If Your AI Advert Looks Like Everyone Else’s, What’s the Point?

There is a particular type of social media graphic that I am starting to recognise before I have even read it. It has too much text, an odd mixture of fonts, a slightly over-designed background, colours that do not quite belong to the business and a layout that somehow feels familiar even though I have never seen the advert before. Then I look a little closer and realise why. It is another AI-generated social media poster.

The strange thing is that the businesses using these graphics are often trying to achieve exactly the opposite effect. Advertising is supposed to attract attention, make a brand easier to recognise and give people something memorable enough to associate with the organisation behind it. Yet if hundreds of businesses are using the same generative tools to create their graphics, and those tools keep producing variations of the same visual style, we risk creating an enormous amount of advertising that simply blends into everything around it.

This is the AI slop problem that interests me. It is not an argument that AI is becoming too good at design. Quite often, I think the opposite is true. Some of the social media graphics being generated by tools such as ChatGPT are simply poor pieces of advertising. They contain too much information, weak hierarchy, generic visual choices and inconsistent branding. More importantly, they often look so obviously AI-generated that businesses from completely different industries begin to look as though they are being marketed by the same designer.

That seems to defeat one of the fundamental purposes of advertising.

Advertising should help a business stand out, not blend in

Imagine walking down a high street where every shop had been given the same colour palette, the same typeface and broadly the same window display. The businesses might sell completely different things, but visually they would begin to merge into one another. You would struggle to recognise which shop you had seen before, which one somebody had recommended or which one was responsible for a particular advert.

We would immediately recognise that as a branding problem in the physical world. Yet something similar is starting to happen online.

A restaurant asks AI to create a poster for a weekend offer. A gym asks it to promote a membership campaign. A trades business wants an advert for a new service. A local event needs a social graphic. The prompts are different, but the instructions often contain broadly similar words such as professional, bold, eye-catching, exciting or premium. The resulting images are technically different, yet the visual language can be remarkably similar.

There are familiar gradients, glowing effects, oversized headings, text boxes, generic stock-style people, dramatic lighting and layouts that try to communicate far too much at once. The business may have saved an hour of design time, but it has also created something that could easily have come from almost any other organisation using the same technology.

That is not distinctiveness. It is visual convergence.

An advert is not a leaflet squeezed into a square

One of the biggest problems I see with AI-generated social graphics is the amount of information they try to include. There can be a headline, subheading, date, price, website address, phone number, promotional message, explanatory text, call to action and several other pieces of information competing for attention inside one relatively small image.

Everything has been made important, which means nothing feels particularly important.

Good advertising usually requires much harder choices. What is the single most important thing somebody needs to understand? What should attract their attention first? What needs to be remembered after they have scrolled past? Which information can live in the caption, website or landing page rather than being forced into the creative itself?

A social media advert does not have to contain every piece of information somebody could possibly need before making a purchase. Its job might simply be to make somebody stop, recognise the brand and become interested enough to take the next step.

This is where AI can make weak design dangerously easy. A person with little design experience can ask for a promotional poster, receive something that appears superficially finished and assume the creative work has been completed. The difficult decisions about hierarchy, typography, spacing, composition and communication have been outsourced to the system, but there is no guarantee the decisions are particularly good.

The ability to produce something quickly should not be confused with the ability to produce something effectively.

Generic design is a branding problem

This becomes even more important when we think about distinctiveness.

Byron Sharp and the Ehrenberg-Bass Institute have written extensively about the importance of distinctive brand assets. Colours, logos, shapes, characters, typography, sounds and other recognisable cues can help people identify a brand and retrieve it from memory. Over time, those assets become valuable because customers learn to associate them with a particular organisation.

That requires consistency.

If a business has spent years building recognition around a particular colour palette, visual style and set of brand assets, allowing an AI tool to reinvent the design language every time somebody needs a Facebook graphic works against that accumulated memory.

The poster might look attractive in isolation, but if it uses colours customers do not associate with the business, typography that feels completely different from the website and imagery with no relationship to anything else the organisation produces, it is not reinforcing the brand.

It is starting again.

Now multiply that problem across hundreds of AI-generated pieces of content and the business can gradually lose the very things that once made it recognisable.

This is why I think the rise of AI-generated advertising makes brand discipline more important rather than less important. The easier it becomes to create something new, the more important it becomes to know what should remain consistent.

The tool should follow the brand

There is nothing inherently wrong with using AI to create advertising. It would be strange to reject a tool simply because it makes parts of the creative process faster or more accessible. The problem comes when the technology begins making the brand decisions instead of supporting them.

The process should begin with the identity of the business. What are the recognised colours? Which fonts are used? How should photography look? Which logo treatment is correct? Are there established shapes, patterns, icons or other distinctive assets? What does the brand sound like, and what does it definitely not look like?

Only then should AI enter the process.

A useful instruction might provide the technology with those constraints and ask it to explore ways of communicating a particular message while protecting the established identity. The output can then be judged against the brand rather than judged simply on whether it looks vaguely professional.

This changes the relationship completely. Instead of asking AI to invent the advertising, the marketer is using AI to help execute an existing creative strategy.

The distinction matters because brands become distinctive through repetition and consistency, not through repeatedly surprising themselves with a completely different visual identity.

AI should follow the brand. The brand should not follow AI.

