Creative Co-Pilots: How AI is Shaping the Future of Advertising
Recent studies reveal an alarming trend: in 2022, over 50% of global media budgets, totalling $1 billion, were wasted on subpar digital creatives. This gap between expectations and realities poses a major challenge for agencies worldwide.
To bridge this gap, many are looking to artificial intelligence. However, AI’s role in advertising should not be to completely overhaul proven methods but instead to improve the creative process with generative capabilities and data-driven insights. Technologies like machine learning and big data have already transformed planning and optimisation. Now, generative AI promises to reinvent campaign execution.
For executives and creative professionals, integrating AI, particularly generative AI and Large Language Models (LLMs), signals a radical shift driving much-needed insight, the ability to experiment and stronger returns on creative ad spend. Here’s how:
1. The rise of creative co-pilots
AI “co-pilots” refer to LLMs that work alongside people, often embedded in existing tools, to enhance and speed up specific workflows. Tools like Adobe’s Firefly are attempting to revolutionise the creative process by enabling faster asset creation and more scalable production pipelines. This allows teams to spend more time focused on exploration and problem solving, opening up new creative possibilities.
AI also improves campaign precision and effectiveness. With multimodal capabilities, AI co-pilots can detect content inconsistencies across formats, ensuring cohesive, error-free creatives – streamlining operations and improving output quality. The Microsoft Advertising Platform co-pilot provides marketers with a 24/7 “intelligent assistant” to give critical insights throughout the production journey, including tips based on key selling points and search trends.
2. New AI tools accelerate and scale content creation
Major players in advertising are leveraging AI to rethink content creation in a more customisable and cost-effective manner. A prime example is the partnership between WPP and Nvidia, resulting in a new content engine built on Nvidia’s Omniverse Cloud. The software enables creatives and designers to produce scalable, high-quality commercial content by using an ecosystem of services such as Adobe Firefly and Getty Images.
Meta’s new platform for ad creatives, meanwhile, tackles massive scale optimisation. Its global rollout of AI-powered features for ad creatives promises to allow advertisers to automatically generate new variants of their creatives for different audiences. Meta claims early testing indicates the toolkit will save weeks of effort each year.
Alongside the major players, new generative AI startups are also bringing these capabilities to a wider audience, enabling smaller teams and even individuals to create campaigns that used to require entire agencies. Platforms like Runway and Pika exemplify this trend, enabling rapid, high-quality content production. This impromptu Adidas commercial, created by a single person in only a few hours, showcases the strength of such technology.
3. AI enables rapid iteration and feedback
Historically, the advertising industry has faced constraints when iterating and testing campaign creatives due to limited resources such as small focus groups and tight testing budgets. AI platforms like Kantar’s Link AI are transforming this process through automation.
Leveraging AI capabilities, Link AI rapidly evaluates creative assets, based on an extensive database of campaign data. This enables teams to optimise creatives, enhance media efficiency, and make faster data-driven decisions. The manual constraints around testing are removed and teams can quickly determine what resonates, adjusting campaigns accordingly.
The automation unlocked by AI introduces new potential for agile, effective creative development cycles. In an industry historically hampered by limited resources and rigid processes, platforms like Link AI represent a transformative shift.
4. In-house vs off-the-shelf solutions
As generative AI reshapes advertising, the emerging trend from off-the-shelf SaaS to custom tailored solutions becomes increasingly apparent. This move isn’t just about technological customisation; it’s a strategic response to complex challenges in data privacy, IP rights, and regulatory compliance unique to generative AI.
Generative models, trained on extensive datasets, can inadvertently breach privacy or IP laws. The risk of exposing sensitive data, coupled with the potential for copyright infringement, necessitates tighter control over AI systems. Hence, many firms are opting for modular solutions that offer better oversight of data use and compliance with evolving regulations like GDPR and CPRA.
These tailor-made systems allow agencies to safeguard their creative assets while ensuring their technology aligns with legal and ethical standards. This strategic approach not only addresses operational challenges but also empowers companies to maintain their creative advantage and valuable IP in an AI-driven era.
5. The need for mindful transition
Integrating AI requires a balanced perspective. It is not an instant solution, but rather a tool that complements human creativity. Its effectiveness depends on input quality – AI cannot fully replicate the emotional depth of human-made content.
Thoughtful integration is crucial. While AI can streamline processes and deliver insights, brands must ensure it aligns with their values. Bias and ethical considerations need to be addressed proactively.
AI is the paintbrush, not the painter. As advertising adopts generative AI, its transformative role is clear. However, human creativity remains indispensable. The most effective results will combine AI’s capabilities with strategic human guidance. Rather than replacing creators, AI can empower them. But for innovation and authenticity, the human touch is still essential.