How to Use AI for Creativity, Not Conformity

Super Bowl. The greatest day in the calendar for Advertising. It's an embarrassment of riches, promotional excess and big bets. It’s glorious.

My second favourite thing after the great Football Advertising Party is reading and writing all about it. The hot takes. The bold claims. The annual flurry of loosely qualified assertions about how the landscape has forever changed. We in advertising never waste a good opportunity to declare a turning point. Naturally, we gorged ourselves. And although I might just be getting a bit older, both Caro and I couldn’t help but notice this one felt a little less sparkly.

Back on home soil, Caro was watching with equal parts ad-nerd enthusiasm and Patriots-induced heartbreak. Her read was immediate: the game felt a bit flat. And so did some of the ads. The sparkle was missing.

Not all of it, of course. There were proper bright spots.

Liquid IV leaning into a hydration truth most of us have quietly validated with a sideways glance. I suspect very few AI tools would confidently surface that particular insight if prompted. Kellogg’s William Shat Raisin Bran fibre reveal. Annoying, until it clicked. Then genuinely smart. Novartis weaving tight ends into preventative screening with surprising elegance.

Bathroom humour. Fibre jokes. Locker room puns. Juvenile? Slightly. Human? Completely. And maybe that is the point. Because the human bits cut through.

But let's talk about the very well-paid elephant in the room. Celebrities.

This reached a fever pitch last year and although their inclusion is down from 68% of all ads to 62%, it’s clear brands are still hedging risk and distinctive efforts with these folk. There’s nuance here. We can probably expect the number to keep falling. Others have addressed the issue more comprehensively. But the rule I was taught (and still try to stick to) is simple: fame is a byproduct, not a strategy.

Caro put it more bluntly: “if the idea only works because the celebrity is famous, it’s not an idea. It’s insurance.”

Insurance isn’t inherently bad. Insurance can feel safe. But safe rarely makes history.

And speaking of safe… Let’s talk about the other buzzword of the night, besides “field goal.” AI.

Interestingly, there may not have been a huge amount of AI used in the ads themselves (beyond Svedka, who at least admitted it). But there were certainly a lot of ads for AI. If Caro’s tracking is correct, we saw:

Anthropic: their first ever Super Bowl spot, clearly differentiating itself in the LLM race.
OpenAI: a 60 national Codex spot, plus regional ads showcasing small businesses leveraging ChatGPT.
Google Gemini.
Microsoft Copilot.
Amazon (Ring and Alexa. Framed as helpful, but among Caro’s watch party crowd, more than a few felt “creepy”).
Genspark, which sparked a different kind of room reaction: “Is this taking my job?”
Salesforce promoting Slack AI.
ai.com, which left many wondering what, exactly, it does.

From our vantage point at a scrappy AI company from Down Under, it was fascinating to watch the category openly confront itself on the biggest stage in culture. It is a competition, after all. And although many in our space are still learning how to “Super Bowl” (present company included), the open confrontation across tech and beyond was refreshing.

At the risk of alienating the EBI crowd, acknowledging your competition doesn’t simply donate attention to the category leader. Positioning still matters.

While I admired the spirit of Anthropic’s work, it reminded us of something Reed Hastings once said about Netflix: “No advertising coming to Netflix. Period.” Less than two years later, that position shifted. As the major players in AI fight to become the homepage of the internet, there are probably lessons to be learned from the streaming wars.

So yes. A few observations. A few trends. A few stats. But so what? Risk is so what.

Risk underpins every great Super Bowl ad in history. Every great campaign. The work we still talk about didn’t come from playing it safe. It came from someone taking a calculated leap. This year, outside of a few friendly swipes, felt more than a bit cautious. One of the more frustrating paradigms in Advertising is the idea of maths versus magic (as though we have to choose between data and creativity). Realistically, you need both. It’s a false dichotomy.

Magic i.e. creativity is, fortunately, much more difficult to train a language model on. It’s an innately human pursuit that the machines we’re currently enamoured by aren’t very good at on their own. They recognise patterns and desperately avoid risk. There is an answer to this, or at the very least, a wealth of inspiration.

