Balancing AI Innovation with Brand Safety in Marketing

Artificial intelligence (AI) is reshaping industries, with marketing poised to benefit significantly from its transformative potential. While there is a significant and ongoing discussion over the differences between Machine Learning and Language Models, all colloquially called “AI,” the impact these tools and projects is having across industries simply cannot be ignored. Many in the ad industry may ask what the key differences are between ML models and LLMs. This is a valid question, and can be answered best by those working closely with the topic. One answer comes from Medium in their article on the subject saying, “the distinction between ML and LLMs depends on the specific requirements of the application. LLMs are often preferable for tasks demanding nuanced language understanding or Generative AI, like chatbots or text summarization, due to their advanced capabilities. However, traditional ML shines in scenarios where interpretability and computational efficiency are crucial, such as structured data analysis or resource-constrained environments like edge devices.” Simplified, Machine Learning can be seen as a “task extractor” & duplicator, while LLMs focus more on grammar structure to facilitate user interactivity.

Likened to the printing press and how it democratized access to information, AI seemingly promises to revolutionize marketing through hyper-personalization and operational efficiency. However, alongside its promise, AI raises valid concerns about brand safety, job displacement, and misinformation. A recent Accenture survey revealed that 37% of marketing professionals have integrated AI into their workflows, but skepticism remains strong, prompting companies like Apple and Wells Fargo to limit Generative AI in the workplace. Generative AI recently had a day in the limelight with mixed reviews following the release of several AI generated video Coca-Cola commercials as part of the brand’s standard holiday run. The core criticism of the AI generated content is two-fold: that it disrespects the legacy of staff/artists that worked on previous campaigns, and the arguably more impactful point, that it removes artists and production staff from access to income they both need and deserve.

The challenge lies in embracing AI’s capabilities while ensuring that brands maintain integrity and trust, even in the face of this rapid technological shift. As Writer’s Room put it recently, “With the rise of AI tools, there's an increased risk of brand liability due to content containing inaccuracies, bad actors imitating the brand, and potential data privacy and security risks.” Meanwhile, platforms like Meta are enhancing control mechanisms for brands in other ways. Meta’s live test of preemptive comment deactivation on ads offers advertisers a safeguard against reputational risks during sensitive campaigns or crisis periods. While this feature underscores a commitment to brand safety, it also highlights a potential drawback: silencing comments could be perceived as overly protective or deceptive. Finding the balance between protecting a brand’s image and fostering authentic consumer interaction remains an ongoing challenge.

One area where AI excels is streamlining brand safety controls, particularly for influencer marketing. Agencies, now tasked with vetting tens of thousands of creators, once relied on labor-intensive manual checks of online history, sentiment analysis, and content quality. AI tools can expedite this process, enabling more thorough reviews at scale. This shift not only increases efficiency but also empowers brands to partner with influencers confidently, knowing that their content aligns with brand values and safety standards. As partnerships expand, these AI-driven vetting systems are becoming essential to maintaining trust across increasingly diverse audiences. Ultimately, the marketing industry stands at a crossroads. AI offers unparalleled tools to refine and secure brand messaging, but its adoption must be guided by ethical considerations and robust oversight. By coupling AI innovation with transparent practices and a commitment to brand values, marketers can harness its potential while navigating the complexities of a rapidly evolving digital landscape.