Tag: AI Search

I remember the days when a Google search was akin to embarking on a quest for information. It was an adventure of navigating various links and forming my own opinions.

Nowadays, tools like AI Overviews, ChatGPT, and Perplexity condense all that information into a single, simplified answer. This transformation often strips away the finer details while amplifying certain perspectives.

This shift has redefined online reputation management. Now, search engines not only present information but shape the underlying narratives. This raises the stakes for brands, as even a top-ranking status doesn’t guarantee influence if AI stories tell a different tale.

For brands, the game has changed. Being number one doesn’t ensure visibility and influence anymore. The underlying narrative holds far greater power.

AI Narrative Formation: Crafting User Answers

AI platforms now utilize what I like to call ‘AI narrative formation.’ This process crafts the responses we receive from various search engines. Let me walk you through how this system works.

Source Pooling

These systems pull content from numerous sources. Contrary to expected reliance on peer-reviewed articles, they gather data from Reddit, YouTube, and social platforms like Instagram and TikTok.

Signal Weighting

Not all sources are equal. Often, a popular yet low-quality source can outweigh a singular, credible entry. A bustling Reddit thread with negative feedback might overshadow a well-researched Wikipedia page.

Narrative Compression

The summarization process compresses diverse inputs, often losing nuance along the way. Complex reputations are simplified into general statements like, ‘Users find this company untrustworthy.’

Continued Reinforcement

These summaries transcend their original context, getting shared and re-shared across social media. As these echoes return as new data, they further entrench the narratives in AI responses.

Explore deeper: How AI is Redefining Authority in Search

Unraveling a Finance Company’s Reputation in AI Search

To illustrate AI narrative formation, consider a recent case I worked on involving a financial company, which we’ll call Company X.

Company X’s reputation remained strong on traditional SERPs. High Trustpilot ratings and reputable endorsements were the norm until Google AI Overview threads surfaced a forgotten Reddit forum rife with grievances against them.

The AI Overview skewed the narrative, suggesting Company X had unresolved customer service issues, even though these concerns had been addressed years prior. This created a skewed perception that was hard to counteract.

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The Amplified Risk from AI Searches

AI dramatically increases reputational risk through several mechanisms:

• The Spread of Negative Narratives: Negative content surfaces faster and more prominently than before.
• AI Hallucinations: Despite growing awareness, AI inaccuracies continue to deceive.
• The Snowball Effect: Repeated narratives gain momentum, complicating reputation management efforts.

It has become evident that in ORM, repetition often overrides accuracy.

Explore deeper: Generative AI’s Defamation Challenges

Auditing AI-Generated Narratives: A Step-by-Step Approach

Let’s consider a situation involving an AI-generated narrative challenge faced by CEO X of a well-known SaaS company.

After an out-of-context quote from CEO X’s podcast appearance went viral, AI summarized him unfavorably. Quickly, his reputation transformed negatively across major platforms.

Step 1: Mapping Queries

I initiated a process to understand what queries AI outputs were generating about CEO X. This helped identify the underlying issues.

Step 2: Capturing Outputs

Identifying repeated claims revealed how CEO X was perceived. Narratives from Google AI and ChatGPT were consistently portraying him negatively.

Step 3: Delving Through Sources

The next step involved examining the quality of sources contributing to these narratives, often outdated or lacking accuracy.

Step 4: Analyzing the Narrative Gap

This involved assessing discrepancies between AI narratives and his actual reputation, contextualizing the initial quote, and examining the long-standing perception of CEO X.

Step 5: Correcting and Replacing Sources

Finally, I focused on directly addressing, correcting, and replacing those negative narratives. This involved engaging directly with platforms that contributed to the misinformation and reinforcing positive content elsewhere.

Explore deeper: Responding to Negative AI Reviews

A New Perspective: From SEO to Narrative Management

The focus has shifted from merely achieving top SEO rankings to understanding and adapting to narrative shifts. We must rethink our strategy from content engagement to managing the narratives AI disseminates.

To succeed, it’s important to reinforce AI systems with quality inputs, including crafting high-quality content, pursuing credible mentions, disseminating structured data, and managing misinformation directly.

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Inspired by this post on Search Engine Land.

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