Introduction
Today it's hard to open a media outlet without hearing the word AI, without reading a text generated with ChatGPT, without seeing an image created with AI. AI has become ubiquitous in content creation.
Yet, in brand and communication teams, the question I hear very regularly is:
“Is it really good quality?”
Understandable. Between the promises of tools and the reality of produced visuals, the gap is brutal. Smooth images, without emotion. Videos difficult to use. Content quickly produced… but interchangeable.
Result: contradictory demands.
Produce faster, reduce costs, test AI — without ever degrading brand image
The problem is not AI.
The problem is not knowing how to use it within a demanding creative framework.
So where to start, when talking about visual campaign creation?
And above all: under what conditions can AI actually produce quality?
Myths vs realities - that's what we'll decipher in this article.
1. AI Has Never Been More Powerful. So Why So Many Disappointments?
We are living through a period of unprecedented technological acceleration.
Never have so many generative AI models — text, image, video — been developed and made accessible in such a short time.
On one side, increasingly powerful models.
On the other, an explosion of tools promising to "revolutionize" content creation in just a few clicks.
This dynamic creates a frequent confusion: we confuse technological progress with usage maturity.
Because while generative AI models (e.g., ChatGPT, Gemini, etc.) evolve quickly, organizations must deal with very real constraints: brand image, visual consistency, deadlines, budgets, internal validation.
Three Major Tensions for Marketing and Brand Teams
1. Technology That Evolves Faster Than Organizations
What works today may be obsolete tomorrow.
Consequence for brands: how to invest in an AI workflow without rebuilding your organization every six months (or less!)?
2. A Promise of Simplicity Largely Oversold
On LinkedIn and elsewhere, spectacular demonstrations multiply.
A "wow effect" visual presented as a result obtained in 5 minutes generates frustration and loss of confidence among teams who cannot reproduce these results alone.
3. An Explosion of Content Perceived as "AI Slop"
Under platform pressure, brands prioritize quantity over consistency.
Result: generic, interchangeable visuals that weaken brand perception instead of strengthening it.
The Real Challenge: Adopting AI Without Diluting Brand Image
In this context, the question is no longer whether to adopt AI.
The risk today is not using it — it's using it without a framework.
How to take advantage of this technological acceleration without falling into the trap of ease?
How to integrate AI into a demanding creative process, without sacrificing artistic direction or perceived quality?
This is precisely the point we will explore.
2. Is AI Really Less Qualitative?
For 2 years, platforms have been saturated with mass-produced content.
Under social media pressure, quantity has often taken precedence over rigor. Result: mediocre visuals proliferate rapidly, and are associated — rightly or wrongly — with AI.
This phenomenon is not new.
There have always been multiple levels of quality in visual creation.
We don't compare a Hollywood film to an amateur production. Not because the technology is different, but because the resources, time and expertise invested are not the same.
With AI, the reasoning must be identical.
AI is a tool. Not a guarantee of quality. No more than a professional camera makes a good photographer.
The Perception Bias: AI Is Presumed Guilty
A phenomenon often appears in the feedback I observe: when a visual is identified as "made by AI," it is judged more harshly.
Details are scrutinized closely. Imperfections become suspicious.
Elements that would never have been noticed otherwise become deal-breakers.
Interesting fact: when presenting 2 images without specifying their origin — traditional shoot and AI — judgment is first based on the emotion felt.
It's only after revealing the use of AI that perception changes.
This bias is deeply human.
It says less about the actual quality of images than about our relationship with technology.
Artificial Perfection vs Authenticity: A Poorly Framed Debate
A frequent criticism of AI visuals concerns skin treatment: too smooth, too perfect, without texture.
This criticism deserves to be put in perspective.
For over 30 years, tools like Photoshop have allowed smoothing, erasing and correcting textures. The quest for perfection did not begin with AI.
What has changed is our expectation.
Today, brands seek more authenticity.
This is where the real challenge lies: The quality of a visual is measured by its ability to be right — not by its level of technical perfection.
We Don't Notice the Campaigns That Work
The other bias that influences our perception: we remember AI campaigns that cause controversy. Those that trigger backlash or question brand consistency have disproportionate visibility.
Conversely, campaigns already integrate AI invisibly.
The visuals are right, consistent, emotional. They fulfill their role — without making noise.
And for a brand, this is often the best indicator of success.
When AI is used with method and rigor, it disappears in favor of the result. No one questions the creation process or the tool.
Only one thing is remembered: the quality of the visual result.
3. Why So Many AI Shoots Are Low Quality
Like any shoot, an AI shoot can fail.
And as with a traditional shoot, the reasons are rarely mysterious.
When the result appears "cheap," inconsistent or difficult for a brand to assume, AI is the ideal culprit.
In reality, the causes are almost always structural.
1. Using the Wrong Tool for the Wrong Use
Image and video generation models evolve very quickly.
Some excel in photorealism, others in atmosphere, still others in landscapes or product close-ups.
Thinking that a single model can meet all uses is a common mistake.
A model performing well for a lifestyle visual can distort a product.
Another, very precise on texture, will be unable to render a credible emotion.
To this is added an often underestimated issue: final resolution.
A visual intended for an Instagram post doesn't have the same requirements as a point-of-sale display, a subway platform or an OOH campaign.
Putting a team in front of tools without expertise is like entrusting the kitchen of a Michelin-starred restaurant to someone discovering the equipment.
The equipment doesn't make the result.
2. Sacrificing Artistic Direction in the Name of Speed
The most frequent cause of failure.
Under time and productivity pressure, some teams let AI "propose," hoping a good idea will emerge on its own.
A visual comes out. Then another. Then a third.
Except that even without a guideline, without intention, without a story, AI still gives a result.
Without clear artistic direction, and therefore unsurprisingly, the result is smooth and generic.
Backlash occurs, not because the visual is "made by AI," but because it breaks brand consistency. An AI shoot only works if artistic direction is central. It arbitrates and guarantees the fidelity of each visual to the brand DNA.
3. Producing Visuals VS Thinking a Campaign
Working with AI urgently, visual by visual, is a dead end.
In a traditional shoot, we never produce a single image. We build a series. A narrative. An overall intention.
With AI, the logic is identical.
If visuals are produced piecemeal, without an overall vision, consistency is diluted. Differences in style, lighting or framing become visible — and weaken brand perception.
The most successful AI projects are those conceived as real campaigns: a clear story, declined in several consistent visuals, rather than a succession of opportunistic images.
CONCLUSION
AI is neither a magic wand nor a threat to campaign quality.
What's problematic today is not the technology — it's how it's used. AI visuals perceived as "cheap" are not the result of poor model performance, but of a lack of framework: no clear artistic direction, no anticipation of constraints, no critical eye to arbitrate.
Conversely, when AI is part of a demanding creative process — with a guarantor Art Director, a structured method and a campaign vision — it becomes a powerful lever. A lever to produce faster, test more, iterate without sacrificing brand DNA.
The question is therefore not “should we use AI?”
But “under what conditions can it serve quality rather than dilute it?”
And the answer is clear: AI does not replace creative rigor. It makes it essential.