José Antonio Martínez Aguilar, Global CEO Of Making Science, On Ways AI Is Transforming The Creative Landscape

José Antonio Martínez Aguilar (JAMA): The core issue is scale. Marketing today demands a relentless volume of content, across formats like display banners, reels, stories, connected TV, you name it. But while formats multiply, resources don’t. Creative teams are often expected to churn out more assets, faster, and for more platforms, without proportionate increases in budget or time.
 
What ends up happening is that we see beautiful one-off assets that are rarely optimized beyond the campaign’s launch. Updates, if they happen at all, are slow, subjective, and disconnected from real-time performance data. The traditional process just doesn’t keep up. And when 50% of your campaign's success is driven by the creative, that disconnect becomes a real liability.
 

José Antonio Martínez Aguilar (JAMA): Creative fatigue is a performance killer. When the same content is shown repeatedly across different platforms without adaptation, it becomes invisible. Consumers start tuning out. Engagement drops. Performance weakens. And brands often don’t realize it’s happening until results have already dipped.
 
It is not just audiences that suffer from fatigue. Marketers and creative teams are drained by the constant push to make more, with the same limited creativity. It’s a lose-lose. Ultimately, a creative that doesn’t evolve with the campaign becomes a blocker to growth.
 
Are creative assets effectively used throughout campaigns?
 
Honestly, no. In most cases, creative is still treated like a one-time setup; created at launch and then left untouched, even as campaign performance shifts. Algorithms often work with limited or poorly structured content, which means a huge chunk of potential impact is being left on the table.
 
Most assets aren’t even being fully utilized by the platforms. If your creative doesn’t meet specific, often-opaque requirements or offer enough variation, the algorithms deprioritize it. We estimate up to 80% of creative impact can be lost before an ad is even seen, which is a staggering inefficiency.
 

José Antonio Martínez Aguilar (JAMA): If your creative isn’t structured correctly, platforms won’t serve it, no matter how good it looks. Algorithms are built to test, learn, and optimize based on performance signals. If there’s not enough variation or clarity in your assets, the learning stalls.
 
Poorly structured creatives also limit the system’s ability to match the right message to the right audience. That lowers ad relevance and expected engagement, which in turn drags down your total value score in auctions. The result is fewer impressions, lower performance, and higher costs. Structurally weak creative is a hidden tax on your entire media investment.
 

José Antonio Martínez Aguilar (JAMA): AI flips the script. Instead of relying on a fixed set of creatives, AI, especially performance-optimized creative AI, works dynamically. It generates, tests, and evolves creatives in real time based on platform feedback and campaign results.
 
For example, at Making Science, our Creative Hub technology in our ad-machina platform considers existing creative and product feeds and uses generative AI to produce countless variations, tailored by format, platform, audience behavior, and even search intent. And it’s based on actual performance data: which colors work, which headlines convert, which cognitive biases resonate.
 
It’s not about replacing creativity; it’s about amplifying it and ensuring it contributes directly to performance while maintaining full brand control. We call it creative for performance, and it’s a foundational shift, especially when you have skilled workers guiding and overseeing it.
 
As formats multiply—stories, reels, carousels, emails—how should marketers prioritize where to apply AI-enhanced creativity first?
 
Start where the pain is greatest and the return is highest. For most brands, that’s in high-volume, high-velocity channels, which are incredibly dynamic, and where the creative demand is constant.
 
That’s why ad-machina was built to automate creative generation in platforms such as Google Search, Meta, and Performance Max. That’s where the creative has the biggest opportunity to influence results, but where manual production struggles to keep up. Once you’ve got AI working effectively there, you can scale into video formats, social, and even email, using the same performance-driven approach.
 
The priority should always be this: where can AI have the greatest impact on relevance, engagement, and efficiency? That’s where you apply it first.
 

José Antonio Martínez Aguilar (JAMA): Personalization is key to fighting fatigue. When content feels relevant, as though it’s made for you, it cuts through the noise. But traditional personalization is resource-heavy and, therefore, often limited to simple swaps like name or location.
 
AI changes that. We can generate tailored messages for text and creative based on intent, behavior, and real-time platform signals. If someone searches for “affordable yoga pants,” the AI generates copy that speaks directly to that. If another person searches for “premium activewear,” they get something else. It’s the same product, but a totally different experience.
 
And because it’s all algorithmic, it scales effortlessly. Thousands of variations can be created and served across every major channel, without exhausting your creative team. That’s true personalization at scale, and it’s essential in today’s environment.
 

José Antonio Martínez Aguilar (JAMA): First and foremost, transparency is critical. Brands should be clear about how AI is being used, especially when it influences the creative consumers see.
 
Next is authenticity. AI should support brand voice, not distort it. AI systems should always integrate brand guidelines, tone, and messaging guardrails, and their creative outputs should be controlled by the human-in-the-loop. The tech is powerful, but it needs to stay grounded in what the brand actually stands for.
 
And finally, inclusivity. AI systems are only as unbiased as the data they learn from. Creative AI should be reviewed regularly to ensure it’s not reinforcing stereotypes or excluding segments.
 
AI is a tool, and like any tool, it should be used with responsibility. Done right, it doesn’t just improve performance but also elevates the entire customer experience.