Cheap creative can still be expensive marketing

One of the attractions of AI-generated graphics is obvious. They cost almost nothing to produce. A small organisation that might never have paid for a designer can suddenly create endless posters, graphics and promotional images without adding anything substantial to the marketing budget.

From a production perspective, that looks like an enormous saving.

From a marketing perspective, the calculation is more complicated.

An advert can be cheap to make and still be expensive if it fails to do its job. If money is spent promoting a generic image that nobody notices, or if thousands of people see a piece of creative without remembering which organisation it belonged to, the low production cost is not particularly meaningful.

The cost of advertising is not simply the amount spent creating the image. There is also the cost of the media, the opportunity to communicate with the audience and the attention that has been purchased or earned.

If a business pays to put an advert in front of 20,000 people, the creative deserves more thought than simply accepting the first generated image because it appeared in thirty seconds.

This becomes even more important on social media because the environment is brutally competitive. The advert is surrounded by photographs of friends, videos, news, entertainment, other businesses and an increasing volume of AI-generated material. The creative has fractions of a second to earn attention.

Looking like everything else is not a particularly strong strategy for achieving that.

AI is creating more advertising, not more attention

Generative AI has removed much of the friction involved in creating content. Businesses that previously produced two social graphics a week can now theoretically produce twenty. Marketing teams can create endless variations, change headlines instantly and generate more imagery than they could ever realistically use.

What AI has not created is any additional human attention.

People still have the same number of hours in the day and roughly the same willingness to look at advertising. If every business dramatically increases the amount of content it produces, the result is not necessarily that everybody receives more attention. It simply means there is even more material competing for the attention that already exists.

This is why I find the obsession with content volume increasingly strange. The fact that technology allows a business to publish more does not automatically mean customers want to consume more from that business.

In an environment flooded with disposable creative, restraint could actually become an advantage. One strong advert that clearly belongs to the brand, communicates one thing well and appears consistently might be more valuable than thirty generated graphics fighting with each other for attention.

AI reduces the cost of production, but it does not reduce the need for judgement.

Some of the best brand assets already exist

There is another irony here, particularly for smaller and local businesses. Many organisations are using AI to manufacture generic imagery when they already possess visual assets that competitors cannot copy.

A gym has its actual members, trainers, building, equipment and community. A restaurant has its chefs, dishes, customers and interior. A local event has a real location, real atmosphere and real people attending. A trades business has completed projects, vehicles, employees and customers.

Those things are distinctive because they genuinely belong to the organisation.

A slightly imperfect photograph of an actual gym member may do more for the brand than an immaculate AI-generated fitness model because the real photograph contains cues that belong specifically to that business. The walls, equipment, clothing and people all reinforce the organisation behind the advert.

The same applies to local businesses using distinctive colours, uniforms, packaging, signage or locations. Those assets are already doing branding work. Replacing them with generic generated imagery can actually remove valuable recognisable information.

This does not mean every advert needs to be a badly lit phone photograph. Professional design and photography still have enormous value. The point is that specificity matters. Creative should contain things that help someone understand who is speaking.

AI has made taste more important

One of the most interesting consequences of generative AI is that the ability to physically produce marketing material is becoming less valuable on its own. Almost anybody can now create an image, write a headline, generate a video concept or produce a basic layout.

The skill increasingly lies in knowing whether the result is any good.

That requires judgement. Somebody still needs to recognise that the poster contains too much text, that the hierarchy is wrong, that the colours do not belong to the brand or that the concept looks exactly like the last fifty AI graphics in the feed.

That is creative direction.

It is why designers, marketers and brand people are not suddenly irrelevant simply because a machine can generate a finished-looking image. If anything, the abundance of output increases the value of somebody who can reject most of it.

The important creative question becomes less about what can be generated and more about what deserves to be published.

The first result should not automatically become the advert

This may be the simplest practical lesson from all of this. Generative AI produces something that feels finished incredibly quickly, and that creates a temptation to treat the first usable result as the final creative.

That would be a strange way to approach almost any other form of advertising.

A designer would usually explore options, question the hierarchy, refine the copy, adjust spacing and ensure the work fits the wider brand. A creative team might reject dozens of ideas before choosing a direction. The process exists because the first idea is rarely automatically the strongest one.

AI should not remove that scrutiny.

If anything, because generating alternatives is now almost free, marketers should become more demanding. The fact that ten options can be created in a minute should mean we are more willing to reject mediocre work rather than lowering our standards because the machine produced something quickly.

Technology should make experimentation easier. It should not make acceptance easier.

If it looks like everybody else’s advert, what has it achieved?

The biggest danger of AI slop is not that somebody occasionally publishes an ugly graphic. Bad advertising existed long before generative AI and will continue to exist long after the technology improves.

The more interesting problem is what happens when businesses increasingly hand their visual communication to the same systems and those systems begin producing recognisably similar creative.

Advertising is meant to help brands get noticed and remembered. Branding is supposed to create associations that make one organisation easier to recognise than another. If AI leads thousands of businesses towards the same fonts, layouts, visual effects and generic imagery, we are using technology to achieve almost the exact opposite.

The answer is not to ban AI from the creative process. It is to use it with more discipline. Start with the brand, decide what the communication needs to achieve, reduce the message to what actually matters and then use technology to help execute the idea.

Most importantly, somebody still needs to look at the final creative and ask a very human marketing question.

If I removed the logo from this advert, would anybody know it was ours?

If the answer is no, generating it in thirty seconds is not much of an achievement.