You pause on an ad mid-scroll, squint at the image, tilt your head, and think, “Yeah… this is totally AI.”
You might not be able to say exactly why. Maybe the lighting’s a little too perfect. Maybe the people look slightly off. Maybe the copy sounds like it was written by a committee of interns who’ve never met a real customer.
What matters is that you’re not just looking at the work anymore. You’re judging how it was made—and whether you should trust it.
We’re past the point where AI is a novelty. Most people know brands are using it somewhere in the process. At the same time, a lot of them are uneasy about what that means for truth, ethics, and quality. They’re not only asking, “Do I like this?” They’re also wondering, “Do I believe this?”
If you’re responsible for design or brand, that changes the job. You’re not just designing with AI tools. You’re designing for an audience that expects AI to be involved and is actively scanning for shortcuts.
This article looks at what that means in practice: how to recognize the “AI feel,” how to use AI without flattening your brand, and how to design experiences that still feel credible when everyone knows what’s technically possible.
The Audience Isn’t Naïve Anymore
For years, the industry conversation was mostly about whether we could generate content that looked “real enough.” Now the bar has moved. People assume you can generate something that looks decent. They’re more interested in whether you chose to.
You can see this shift play out everywhere:
- People call out weird hands, impossible reflections, and uncanny faces in the comments.
- Creators debate whether brands are paying artists fairly or just swapping budgets for prompts.
- Regulators and platforms are starting to push for labels, disclosures, and provenance tools.
Underneath all of that is a simple change in mindset. Your audience isn’t giving you the benefit of the doubt by default. They’re bringing a layer of skepticism to anything that feels overly polished, generic, or disconnected from reality.
That doesn’t mean you should stop using AI. It means you need a clearer point of view about where it belongs, how you talk about it, and what “authentic” looks like for your brand now that everyone knows how easy it is to fake things.
What People Actually Mean When They Say “This Feels Like AI”
When someone says a visual or a campaign “feels like AI,” they’re usually reacting to a combination of signals, not a single glitch.
On the visual side, it often looks like this:
- Perfectly lit, spotless scenes with no texture or history.
- Faces and bodies that look smooth but slightly unreal, like they came from the same template.
- Tiny inconsistencies in details—background objects, text on signs, hands—that don’t match how the physical world works.
On the copy side, it often sounds like this:
- Sentences that are grammatically fine but emotionally flat.
- Brand lines that could belong to any company in the category.
- Big promises with zero specifics or lived experience.
People read those signals as “no one cared enough to make real choices.” The work feels like something that was generated, skimmed, and shipped.
You don’t fix that feeling by hiding the tools. You fix it by putting more human judgment back into the process and making that judgment visible in the work.
Principle 1: Let Real-World Texture Back In
AI is very good at “average.” It’s designed to converge on what a thing usually looks like. That’s part of the problem.
Real experiences have texture. They include small flaws, mismatches, and traces of actual use. When you strip all of that out, your content might look clean, but it doesn’t feel grounded.
You can push against that in a few practical ways:
- Pair AI-generated visuals with real photography of actual people, places, and products, especially in high-trust moments like case studies or onboarding flows.
- Leave in small honest details—scuffed surfaces, imperfect lighting, real office clutter—so scenes don’t look like empty renderings.
- Write copy with specifics: names, locations, timeframes, and observations you’d only know if you were really there.
You’re still allowed to polish, but you’re aiming for “credible” rather than “flawless.” A few deliberate imperfections can do more for trust than another round of smoothing.
Principle 2: Decide Where AI Belongs in Your System
The question isn’t “Should we use AI?” It’s “Where does AI make sense for us, and where doesn’t it?”
Instead of treating every asset the same, think in terms of a sourcing palette:
- Use real photography and video when you’re asking people to trust you with something big: health, money, safety, identity, or long-term commitments.
- Use AI-assisted images for concept worlds, abstract metaphors, mood explorations, or things you literally can’t shoot, like visualizing data or future scenarios.
- Use community and UGC content when you’re building belonging, showing social proof, or highlighting lived experience.
Once you sketch that palette, write it down. Make it part of your brand guidelines. That way, designers, marketers, and agencies aren’t improvising the ethics of each campaign at the last minute.
The point isn’t to be pure. The point is to be intentional.
Principle 3: Be Clear About the Role AI Plays
People are much more forgiving when they feel like you’re being straightforward with them.
If you quietly replace photographed people with generated ones, or you position an AI-built scene as a documentary image, you’re inviting backlash the moment someone notices a seam. If you treat AI as one of several tools and talk about it that way, it feels very different.
You don’t need a disclaimer on every banner, but you should be able to answer three questions internally and, when it matters, externally:
- Where did AI touch this work—idea generation, layout exploration, image creation, editing, copy variants?
- What did humans still own—direction, taste, ethics, brand voice, final selection?
- What guardrails did you use—no real people’s likenesses, no deepfakes, no synthetic testimonials, bias checks?
That level of clarity prevents “we’ll figure it out later” decisions that turn into trust problems down the line.
Principle 4: Design for an Audience That’s Looking for Red Flags
When you assume people trust you, you optimize for impact. When you assume people are skeptical, you start optimizing for clarity.
In a world that’s watching for AI tells, clarity is a competitive advantage.
That might look like this:
- Steering away from synthetic humans entirely in some categories and using AI only for environments, objects, and abstract visuals.
- Keeping your design system strong enough that even AI-assisted assets still feel like “you” instead of random outputs from a prompt playground.
- Tightening your claims and backing them up with concrete examples, numbers, and stories instead of vague, hype-heavy language.
You’re making it easy for someone to say, “I might not love every aesthetic choice here, but I can see how they made it and what they’re trying to say.”
A Simple Checklist for Your Next AI-Touched Project
Before you ship a campaign, page, or visual that uses AI somewhere in the process, you can run it through a quick gut check:
- If a customer knew exactly how this was made, would we still feel comfortable with it?
- Does this piece rely on trust? If yes, did we lean on real people and real places where it counts?
- Can we explain, in one or two sentences, how AI helped and what humans decided?
- Does anything here scream “generic” when we read or look at it out of context? If so, where can we add specificity or texture?
If you can answer those questions honestly and still feel good about the work, you’re probably in a solid spot.
Designing for People Who See the Wires
Your audience knows AI is in the room. They know brands are under pressure to move faster and cheaper. They know how easy it is to create something that looks plausible at a glance.
What they don’t know is whether you’re using that power in a way that respects them.
That’s what you’re really designing for.
You’re not just deciding whether to use AI. You’re deciding where to bring in real faces and real stories, where to lean on synthetic imagery, how much texture to keep, and how much you’re willing to say about your process.
When you make those choices on purpose—and you let a bit of human messiness back into the work—you stand out in a feed full of smooth, interchangeable content.
You’re not just designing for a world full of AI. You’re designing for people who can see the wires and are still looking for something they can believe.