If you want to see something risky, have a look at our Super Bowl spot. Apologies, experiment (for many reasons, most of which are legal). And if you have an appetite for risky work, please reach out - so do we.

We’ve built something that inspires you to find the risks worth taking.

The kind we both kept wishing we’d seen more of this year.

We wanted to lean into this and decided to dramatise the problem instead of explaining it.

We picked an ad, a recent spot from OpenAI, and flipped the ending to make a point about what happens when everyone uses the same tools – they end up getting sent to the same destination both in real life and creatively.

Our original thought was to see how quickly we could conceptualise this approach and to mock up the concept. What we didn’t expect was how quickly the work would become uncomfortably close to the original.

The acceleration of LLM adoption across marketing and creative industries over the past couple of years has been remarkable. These tools are being woven into workflows everywhere – from concepting to copywriting to production.

When deployed thoughtfully, generative AI can push creative boundaries and help teams explore territory they may never reach by themselves, or help to short-circuit work that could take days or weeks in that creative exploration to help teams move more quickly.

But LLMs are converging – and not enough of us are paying attention.

Recent research from MIT and other institutions — published as the “Artificial Hivemind” study — documents something many of us have felt but struggled to quantify: these models are gravitating toward remarkably similar outputs, even in open-ended scenarios where countless valid answers should exist.

The simple test is just to ask your LLM to generate a random number between one and 10. With 95%+ accuracy you will get a seven every single time regardless of the model, where you live or your chat history. And while there are parallels with humans who also pick seven the most often, at 28%, of the time, LLMs are amplifying the average – from 28% probability to 98%. Doesn’t that tell you everything you need to know?

This isn’t about people using the technology incorrectly. It’s about how the models themselves are designed. They’re trained on patterns and they optimise for coherence and probability. They deliver what’s most likely, not what’s most interesting or unexpected.

And when everyone’s drawing from the same well, standing out becomes exponentially harder.

Which brings me back to our experiment.

Firstly, credit should go where it’s due — our production partners absolutely nailed the brief. Frame composition, lighting, movement – all of it was eerily accurate. Too accurate.

The result raised immediate questions about likeness, intellectual property and how effortlessly these systems can blur ethical lines without anyone deliberately trying to.

We wanted to get close to the original and the technology made it almost effortless to get there.

It crystallised the convergence problem in a way that felt impossible to ignore. If we could recreate a high-production advertisement this accurately, this quickly, with relatively little iteration – what does that mean for originality across the board? What does that mean for our craft? And how busy are copyright lawyers (or their bots) going to be in the years to come?

So we kept playing and pulled the work right back into a safer zone. We needed to make it less perfect. And as anyone in the industry knows, adjusting the brief halfway through a campaign means deadlines and costs often get blown out.

The final version we ended up with was a bit rougher around the edges, but it was necessary.

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Another thing that became obvious through this process is how easy it’s become to mistake polish for purpose.

AI-generated content now rivals or exceeds human-created work across massive portions of the web. By late 2024, the balance had already tipped in many categories. That stat alone isn’t the problem – the problem is why.

Speed and frictionless production are replacing deliberation. Teams are shipping work that looks finished even if it took minutes to create instead of days. The question has now shifted from “Is this the right direction?” to “Is this ready to publish?”.

When creating something that looks finished, that takes minutes instead of days, we risk conflating output with outcome, volume with value and “good enough” with genuinely good.

Here’s where it gets tricky for our industry specifically.

Marketing has always been about standing out and saying something in a way that cuts through. That’s the craft.

But if the tools we’re using to generate ideas are all trained on the same corpus, optimised for similar outputs and rewarding safe, predictable thinking – how do we avoid becoming indistinguishable from each other?

The answer isn’t to abandon AI. That ship has sailed, and frankly, I think it’d be the wrong move anyway. The answer is to fundamentally change how we interact with what these systems give us.

Our experiment forced us to reckon with something uncomfortable: the first thing AI gives you is almost never the right thing to run with. It’s a spark but it’s not the answer.

Here’s what that means in practice:

Interrogate everything.

The moment something looks finished, that’s when you need to push harder. Ask what’s been smoothed away, what assumptions the model made and what directions got optimised out in favour of coherence. The rough edges are often where the truth lives;

Resist the path of least resistance.

