The news: Marketers are scaling production with AI, but a gap is emerging between quantity and quality.

• 90% of marketers in the US, the UK, Australia, and Brazil call genAI a key tool in the creative development process, per a TikTok study conducted by WARC.
• 88% say AI has increased their creative output, but less than half (45%) say their quality is significantly better since implementing AI.
• 59% call traditional demographic segmentation (age, gender, and location) ineffective, but two-thirds (67%) say it’s their primary input for briefing AI.
• 40% cite an over-reliance on generic styles as the biggest limitation of AI creative output, while 36% blame unpredictable quality and 32% report a lack of originality.

Despite those setbacks, ad performance improves when supported by AI, with AI-generated ads scoring notably above the global advertising average, per WARC.

Zooming in: As marketing teams move past AI adoption and into optimization, they need to understand that increased quantities of ads doesn’t equal improved performance if every brand produces homogeneous, similar-looking creative.

Cutting through the noise requires critical analysis of audience engagement, behavior, and interests. Improving AI’s understanding of who the content is targeting, the goal of the creative, and how it’s being consumed could help models create more brand-specific messaging.

Ineffective briefing processes for these models will fail to create stand-out content as they may not understand nuances of brand voice, prior marketing themes, and audience niches to target.

Recommendations for marketers: Differentiate by focusing less on mass production and more on using signal data, strong creative direction, and human oversight to avoid generic outputs.

• Gather data on how viewers perceive and engage with ads and pair that with distinctive brand assets to help models craft content that highlights the uniqueness of different products and brand voices.
• Invest in hiring or training employees who can effectively prompt and evaluate AI-generated content.

The ability to guide AI models, apply strategic judgment, and reject AI outputs when necessary will help keep quality up and encourage consumer trust by keeping humans in the loop.

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