How generative AI is upending brand crisis strategies

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The following is a guest article written by Shannon Reedy, Chief Brand Officer at Terakeet. Opinions are the author's own.

Brand crises used to go through predictable stages of sparks, media cycles, reactions, and then subside. As the recent Campbell Soup controversy shows, that strategy is outdated in the age of artificial intelligence.

After the conversation with executives went viral, the impact was swift and measurable. Beyond traditional media coverage, this narrative was rapidly reinforced across AI platforms and search engines, expanding its reach and impact.

This incident revealed a new reality in crisis management. When AI becomes the first stop for information, negative brand stories can spread faster, linger longer, and dangerously misrepresent the “truth” to important audiences such as employees, shareholders, and customers.

This raises important questions for brands. How do you respond when an algorithm is shaping your story faster than you can?

brand chaos

In November, news surfaced that a Campbell executive had filed a lawsuit for making “highly derogatory comments” about the company's products. [processed] Food for the “poor”. The executive also claimed the brand used “bioengineered meat” and allegedly made derogatory comments about employees.

After that, Terakito's analysis found that negative news sentiment at Campbell's soared to 70%, and the first page of the search site was flooded with harmful coverage.

Anyone who searches for Campbell's brand or its products will encounter the article in prominent Google features such as the News Feed, the “People Only Ask” section, and the “AI Overview.” Years of marketing and branding disappeared in an instant.

One of the biggest risks posed by AI is its inherent bias towards negative information. In the digital ecosystem, sensational or controversial stories garner a lot of attention, and once they gain momentum, they are rapidly reinforced and amplified across platforms.

That's exactly what happened in Campbell's case. Coverage quickly spread across social media and traditional news outlets, generating a flood of new content that AI systems began to capture and enhance.

The article sparked a spike in searches for “3D printed meat” and raised questions about whether Campbell's uses real meat, but Generative AI did not step in to correct the narrative. Instead, it pulled language referring to “mechanically separated chicken” from Campbell's own website, surfacing fragmented context that further confuses rather than clarifies the perception.

The impact of such reputation-enhancing events extends far beyond the headlines. In addition to questions about product integrity causing an immediate loss of consumer confidence, Campbell's saw its stock price decline 7.3%, according to Terakeet's analysis. This represents a $684 million decrease in market capitalization.

Consumer reaction quickly followed. Calls for a boycott followed in response to the executive's flippant comments, highlighting how leader behavior and executives' visibility can directly influence purchasing decisions and brand loyalty.

The ripple effect is likely to extend to talent and employer branding as well. The allegations surrounding the employee who recorded the remarks, and his subsequent firing and lawsuit, pose further reputational risks. For prospective employees, these stories shape perceptions about company culture, leadership responsibility, and psychological safety, all of which can impact recruitment and retention.

Be proactive rather than passive

Campbell's Company issued an official statement and posted a press release on their website reaffirming the authenticity of the ingredients used in their products. This traditional public relations response helps reintroduce factual information into the conversation, and early signs suggest that AI systems are already starting to look at companies' narratives.

But this alone is not enough to reset the narrative currently circulating online. When a controversy spreads widely across news, social, and search, it becomes part of the data layer that AI relies on. This makes it difficult to modify online perceptions after the fact.



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