Why Early Generative AI Ads Aren’t Working and How Creatives Will Shift to Integrate the Tech into Their Work

Welcome to our in-depth exploration of why early generative AI ads aren’t resonating with audiences and how creatives are adapting to integrate this technology into their work. In this article, we’ll delve into the challenges faced by marketers and creative agencies as they navigate the exciting yet complex landscape of generative AI in advertising.

Marketers are faithfully obsessed with the shiny new thing when it comes to their brand activations. So it’s no surprise that in year two of having generative AI at their disposal, marketers have rushed to use it in their advertising. But so far, consumers aren’t as enamored with generative AI created ads as marketers have been.

Imagine a towering, futuristic ad billboard standing sentinel in the heart of a bustling city, its backlit screen flickering with the promise of innovation. The display is awash with a glitchy, AI-generated image, a disjointed attempt at evoking human emotion that misses the mark by a wide margin. The colors are slightly off, the faces almost but not quite human, and the intended emotional resonance is lost in translation, resulting in a eerily vacant tableau.

The disconnect is palpable. Marketers, enamored with the potential of generative AI, have poured their enthusiasm into this cutting-edge display. They envisioned a future where ads would seamlessly blend with the human experience, evoking empathy, stirring desire, and driving consumption. But the public, ever the harsh critic, remains unimpressed. The AI’s attempts to mimic humanity fall into the uncanny valley, leaving passersby with a sense of unease rather than awe.

The city throbs with life around the billboard, a stark contrast to the stilted image it displays. People hurry past, their eyes barely grazing the ad, much less absorbing its intended message. A few cast sidelong glances, their expressions ranging from mild curiosity to outright dismissal. The disinterest is tangible, a silent rebuke of the marketers’ zeal. The generative AI, for all its hype, has failed to connect, to inspire, to make people feel. It stands as a stark reminder that while AI can generate, it cannot yet truly understand or replicate the complexity of human emotion.

The Rush to Adopt Generative AI in Advertising

The emergence of generative AI initially sparked a wave of enthusiasm among marketers, captivated by the promise of efficient, personalized, and scalable content creation. This technology, with its ability to mimic human creativity, offered brands the allure of reducing production costs and accelerating content delivery. Generative AI could churn out countless variations of advertisements, social media posts, and even customer interactions, allowing brands to engage with audiences in novel and seemingly personal ways. The potential seemed limitless, with marketers rushing to integrate AI into their strategies, envisioning a future where content creation was streamlined and data-driven.

However, the honeymoon phase was short-lived as consumers and the creative community began to express concerns and criticisms. Consumers started to notice the lack of authenticity and the ‘uncanny valley’ effect in AI-generated content, which often felt soulless and disconnected from human experiences. The creative community, including artists, designers, and writers, raised alarms about job displacement, exploitation of creative work for AI training, and the potential devaluation of human creativity. Ethical considerations surrounding AI-generated content, such as bias, misinformation, and cultural appropriation, further fueled the backlash.

Several high-profile brands faced criticism for their AI-generated advertisements, illustrating the growing tension. For instance:

• Toys R Us encountered backlash when it used AI to create a holiday ad campaign. Consumers complained about the lack of originality and the ‘creepy’ factor in the AI-generated toy designs.
• Under Armour faced scrutiny for its AI-generated sports slogans, which were deemed insensitive and out of touch with the brand’s diverse customer base.
• Coca-Cola received criticism for an AI-generated ad that inadvertently perpetuated stereotypes, highlighting the potential for AI to amplify biases present in its training data.

These examples underscore the complexities and challenges brands face when leveraging generative AI in marketing, highlighting the need for a balanced approach that considers ethical implications and consumer perceptions.

The Missing Human Element

Early generative AI ads, while showcasing the technological prowess of artificial intelligence, often fell short in capturing the essence of human connection. The core issue lied in their inability to replicate genuine emotion and authentic storytelling, crucial elements that have traditionally driven successful advertising campaigns. These ads, though innovative, frequently came across as soulless and mechanical, failing to resonate with audiences on a deeper level.

Industry experts have weighed in on this phenomenon, emphasizing the importance of human emotion in advertising. Eva Neveau, a renowned strategist, argues that emotion is the currency of advertising. She believes that while AI can generate content, it struggles to understand and mimic the nuances of human emotion, which are often spontaneous and unpredictable. AI can’t feel, Neveau states, and that’s a significant barrier when it comes to creating content that moves people.

Bill Oberlander, a creative director, echoes Neveau’s sentiments, stressing the role of storytelling in advertising. He notes that storytelling is about more than just putting words together; it’s about understanding the human experience and communicating that. Oberlander outlines several key aspects of storytelling that early AI ads often lacked, including:

• Cultural context: AI struggles to grasp the subtleties of cultural relevance, often resulting in tone-deaf or irrelevant content.
• Emotional depth: While AI can create surface-level emotions, it fails to capture the complexity and richness of human feelings.
• Authenticity: AI-generated content can feel artificial and manufactured, lacking the genuine touch that human-created stories bring.

To truly engage audiences, Oberlander believes that AI should be used as a tool to enhance human creativity, rather than replace it.

The Future of Generative AI in Creative Work

As we gaze into 2025 and beyond, creative agencies are beginning to embrace generative AI as a powerful instrument that augments human creativity rather than supplanting it. This burgeoning relationship is not about substitution, but about synergy. Generative AI, with its capacity to analyze vast datasets and generate novel ideas, is being harnessed to bolster the creative process. Agencies are experimenting with AI to produce fresh concepts, predict trends, and even draft preliminary designs, thereby freeing human creators to focus on refinement and strategy.

John Cornette, a prominent figure in the creative industry, posits that the potential benefits of integrating AI are manifold. He argues that AI can “democratize creativity”, making it more accessible and efficient. By handling repetitive tasks and providing a steady stream of innovative suggestions, AI can act as a “force multiplier”, enabling agencies to do more with less. Moreover, AI’s ability to learn and adapt could lead to more personalized and relevant content, enhancing the overall quality of outputs. However, Cornette cautions that over-reliance on AI could lead to homogenization, with algorithms churning out similar ideas based on the same datasets.

Paul Malmstrom, another influential voice, echoes Cornette’s sentiments and adds further insights. He believes that while AI can bring “scale and speed” to the creative process, it should not be viewed as a panacea. Malmstrom emphasizes the importance of human intuition, cultural understanding, and emotional intelligence—areas where AI currently falls short. He advocates for a balanced approach, where AI and humans collaborate, each playing to their strengths. Malmstrom also highlights potential pitfalls, including:

• The risk of “over-automation”, leading to a loss of the human touch in creative works.
• The potential for AI to perpetuate existing biases present in its training data.
• The need for continuous upskilling among creatives to work effectively with AI tools.

FAQ