What AI slop teaches the creative industries in 2026

In an age where everything from the architecture we build to the adverts we scroll past is shaped by Generative AI, it’s becoming impossible to deny its power.

In a number of ways Generative AI is the evolutionary child of big data, a way to make the vast personal data banks productive rather than just useful. To give you some context to the scale I’m talking about: OpenAI reported in 2025 that ChatGPT had passed 700 million weekly users, and industry estimates now put the number of AI-generated images produced each day at well over a billion. Staggering numbers if you can begin to wrap your head around them. This avalanche of machine-made content means a growing share of everything we see, read and watch now has generative AI somewhere intwined into how it was produced.

The birth of these tools has helped to augment our lives, revolutionising the world’s biggest industries of Education, Finance, Gaming, Healthcare, Automotive and more. The Internet of Things has also allowed us to collect exponentially more invaluable personal data, feeding the models that now sit on top of it. Generative AI does have the power to change things for the better.

In the design world, Generative AI towards the end of of 2025 began to weave its way in, moving far beyond the recommendation engines and ranking systems that quietly shaped feeds and search results for years before it. In the first half of 2026, we have seen the acceleration of AI adoption accelerate exponentially.

As James Kirkham, co-founder of digital strategy agency Holler London, put it back in 2020:

“Where once the creative sat aside from those who were listening and analysing, only being briefed at a later date, now the creative sits alongside those listening to communities, mining data and forming thoughts based on analysis, interaction and algorithms. Speed of response is now so vital that the algorithm at the heart of the cycle can trigger a creative cue that will be tested and put up online in a matter of minutes.”

Six years on, that’s no longer a forecast. A clear example of this speed-first approach is Coca-Cola’s AI-generated Christmas advert, produced almost entirely using generative video tools rather than a traditional shoot, letting the brand turn around dozens of shot variations in a fraction of the usual production time. Efficient, yes. But these tools are much more open to misuse than any human process could be, and in a world where digital already accounts for roughly half of global ad spend, that’s worth taking seriously.

Regulation is starting to catch up (the EU’s AI Act, a growing string of copyright rulings) but enforcement remains patchy, and experts still worry that Generative AI concentrates too much control in the hands of a small number of platforms, perpetuates bias, narrows genuine choice, and crowds out the creativity and serendipity that made the work worth looking at in the first place. These fears are not just conjecture. The Coca-Cola ad was widely mocked online as ‘AI slop’, criticised for looking uncanny and lifeless, and became one of the clearest public examples yet of a brand’s reputation taking a direct hit for leaning too hard on the machine. It won’t be the last.

There’s a second, quieter version of the same problem. Generative models are trained on enormous volumes of existing creative work, much of it used without the knowledge or consent of the people who made it. Getty Images’ ongoing case against Stability AI, and a growing list of similar claims from authors, illustrators and musicians, allege exactly this kind of extraction: value taken from human creativity without permission or payment. It’s a live legal question, and it’s already reshaping how carefully serious brands and studios approach using these tools.

Challenges are being faced in creative industries by these tools in a different way too. The way Generative AI predominantly works is by studying and remixing what has already been made. The internet gave birth to a whole new facet of creativity, code pushing at the boundary of rules and producing surprising results (anyone remember Flash? I’m proud to this day of the Pirelli Tyre Portfolio I built completely in Flash that still defies logic!).

However, at its most reductive, Generative AI is a mirror held up to everything that already exists: creativity flattened into an average, a plausible next word, a plausible next pixel. That output then shapes our aesthetic expectations around feedback loops, an endless supply of visuals and copy that seem to match what we already respond to.

Michael Veitch made a version of this point in 2020, and it applies just as well, if not more so, to the generative tools built since:

“Most algorithms work by suggesting things you might like based on what you already like; they don’t account for something you might not currently like, and will have to force yourself to, but will one day love.”

Fashion designer Gretchen Jones, the former fashion director of womenswear at Pendleton Woolen Mills, found her role becoming more ‘defensive’ than proactive under the weight of data long before generative AI arrived, and the dynamic she describes has only intensified since:

“I was fighting against big data that would often negate the creative design directions,” Jones said. “I was speaking through my gut and they had paperwork that could prove another black mock turtleneck was the thing that sold. But rarely can a customer tell you what they want that hasn’t been created yet, and that was stifling my ideation.”

Digital design has found itself under a similar pressure, and generative UI tools risk making it worse. A user is treated as a homogenised set of past actions, an anonymous data profile that demands everything be relentlessly ‘user-friendly’. As Patrick Burgoyne, CEO of Creative Review, put it in a widely shared piece:

“For so long branding has preached the doctrine of differentiation, now it seems that in the screen-centric world of ‘customer experience’ our loftiest ambitions are for our brands to be sleekly efficient, offering no surprises (and certainly no delight), just the promise of frictionless ease of use.”

What people crave most from creativity and art is something new, the element of surprise. Abstract art, modernist sculpture, a new perspective. Marcel Duchamp’s urinal, Andy Warhol’s Campbell soup, Guernica by Pablo Picasso. When we respond to viral memes on social media it’s because they produce something unexpected, leveraging the relationship of surprise and humour.

How then to break out of this AI-driven loop of sameness

The signs are there that companies are beginning to seek out individuality again, this time as a direct reaction to AI sameness. Cara, the artist platform that explicitly bans AI-generated work, grew fast in 2024 as illustrators and photographers went looking for somewhere their craft would still be recognised as their own. A wave of brands have started treating ‘made without AI’ as a badge of honour rather than a limitation, leaning into visible hand-lettering, imperfect illustration and unmistakably human quirks precisely because they read as the opposite of machine-smooth. Good design is still a piece of magic, and increasingly the magic trick is proving a human made it. Clients are reevaluating the understanding that they know how to reach their customers; it’s no longer enough to get in front of them, there needs to be something new that generates true, long lasting engagement. Do you remember the Gorilla and Phil Collins advert (launched by advertising agency Fallon London) which relaunched the reputation of Cadbury in the UK? Nearly two decades on it’s still the reference point people reach for, because nobody has managed to fake that kind of surprise since.

This is where we think the useful distinction actually sits: not for or against Generative AI, but where in the process it belongs.

At Studiomade we use these tools every day, for early exploration, for producing options quickly, for handling the unglamorous production work that used to eat a brief’s whole timeline. We extend and adopt it for streamlined prodict prototyping, and code implementation.

What we don’t hand over is judgement: the decision about which of those options is actually right for a FinTech founder’s first-time investor, or a HealthTech brand’s most anxious patient, or an asset manager’s most sceptical board. That decision has to come from strategy and human insight, because it’s the one place these tools still can’t reliably go. They can produce the average of everything that’s been made before. They can’t tell you which idea is true for your specific client, in this specific market, right now.

Clients don’t need to disregard the data, or the tools. But the trust placed in a Generative model should go hand-in-hand with the trust placed in the people directing it. Used well, Generative AI can help drive us to better solutions, joining data and creativity together rather than letting one flatten the other.

At Studiomade we’re moving past the old, uncritical treatment of these tools and looking to keep human creative and strategic judgement firmly in charge of our creative output. We’re looking to work with confident clients who are searching for something new to motivate real behavioural shifts, not just more of the same, produced faster.

If you’re working through what Generative AI should and shouldn’t touch in your business, we’d like to talk.

Studiomade is a strategic design studio. We shape products and brands people trust in the AI era, for Fintech, HealthTech and Investment. Compliant, credible and adopted.