A few weeks back, the world’s largest advertising agency, WPP, announced an AI partnership with NVIDIA. The partnership outlines that NVIDIA will empower creative advertising production by using exclusive visual content from Adobe Firefly and Getty Images created by NVIDIA Picasso, a foundry for custom generative AI models for visual design, and will be provided exclusively on behalf of WPP’s advertising clients.
This recent news underlines how generative AI has emerged as a powerful tool that is revolutionising the field of ad creatives among the world’s largest agencies and enterprises. By leveraging advanced algorithms and machine learning, generative AI enables marketers to produce innovative and high-performing advertising content with unprecedented efficiency.
As the cost to produce content trends to zero and the organic discovery becomes ineffective, the ability to attract attention towards various forms of content will become harder and more crowded. This cost dynamic increases the necessity of paid advertising to find new customers and realise meaningful traction, which is why we need to uncover how generative AI impacts marketers so that every marketing team can include it as a part of their toolkit.
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Before diving into the impact of generative AI on ad creatives, what is the most important objective of an ad creative production and testing process? The purpose of an ad creative production and testing process is to ensure the availability of fresh and viable creatives before active creatives approach their performance inflection point. Put simply, marketers need to launch new ads before their previous ads degrade and reach creative fatigue.
As a result, generative AI is a real breakthrough for advertising, given its ability to preempt creative fatigue with minimal cost. Tools like Runway, StableDiffusion, and Midjourney enable marketers to experiment with various iterations that were once of high complexity and cost, like video, and once commanded by an army of designers but now can potentially be covered by a SWAT-like team of two or three persons.
Depending on your perspective on this new process, the definitive value of using such AI tools is that it eliminates the need to understand the why behind a successful outcome, as we can now use processes like image inversion to feed the AI with your competitor’s assets, for example, to help create remarkably similar results and quickly cycle through various iterations until you get the campaign results that you want.
That said, the most important point that I want to underline here is not that generative AI will lead to substantial cost savings but that it is likely to produce outperforming advertising results that defy conventional human intuition.
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For example, the critical value of tools like MidJourney and Stable Diffusion is to decouple the ideation process from the arbitrary aspects of advertising creatives that teams often believe lead to the success of their ad campaigns. By pairing MidJourney and Stable Diffusion with vast datasets and sophisticated algorithms, generative AI algorithms will identify subtle patterns and correlations that humans may often overlook.
This inherent capacity to uncover hidden insights enables marketers to reach highly targeted segments and achieve optimal campaign performance. Consequently, the value of generative AI is in its ability to push beyond traditional boundaries and consistently deliver superior results with significantly less effort.
For smaller and less resourced teams, the future of advertising production might be human creators who are advanced in procedurally parsing existing creatives and really good at generating high-quality variants from those components. This future is exciting because it is more likely to level the playing field between bigger and smaller advertising teams.
Ultimately, I want to highlight that generative AI tools provide value to the creative production process not so much by replacing the human, mechanical efforts involved in asset creation but rather by mitigating the risks associated with human biases when determining which specific creative elements outperform others because these processes will be led by unemotional machines.
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