Just because you can generate a hundred options in ten minutes doesn’t mean you should use the first one that’s good enough. Speed is valuable but only if it’s pointed in an interesting direction;

Make imperfection intentional.

We deliberately pulled our final version back from perfection because perfection wasn’t the goal – purpose was. Sometimes the most polished version is the least honest one.

The advertising industry has always been vulnerable to trends, templates and formulas. We’ve dealt with this before – when everything looked like an Apple ad, when every brand tried to sound like Dove, when “purpose-driven” became a checkbox instead of a commitment.

AI accelerates that tendency. It makes it easier to drift toward the middle. But when used with intent, it can help us generate unexpected combinations and surface connections we’d miss.

So yes, we used AI to recreate an LLM ad to criticise how LLMs create sameness. The irony isn’t lost on us. In fact, through play, it became the point. But, sometimes, to make people aware of the danger, you need to take them there. Because if we let convenience override craft, if we confuse ease with excellence, we won’t just end up with boring work – we’ll end up in a boring industry.

And none of us got into this business for that.

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This article first appeared on Mumbrella, one of Australia’s leading media and marketing industry publications. Read the original piece by Pip here.

SYDNEY, Australia – 27 January 2026: Springboards, a creative tool built to inspire creativity in advertising, has released a new piece of AI-created work that puts the spotlight on large-scale generative models by revealing how quickly these systems can produce polished advertising output and how easily they drift into unsafe or unoriginal territory.

‘The Dangers of AI’, the latest experiment drop from Springboards, involved taking inspiration from an existing ad to create a new one, showing how quickly these models can generate work that appears finished but frequently crosses copyright lines and collapses into familiar patterns.

CEO and co-founder Pip Bingemann said the team wanted to put a spotlight on the dangers of AI when used in creative practices. “We’re very aware of the irony here. We’re dramatising the problem of large models sending everyone to the same place by deliberately using a technique that exposes how easily they drift into infringement. But sometimes the only way to show the danger is to step into it. This work is about making those risks visible, not pretending they don’t exist."

The project makes clear the challenges agencies and advertisers now face as generative models become more widely adopted. These systems can produce near-finished creatives in minutes, yet they also drift into copyright-sensitive territory, replicate distinctive likenesses and collapse different directions into outputs that feel largely the same. 

Springboards created the piece to highlight the gap between what generic large language driven models can generate and what agencies actually need, showing how the speed of these tools often comes at the cost of originality, safety and true creative variation.

This widening gap between speed and originality underscores the role of Springboards. Founded by Pip Bingemann, Amy Tucker and Kieran Browne, Springboards is used by more than 200 agencies and companies worldwide and was built to help creative teams explore a broader range of ideas without sacrificing the craft, judgement and originality essential to great work.

CMO and co-founder Amy Tucker said the project reaffirmed why dedicated creative tools matter: “This experiment really showed the dual reality. The models are powerful, but they narrow creative possibilities as much as they expand them. Creativity needs tools built for the craft, not systems that smooth every idea into the same outcome.” 

“That’s why at Springboards, we aim to be an enabler, not the final answer. Springboards gives teams the variation and space they need to unlock new creative directions while keeping the taste, judgement and originality human.”

For agencies, this work serves as a wake-up call for 2026. Generative tools are accelerating, but the creative standard is not. The industry does not need faster shortcuts; it needs stronger ideas supported by tools built specifically to elevate the work.

CREDITS

Client: Springboards

Strategy & Concept Development: Springboards inhouse team

Creative Inspiration Tool: Springboards.ai

Production & Delivery: Vinne Schifferstein, Marie-Celine Merret

AI Artist: Bob Connelly

Sound Design & VO: Jaron Ransley

About Springboards

Springboards is an AI-powered platform built to inspire creativity in advertising. The platform empowers teams to explore more ideas, without sacrificing the craft of great work. Founded by industry veterans Pip Bingemann, Amy Tucker, and Kieran Browne, Springboards has already partnered with 200+ companies globally. For more information, visit Springboards or contact hello@springboards.ai.

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