Q&A with José Antonio Martínez Aguilar (JAMA), Global CEO of Making Science
By R. Larsson, Advertising Week
Q: What’s the core tension performance marketers are facing today regarding automation?
One of the biggest challenges is the growing sense that performance marketing has become a black box. With so many of the major ad platforms automating bidding, targeting, and even creative decisions, a lot of marketers feel like they’ve lost visibility, and along with it, a sense of control. These tools are delivering results, but it’s often unclear why. What’s actually driving performance? Is it the audience, the creative, the timing?
That uncertainty makes it hard to trust the outcomes. It’s not that automation is bad—it’s doing a lot of heavy lifting. The challenge is that teams can’t see exactly what’s working, or replicate it elsewhere, which creates real friction. And that’s where we are now. Marketers are caught between the speed and scale that automation offers, and the frustration of not being able to steer or fully understand it.
To address that, marketers need to look at the different types of automation in play—namely Embedded AI and Applied AI—and where they can still intervene.
Q: Can you explain the difference between Embedded AI and Applied AI?
When we talk about automation in platforms like Google or Meta, we’re usually referring to what’s known as Embedded AI. This technology is built directly into the platforms, like Performance Max or Advantage+. These systems are designed to streamline campaign management by automating much of the process. But as they take over more functions, they also limit visibility and control. You don’t always know why a certain campaign outperforms another or why an ad variation works best.
That’s where Applied AI becomes important. These are external, third-party tools that sit alongside the platforms, helping marketers make sense of the automation and customize it to their needs. They provide the insights and flexibility that Embedded AI doesn’t always offer. An example of such technology is ad-machina, which can generate and scale creative assets based on user intent in search campaigns, while also helping teams understand which messages and formats resonate the most on each platform; continuously analyzing and optimizing the performance of each asset. In this way, these tools are not about replacing the platforms’ automation, but instead complementing it, so marketers regain a sense of clarity and influence.
Q: How has the shift to automation impacted the role of the creative in performance marketing?
It has brought the creative to the forefront. When everything from bidding to audience targeting is automated, creative becomes one of the last remaining levers marketers can actually pull. It’s where you can differentiate, tell your story, and drive results—but only if you understand what’s working.
The challenge is that many platforms are now also automating creative delivery, choosing which assets to show and when, based on their own algorithms. Without access to performance insights, creative teams are left guessing. That’s why tools that bring data back into the creative process are so valuable. They let teams test and learn, quickly iterate, and see the impact of their work. And this feedback loop is essential; it helps bridge the gap between automation and storytelling, which is where great performance really happens.
Q: Why do you think trust in platform automation has started to erode, and what can be done about it?
That erosion of trust stems largely from opacity. When a campaign performs well but you can’t explain why, it feels like luck. And when it underperforms, but you can’t diagnose what went wrong, it’s incredibly frustrating. Add in constant algorithm updates and minimal transparency from platforms, and it’s easy to see why marketers are skeptical. They’re being asked to make and justify decisions without all the information.
The solution isn’t to abandon automation, but to balance it with tools that provide insight and context. Applied AI plays a key role here—it helps decode what’s working, where performance is coming from, and how to replicate success. It gives marketers the confidence to stay accountable and make better decisions, even in an increasingly automated environment.
Q: What’s your advice to marketers trying to strike the right balance between automation and control?
The goal shouldn’t be to push back against automation, as it’s too valuable and is only getting smarter. But marketers shouldn’t surrender to it either. Your value lies in understanding your brand, your customer, and your business goals in a way that no platform algorithm ever will. The trick is to engage with automation actively, through Applied AI tools, and not passively through only Embedded AI technologies.
That means investing in tools and practices that give you insight, flexibility, and creative control. It means working with the platforms, but on your own terms—guiding the strategy, shaping the messaging, and always learning from the data. When you do that, automation becomes an asset, not a black box. And that’s where the real advantage lies.
It’s also important to view AI adoption as a journey. I always talk about the ‘4 As’—Automate, Augment, Amplify, and Awaken—as a framework for how businesses and marketers can mature their use of AI. Early on, automation might help eliminate manual tasks, but the real opportunity comes when AI begins to augment decision-making, amplify what’s already working, and ultimately awaken new ways of thinking and value-making. Striking the right balance is about recognizing where you are on that journey, and leaning into the technologies that not only drive efficiency, but unlock creative and strategic growth.
