Generative AI is transforming the advertising landscape, offering unprecedented opportunities for machine-driven scale, precision, and relevance. The excitement around AI's potential to uplift productivity by up to 15% globally is well-founded. Yet, to fully harness these benefits, we need to recognize and address some critical areas for improvement in creative production and optimization.
Current generative AI models can already automate aspects of ad creation, fitting content to various platforms seamlessly. However, the true potential of AI to create ads that resonate lies in its ability to understand and predict audience engagement on a deeper level. To achieve this, AI needs to go beyond demographic data and actually know how specific audiences interact with different ad elements. AI needs custom, detailed creative insights to reach its full potential for creative production.
The State of Generative AI: Promising but Evolving
Generative AI is certainly hype-worthy. It’s fully mainstream, with 76% of marketers using it to write copy and 60% leveraging it for instant image creation. These models can quickly process huge datasets to deliver new outputs, making content development more efficient. However, many current tools still operate like earlier AI versions. Much like traditional rule-centric algorithms, most follow what can be described as an assembly line approach. After being fed or retrieving a selection of master creative components — such as logos, key messages, and product images — solutions configure them in line with broad best practices. For example, using specific color palettes, aspect ratios, or calls-to-action that get the highest click rates.
While this approach saves time and resources, especially for increasingly time-poor brand marketers, the ability to hand over assets and let machines compile ads holds obvious appeal, with some estimating that generative AI capabilities save over five hours per week. However, the mass production route also brings challenges in an environment where media overload has made distinctive branding crucial to winning audience attention and outshining competitors. Basic inputs can lead generative AI to produce homogeneous content that struggles to stand out in a crowded media environment.
Homogenous Content Challenges
The challenge with assembly line customization is that if your inputs, like creative assets or data, aren’t differentiated, you risk a high degree of sameness. Building ads according to limited stock suggestions will almost inevitably lead to messages from different brands looking and feeling similar, if not indistinguishable — especially when working within the narrow confines of platform-specific guidelines.
For instance, a video platform may have historic data indicating that using upbeat music and high-contrast images in the first few seconds of ads tends to result in more completed views for lifestyle brands. If this advice is applied universally, users will soon be inundated with homogenous party-like scenes where neither the product nor the brand stands out.
From this low differentiation can stem a second issue; poor performance. When ads aren’t distinctively captivating, they have little chance of inspiring short-term responses, recall, or securing the mental availability that fuels lasting loyalty. Additionally, generic recommendations won’t be a perfect fit for every campaign. For example, following advice to name featured items at the end of ads might successfully boost awareness of established products, but it’s unlikely to help new products foster recognition.
In essence, current smart content generation is hampered by the lack of an essential insight layer; understanding of what works for particular brands, products, and target audiences.
Where Smart Tech Needs to Go
The latest advancements in intelligent content solutions are starting to integrate a broader range of data sources, including creative analysis. This marks a significant step towards more nuanced ad production, guided by insights into how audiences react to specific creative elements, from CTAs to how talent and products interact. By understanding which components drive the best responses, marketers can craft more effective prompts for AI, leading to refined and optimized creative outputs.
The future of smart content production and distribution lies in leveraging custom creative data, and the results can positively impact the entire creative supply chain. By combining insights from creative decisions with performance metrics, teams can generate ads that are not only visually appealing but also highly effective in achieving key performance indicators (KPIs). Media teams can ensure that any dollars allocated go to ads most likely to perform. The integration of creative data into marketing mix models and other measurement frameworks allows for precise quantification of the impact of creative elements on campaign performance and overall business. Optimized outputs and more unique creative data then continue to feed an incredibly efficient, AI-driven flywheel for producing differentiated, resonant content.
Collaborating for Success: AI and Unique Creative Data
To realize the full potential of AI in advertising, we must ensure that AI is fed with high-quality, relevant information. This collaboration is the key to creating personalized and effective advertising. When empowered with rich, contextual data, AI transforms from a commoditized tool into a source of sustainable competitive advantage.
