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The rapid rise of generative AI (GenAI) is unlike anything we’ve seen at the intersection of technology and media since the creation of the Internet.
Some of the initial use cases for GenAI have been so impressive, so quickly, that it’s led to a sense of swift impending disruption.
From late 2022 through all of 2023, there was a real belief that generative AI was going to take over all aspects of the advertising and creative process, automating everything from production to real-time optimization.
We are now in the stage where that hype has transitioned to realism. As the fervor has given way to a more measured perspective, the real practical applications for AI reveal a different story.
In Mediaocean’s April 2023 Outlook Report, a majority of marketers said they expect major impacts from generative AI in copywriting and image generation. However, by November 2023, the focus had shifted dramatically toward data analysis and market research, which remain the top categories.
Those results show a steady adoption curve for AI in creative as large enterprise brands navigate the “crawl, “walk,” and “run” phases of automation, supported by advanced ad tech.
Learning to Crawl and Walk
In the “crawl” phase, AI is primarily used for analysis, insights, and taxonomy. That foundational work is crucial for ensuring data quality and better content signals.
In the case of large language models, it’s garbage in, garbage out. Data quality needs to get better before it can be used for other downstream applications. The use of AI and computer vision to understand content and record it in creative taxonomies is improving signal recognition and interpretation. Through detailed (and fully automated) labeling of ads—covering aspects such as color schemes, themes, sentiments, or product categories—AI can better synthesize and make decisions based on those variables.
The “walk” phase involves using AI to reformat assets, resize content, and implement creative optimization toward business outcomes. AI has significantly sped up the path through the decision tree, and it has made optimizations more responsive to a wider variety of signals.
The decisions—and the content that drives them—are still best handled by creative strategy teams. This stage is about accelerating the execution of that strategy.
The Race has Begun; it’s Time to Run
Ready or not, the “run” phase is upon us, and major players are already off and, well, running.
To be sure, humans still play a critical role in the process of creative development, but AI can be deployed to generate new assets based on prompts.
It starts with a brand book (again, created by humans) and a target audience to model against. From there, assets can be tailored to various formats and channels, ensuring consistency with the brand’s voice and style. AI can then optimize this content in real-time based on contextual and audience signals, making adjustments to improve engagement and effectiveness.
For example, a global retail brand can input its product feed into a creative ad tech platform. The AI generates diverse creative content, such as social media ads, display banners, and video content, all aligned with the brand’s identity. As those ads run, the AI continually analyzes performance data, adjusting elements, such as imagery, copy, and calls to action, to better resonate with specific audience segments or respond to changing market conditions.
Navigating the Pitfalls
Wherever you are on the crawl/walk/run journey, there are important considerations that are worth contemplating to avoid falling down.
Overreliance on AI can lead to generic or misaligned content that fails to engage effectively or to reflect the brand’s unique voice. That dependency not only risks diluting brand identity but also increases the workload for creative teams who must rectify misalignments.
The quality of AI-generated content, no matter how efficiently it was generated, still requires significant human oversight to ensure it meets brand standards and resonates with the intended audience. Furthermore, the opaque nature of AI decision-making processes introduces ethical concerns around bias and representation that can impact a brand’s strategy and consumer perception.
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The idea of GenAI rapidly replacing humans was, in retrospect, a natural reaction to the whiplash of experiencing its terrifying magic for the first time.
But as we’ve settled in, reality tells a different, more balanced story in which we are laying the foundations for an AI-enabled creative ecosystem. That shouldn’t sink us into a “trough of disillusionment” about AI’s limits; rather, it should inspire us to clear the hurdles and gear up for the marathon ahead.
More Resources on AI and Marketing Creative
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