
Pro tip Use different standards for feed-based visual ads and intent-driven search ads.
Watch out Do not impose the same visual requirements on copy-only search advertising.
- 7
Make the spend decision
Classify the ad as ready to run, worth revising, or unsuitable based on the failed dimensions.
Pro tip Translate every failed score into one concrete revision request.
Watch out Do not spend media budget merely because producing the asset consumed time or credits.
In the wild
The copy contains a potentially useful chatbot contrast, but the image is unrelated, text overlaps the word 'support,' and blue value propositions float at the bottom. The scorecard fails visual relevance, legibility, hierarchy, and brand treatment, so the ad is rejected for revision.
→ Specific failed dimensions replace an unhelpful general impression.
A responsive search ad communicates automatic ticket resolution, rapid setup, supported channels, faster resolution, and a clear action. Because search is primarily a copy and intent-matching problem, it scores strongly without elaborate imagery.
→ The ad advances to headline testing and intent matching.
Common mistakes
Scoring only the copy
Strong wording cannot rescue an irrelevant image, incorrect logo, or illegible layout.
Scoring only visual polish
A polished treatment can still contain weak positioning, generic claims, or the wrong platform behavior.
Using identical channel standards
Search, LinkedIn, and Instagram require different judgments about visuals, intent, and creative treatment.
Is it for you?
Best for
Marketers reviewing mixed-quality batches of AI-generated visual and search advertisements.
Not ideal for
Final campaign optimization where conversion data already provides a stronger decision signal than qualitative review.
From the transcript
“Are the ads actually good and would we run them? That is a big, big question.”
“The image has nothing to do with the product, the copy isn't as strong as the other two.”
“This, because it's Google and it doesn't require all the visual imagery, is going to be a lot easier because it's more of a copy…”
From the episode
Can AI Actually Make Good Ads? Replit Ad Maker Review