AI’s ‘Narrative Gravity’ Can Prejudge Your Brand, Warns Dan Sherrard-Smith

In a recent LinkedIn post, Dan Sherrard-Smith highlights a critical challenge for businesses in the age of artificial intelligence: AI’s tendency to form and perpetuate narratives about brands, often before potential customers even interact with them directly. Sherrard-Smith draws upon a significant study by Seer Interactive to illustrate how AI platforms may not always reflect current realities, but rather, a pre-existing story shaped by available data.

The core of Sherrard-Smith’s analysis revolves around the concept of “Narrative Gravity,” a term used by Seer Interactive to describe how AI systems can become anchored to older information. As Dan Sherrard-Smith explains:

“If a negative Glassdoor post, an old news story, or an analyst report shaped the early narrative about your brand… AI keeps telling that story. Even after you’ve updated your website and LinkedIn profile.”

This phenomenon, Sherrard-Smith points out, can be particularly problematic because AI may continue to surface outdated negative information, irrespective of subsequent improvements or positive developments within a company.

The Impact of Narrative Gravity on Brand Perception

Dan Sherrard-Smith elaborates on the findings from Seer Interactive’s research, which analyzed over 2.7 million data points across six different AI platforms. The study revealed a consistent pattern: AI platforms often struggle to retrieve the most current facts about a business. Instead, they tend to complete a narrative based on the data they were initially trained on or have most recently processed.

According to Sherrard-Smith, this was evident in Seer Interactive’s own experience, where multiple AI models repeatedly surfaced a year-old negative Glassdoor review, presenting it as current evidence of employee retention issues. This demonstrates how AI can inadvertently perpetuate outdated perceptions, even when more recent, positive data exists.

“AI doesn’t retrieve current facts. It completes a story it already formed.”

Sherrard-Smith emphasizes that this can significantly impact how a brand is perceived before any direct engagement occurs. The AI’s generated narrative can act as an initial, and potentially misleading, judgment.

Strategies to Counteract AI’s Narrative Bias

To address this challenge, Dan Sherrard-Smith outlines a practical, three-step approach for businesses to understand and manage their brand’s narrative as perceived by AI. This strategy empowers companies to proactively shape how they are represented in the digital information ecosystem.

Step 1: Test Your Own Narrative

Sherrard-Smith advises businesses to begin by directly querying AI platforms about themselves. This involves using tools like ChatGPT, Gemini, or Perplexity and asking questions such as, “What do people say about [your name or business]?” The results provide a baseline understanding of the AI-generated narrative.

Step 2: Identify Dominant Negative Signals

Following the initial query, Sherrard-Smith suggests probing further to understand the specific data points influencing the AI’s narrative. He recommends asking the AI platform:

“What single piece of content is AI likely anchoring to?”

And also asking, “what negative content comes back. List all that apply.” This helps pinpoint the most influential, and potentially outdated, information.

Step 3: Publish a Direct Counter-Narrative

The final step, as proposed by Sherrard-Smith, is to actively counter any negative or outdated narratives. This involves publishing new, authoritative content that directly addresses the inaccuracies or old information. As demonstrated by Seer Interactive’s approach of writing a blog post specifically about the Glassdoor issue, the goal is to inject a fresh, factual data point into the AI’s training data.

Dan Sherrard-Smith concludes by stressing the importance of this proactive approach. He argues that the effectiveness of a brand’s communication is no longer solely about what the company says, but also about the quality and recency of the data within the AI’s training set. By strategically publishing content, businesses can work to reshape these narratives before prospects form potentially inaccurate judgments.