Floating on the sea of sameness

85% of marketers use AI for content creation, according to a 2025 survey by CoSchedule - and I’d wager that figure is far higher in2026. The rise is understandable: the demands on copywriters and marketing teams are increasing exponentially. There are more channels to manage than ever before, more points of contact, more campaigns - the requirements of the job in 2026 look radically different to a decade ago. AI offers a neat solution. It can generate competent copy quickly, and help overworked marketers manage the ever-changing (and ever-increasing) needs of their clients.

 

But an overreliance on AI poses a problem. When brands across the world - from the smallest Mom-n-Pop to the largest multinational conglomerate - use the same few basic LLMs, then marketing copy globally starts to converge on the same bland tone and tired language. We’re already seeing it. It’s almost as if every marketing team in the world has gone ahead and hired the exact same copywriter. And consumers are starting to notice.

 

 

AI’s linguistic tells

The em dash epidemic was the canary in the coal mine. A piece of relatively niche punctuation suddenly started cropping up everywhere: Twitter, Slack messages, LinkedIn posts, and in the chat on Teams meetings. Quickly, it was discovered that ChatGPT favoured it heavily. Its appearance in a block of text became a tell-tale sign that an LLM had been involved at some stage of the drafting process.

 

Since then, more of AI’s linguistic tics have become evident - and they might be more familiar than you think. Over-employed rhetorical tricks such as "it’s not X, it’s Y," a deep obsession with the rule of 3, and a kind of empty optimism are all LLM mainstays. While these may seem innocuous to start (and, in isolation, can be effective writing techniques), they combine to create a homogeneous AI copy-soup.

 

Even subconsciously, your audience picks up on these tricks, and their eyes glaze over. They grow bored. And what seemed such a great idea to start - a few paragraphs of competent prose generated in less than 10 seconds - suddenly becomes ‘slop’. The audience you’ve spent so long building, who you have spent so long collating data on, and who you’re paying to reach, start to tune you out.

 

 

The sea of sameness and the challenge for marketers

Brands are floating in a sea of sameness. So what can they do to stand out? It’s a tough question. The reality is that AI does solve many problems facing marketers today - and turning your back on the technology altogether is only going to hurt you in the long run. If you’re competing with others who are able to handle multiple channels and put out effective copy at scale, AI is the only way to meet that demand. Doubling your headcount won’t help anymore. But just unthinkingly turning to AI won’t work either: the short-term gains in efficiency are quickly undermined when your voice becomes indistinguishable from every competitor in your sector.

 

But it only gets more complicated for marketers. Hyper-personalisation also means that brand voice doesn’t exist in the same way it did a decade ago. The sheer amount of data on hand about customers, broken down into demographics and niche subcategories, means that brands may want to communicate differently to different customers. Instead of one blanket ‘brand voice’, brands are increasingly looking at dozens, if not hundreds, of voices that offer distinct emotional tones, while cohering to a broader overarching tone.

 

Different linguistic patterns resonate differently with different audiences. What Gen Z want from a brand voice is entirely different to what a Boomer might want, for example. What lands with one demographic falls flat with another.

 

So, we’re faced with a paradox. Brands need to maintain a distinctive voice whilst also adapting that voice across dozens of segments, hundreds of campaigns, and thousands of individual touchpoints. It’s the content bottleneck problem magnified. Manual copywriting simply cannot scale to meet this demand - but nor can generic AI tools that flatten everything into the same bland corporate-speak.

 

 

How brands can stand out while sounding like themselves

We’ve already established why abandoning AI isn’t the solution. Instead, brands need to be more intentional about how it’s deployed - they need to cultivate an infrastructure built specifically for maintaining linguistic identity at scale. That means training models on brand-specific data, establishing systematic approaches to tone and voice, and crucially, predicting what will actually perform before it goes live. Competent prose can only get you so far.

 

We’ve reached an inflection point. Brands that continue relying on prompt engineering and off-the-shelf LLMs will find themselves increasingly indistinguishable from the competition. Those that invest in proper content infrastructure - systems that can maintain brand voice whilst scaling across channels and personalising for individuals - will be the ones that don’t sink into the sea of sameness.

 

The question isn’t whether to use AI for copywriting. That ship has sailed. The question is whether you’re using AI that makes you sound like everyone else, or AI that helps you sound distinctly like yourself.

 

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Toby Coulthard is Chief Product & Growth Officer at Jacquard

 

Main image courtesy of iStockPhoto.com and akinbostanci