AI Pre-Review Critic
Use emotionless AI feedback to strengthen work before human review
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 4
- Confidence
- 97%
Provide AI with the actual marketing asset, the intended audience, and the conversion objective, then ask it to act as the first critic. Request separate assessments of design and copy so that visual hierarchy, color, clarity, pain-point relevance, offer presentation, and calls to action receive focused attention. Because the feedback has no interpersonal stakes, the marketer can test larger changes without worrying about embarrassment or status. Revise the work using the strongest observations and arrive at human review with a defensible point of view rather than a timid variation of the status quo. The model does not replace accountable reviewers or experiments; it acts as a low-cost rehearsal that exposes omissions, challenges assumptions, and increases the amount of useful iteration completed before colleagues spend time on the asset.
Origin
Extracted from Marketing Against The Grain through Kipp Bodnar's AI critique of the HubSpot homepage for sales-leader conversion.
Core principles
- 01Seek low-stakes criticism before high-stakes review
- 02Define the target audience and desired conversion
- 03Request separate feedback on design and copy
- 04Use critique to make meaningful changes, not cosmetic edits
- 05Bring a developed point of view to human reviewers
How to run it
- 1
Supply the real artifact
Upload or provide the current page, advertisement, or copy rather than describing it abstractly.
Pro tip Use a screenshot when visual hierarchy and color matter.
Watch out Generic descriptions invite generic recommendations.
- 2
Define the review lens
Specify the audience, desired action, and business objective. Ask for design and copy analysis as distinct sections.
Pro tip Name the product tier or offer being promoted.
Watch out Feedback without a conversion goal may optimize the wrong outcome.
- 3
Interrogate the findings
Separate concrete observations from generic advice and ask for proposed changes tied to each issue.
Pro tip Request the rationale behind every recommendation.
Watch out AI feedback can be plausible but strategically wrong.
- 4
Revise before human review
Implement meaningful improvements, establish a clear point of view, and then seek accountable stakeholder or customer feedback.
Pro tip Document which AI recommendations you accepted and rejected.
Watch out Do not represent AI critique as customer validation.
In the wild
Kipp uploaded a screenshot of HubSpot's homepage and asked how design and copy could persuade more sales leaders to adopt the free CRM. The model flagged the dominant orange palette, challenged the generic headline, and recommended explaining the free offer's specific advantages.
→ The marketer received actionable rehearsal feedback before entering a higher-stakes human review process.
Common mistakes
Requesting generic feedback
Without an audience and desired behavior, the model cannot judge whether the asset is fit for purpose.
Treating critique as truth
AI observations are hypotheses that still require strategic judgment and, where possible, testing.
Making only tiny changes
The method loses value if fear still limits revision to superficial adjustments.
Is it for you?
Best for
Marketers who need to improve a webpage, advertisement, or message before presenting it to senior stakeholders.
Not ideal for
Final approval decisions requiring customer evidence, legal review, accessibility testing, or accountable human judgment.
From the transcript
“Well, your very first line of feedback can be from AI that is essentially emotionless, right?”
“And so it gives us really good design feedback.”
“you're gonna come to like a human review process with a clear point of view.”
From the episode
6 AI Growth Hacks Top Marketers Don't Want You To Know (#167)