What’s missing in generative AI is the ‘why’

According to Google, Meta and other platforms, generative AI tools are the foundation of the next era in creative testing and performance. Meta advertises its Advantage+ campaigns as a way to “use AI to eliminate manual ad creation steps.”

Provide one platform with all your assets, from the website to logos, product images, and colors, and they can create new creatives, test them, and drastically improve results.

For a small company with few design resources, this is a fantastic development. Imagine being able to develop brand-appropriate creatives almost instantly that seamlessly follow the design guidelines and formats of social media platforms. It will make a big difference to millions of small advertisers.

For big brands, it’s likely to be a much different story, and the reason is the “because.” AI can ingest information and spit out new assets. The AI ​​can also test creatives and optimize towards creatives that are working. But when it comes to understanding why one creative performs better than another, AI falls short. For any company that values ​​its brand highly, AI will play a different role.

asking questions is good

Give a media buyer the results of a creative A/B test and I hope the first thing they want to know is why one performed better than another. Getting to the “why” is important in almost every other aspect of a well-run business; Why would creativity be any different?

Few good media buyers can get away with blindly following test results without having a good answer for their client as to why one strategy, design, or approach worked over another. And most CMOs are in the process of amassing as much data-driven insight as possible to justify every dollar they spend.

The why is often very specific and very important. Take an example of two banners developed for a quick service restaurant with different product and design variations. For an AI-powered creative testing algorithm, “burnt orange” stood out as a color associated with the highest-performing creative.

This idea could lead to optimizing the banners to be predominantly burnt orange, which may or may not work because the orange color was actually a cup of coffee with cream. While it’s not clear to the AI, it seems obvious to a person that the highest performing banners have cream on the brown vs. plain black.

Brand images are tricky

Global brands not only have high standards of quality and design, but few want to leave their brand strategy or reputation in the hands of AI. Putting assets in a machine and letting it run can set the stage for a variety of problems.

Take, for example, the dilemma advertisers have long grappled with: whether to use “real” looking models in advertising or overly polished, idealized versions of consumers. For a long time, studies showed that people reacted better to overly polished types, so researchers assumed that most people tended to be aspirational when it came to choosing brands and products.

But recently, a big movement has brought advertising closer to reality. More and more brands are introducing models that more fairly represent their consumer base. Add to that the desire many marketers have to more fairly represent the diversity of their customer base, which is not about testing performance, but about correcting an inherent problem with the old standards.

Social context, implications

Could AI weigh the pros and cons of which direction to go from a brand equity perspective? Certainly AI could create a variety of banners and test them, but the social context and long-term brand implications would be MIA.

There’s also the case of long-term versus short-term campaign goals and the research needed to make smart strategic decisions. Humans are still best suited to make these decisions and must be part of the data-driven process, even if AI plays a significant role.

Deloitte findings that 57% of consumers are more loyal to brands that are committed to diversity, for example. This finding may not be available to an AI performance algorithm at the time they are testing creatives, nor may an AI algorithm have the ability to weigh the various inputs that determine the correct balance of rendering.

Helping AI get better

This is not to say that the AI ​​isn’t useful and, frankly, exciting. In fact, AI is revolutionizing creativity today for major brands and their agencies. Today, AI can help with many manual tasks, inspire new ideas and directions, and provide information. Tomorrow has the potential to be part of the creative process on an even deeper level.

The AI ​​may not understand the “why” right away, but we can get more out of the AI ​​the more we train and interact with it. Telling an AI algorithm that the performance driver is not actually “burnt orange” but is, in fact, “coffee with cream” is just one example.

Another is to input the findings from larger studies on brand perception, sales, and loyalty so that the AI-driven results can fit the metrics that matter to big business brands. Finding ways to deepen an algorithm improves that algorithm’s ability to be useful. The power of insights is not in noticing a difference, but in understanding the “why” behind that difference and applying it back to the system to create a positive upward cycle.

For any business that cares deeply about its brand, AI will truly shine when it can work hand-in-hand with creative professionals, data analysts, brand managers, media teams, and other experts who have the background and are empowered with the context. . to understand the “why”.

Scott Hannan is Senior Vice President of Corporate Development at vidmob.