The emergence of new generative AI tools has caused various industries to rethink how they function and identify ways to enhance their processes. The current iteration of AI tools offers incredible speed in creating text and visual assets, which is a fascinating proposition for brands and advertisers. In the short term, the tools’ real benefits are less about brand visibility and more about paving the way for innovative solutions and quick campaign ideations.
Today’s generative AI tools present a treasure trove of potential issues around content “ownership” and brand safety. While the digital marketing industry is geared to adopt the technology, it’s essential to consider the most impactful ways generative AI can enhance the industry in the near future.
One of the main concerns for brands and advertisers is the potential for generative AI-created content to closely resemble existing artwork. Because content can be generated and implemented into campaigns so quickly, it’s easy for brands and advertisers to unintentionally use imagery and messaging that infringes on intellectual property or copyrighted assets. Additionally, generative AI often suggests terms, mottos and slogans that are copyrighted unless explicitly asked to remove any copyrighted text.
Another consideration is around brand safety, as there is a risk of generative AI creating assets that do not fit brand guidelines or are offensive to certain audiences. This can significantly impact brand reputation. Advertisers need to ensure that AI-generated content aligns with their brand values and will resonate with target audiences.
Despite these challenges, the generative AI market is expected to reach $188.62 billion by 2032, up from $8.65 billion in 2022. This growth is not surprising, as people are increasingly interested in AI and quickly realizing how the current tools represent an excellent “jumping off point” for advancing workflows.
Platforms like Midjourney allow users to develop images simply by typing in basic text. The initial assets it creates based on the prompt could turn out to be very close to an image you are thinking of or could be entirely different, inspiring fresh perspectives on what branded collateral can be for a campaign.
The Challenges of Generative AI for Developing Digital Advertising Products
Generative AI is also being used to develop first-draft code for new digital advertising products or solution updates. It can take several weeks to months to write and test code when developing new solutions or evolving existing ones. Solutions like ChatGPT deliver first drafts in seconds, which is impressive, but it is essential to review it for a few critical reasons.
Generative AI often produces code that is not optimized for performance or security, and it might not be scalable. These issues result in products that miss the mark in regards to reliability standards. Additionally, it’s challenging to maintain, modify, and incorporate the code into existing products.
If every digital solution was initially developed by AI, things would likely function correctly, and innovation and updates would be easy. However, humans developed the initial code, and there is too much variability in how we build solutions. It’s that variability that makes current AI-generated code unable to seamlessly integrate with what we’ve previously made. Therefore, it’s still necessary to have a fact-checker or goalkeeper, just as with using AI tools for plug-and-play creative assets.
Despite these challenges, generative AI tools are here to stay. The quicker we learn their use cases and hindrances, the faster we can optimize our workflows for the better. Only by adopting generative AI tools can brands, advertisers, and solution providers understand what’s coming in the new frontier.
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