You've generated a batch of 20 campaign images and need to decide which six go live tomorrow. There's no creative director in the office. The designer is stretched. Your marketing manager has strong instincts but no formal visual training. What happens next is usually one of two things: the decision gets made based on personal preference, or everything goes into a holding pattern waiting for someone more senior. Neither is good. Here's a better approach.
Build a visual checklist from your brand rules
Before you can review AI visuals consistently, you need a checklist that doesn't depend on someone's taste. Good visual QA is binary, either the asset meets the requirement or it doesn't. Start by translating your visual brand rules into checkable criteria:
- Primary brand colour is present and uses the correct hex value (not 'close enough')
- Font is correct typeface, correct weight, no substitutions
- Logo is present where required and within the safe zone guidelines
- Image composition follows the brand's proportion rules (centred subject, rule of thirds, etc.)
- Any text overlays meet the minimum contrast ratio for accessibility
- The mood or visual tone matches the brand style (check against your approved image library)
- The asset is the correct dimensions for the target platform
A checklist with seven to ten specific criteria replaces a creative director for 90% of routine reviews. The remaining 10%, genuinely ambiguous cases, brand-stretching creative work, can wait for senior sign-off without blocking everything else.
Create a visual reference library
The most useful tool for non-specialist visual review is a curated set of approved examples. Three to five 'gold standard' assets per format, social image, email header, ad banner, that everyone on the team agrees represent the brand at its best.
When reviewing new AI-generated assets, compare them against the reference set. Not 'is this beautiful?' but 'is this closer to or further from the gold standard?' That's a judgment anyone can make with some training.
Update the reference library quarterly. As your brand evolves, the gold standard examples should evolve with it. An outdated reference library is worse than no reference library, it anchors decisions to a brand look you've already moved past.
What to do when you're genuinely unsure
Sometimes an asset passes all the technical checks but still feels off. That feeling is data. Note specifically what's causing it, is it the expression on a person's face? The colour balance feels cool when your brand is warm? The composition feels cluttered?
Be specific enough to add it to your checklist or your brief parameters. A vague 'this feels wrong' doesn't improve your next AI brief. A specific 'the image felt too cold, our brand uses warm-toned photography' does.
For genuinely ambiguous cases, escalate to a quick Slack message with two options and a simple question. Keep the escalation tight: attach the two variants, specify the decision needed, and give a deadline. Most creative directors can turn around a 30-second visual decision when it's presented cleanly.
Feedback loops that improve future generation
Every review decision is a data point for your AI visual workflow. When you reject an asset, record why. When you approve one, note what made it work. After 30 to 40 reviews, patterns emerge: maybe your AI workflow consistently produces images that are too dark for your brand, or always crops subjects too tight.
Feed those patterns back into your image generation parameters. If the darkness is consistent, add a brightness or tone instruction. If the cropping is consistently too tight, specify the framing more precisely in the brief. The review process is how your workflow gets smarter over time.
Choose the format you produce most often, probably social images or email headers. Write a visual review checklist specifically for that format using your brand guidelines. Run your next batch of AI visuals through it and note every case where the checklist gave you a clear answer vs. every case where you had to use judgment. Close those judgment gaps in your brief parameters before the next batch.
Further reading
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