In the AI Era, Brand Trust Hinges on 'Consistent Messaging'

This highlights the need for companies to manage their messages to be accurately and consistently reflected in AI search results, as people gain their perception of results through AI.

This is because AI summarizes and provides search results, leading users to consume information within the AI screen without needing to directly visit websites.

In the past, content that provided solutions to specific keywords or questions was important, and it was crucial to appear within the top 1 or 2 results through search engine optimization (SEO).

However, now that AI summarizes and reconstructs relevant information in context within search results, users' need to directly click links and visit websites has decreased, leading to a drop in visitors.

Query Fan-out is a concept where, if a user asks a broad question, an AI bot generates and expands upon two to ten sub-questions.

The AI bot then finds and compiles answers to each of these sub-questions.

Therefore, companies need a strategy to identify questions relevant to them and consistently deliver the same message through their own media and trusted external channels.

Based on the degree of message alignment in this information, AI judges the company's credibility, also considering the media's tier, website history, and overall authority.

A primary reason why much corporate website content is not properly exposed to AI is technical configuration errors that make it difficult for bots to retrieve information.

Situations where bot access is blocked via settings like robots.txt, or inadequate basic search engine optimization (SEO) makes it difficult for AI to collect and learn from content.

A company's website should serve as a 'benchmark' for its business or specific technologies.

In contrast, the newsroom should act as a 'company commentary,' a space for a wider variety of softer content, such as product/technology context and usability.

By using generative AI tools like ChatGPT or Perplexity, inputting a company website URL, and requesting an analysis from a specific perspective, brand explanatory power can be regularly diagnosed.

Through this, it is important to understand how the basic aspects of a website are perceived by AI, manage about 50 question prompts, and plan the answer structure.

Even large companies tend not to utilize these structures well.

Especially when citations are included, accuracy should be enhanced by specifying 'who from which company' or 'exact name and position' rather than merely stating 'a company official'.

Consequently, a strategy is needed to manage press releases for journalists and data structures for AI learning separately.

Therefore, information related to members' personal expertise, such as degrees, careers, and professional activities, must be clearly recorded and expressed. Building and managing this expertise through LinkedIn profiles or professional articles is necessary.

When uploading images, inputting information about the image via 'Alt Text' allows AI to recognize and use it as learning material.

If there is abundant content for a specific question, AI prioritizes information from highly authoritative and trustworthy sources. This is because AI generates answers by synthesizing learned data and real-time search results.

However, if authoritative and trustworthy content or media for a particular question is scarce, information from unofficial channels like personal blogs may also be retrieved and utilized.

The more influential these experts become as opinion leaders, the more likely AI is to trust them and convey information about the company to the general public.

When users ask questions directly searching for a company name, it falls under 'brand prompts'. 'Non-brand prompts', on the other hand, are more often about solving a problem they are facing.

Therefore, new players can seek opportunities to gain an advantage in the AI search environment by deeply researching customer problem-solving questions and language.

The core of this is approaching it by preparing answers to customer queries rather than a one-sided information delivery method.

To be well-reflected in AI search results, structured writing that AI favors is crucial. However, human readers need context, so a 'dual writing' strategy combining both is effective.

Content should be structured to include a structured information part that AI can retrieve, a context part that helps human readers decide whether to read, and a solution part for problem-solving.

Specifically, it is good to provide context at the beginning to draw in the reader, then simultaneously present structured information and user-friendly solutions.

The decrease in website visitors signifies that the primary agent of customer search behavior is shifting from humans to AI.

Therefore, companies that do not exist in AI search results are effectively in a state of 'existence gap', making it an urgent problem to solve.

Companies must acknowledge changes in metrics like decreasing visitor numbers and actively manage their messages in line with the changed search environment.

A strategy to enhance content consistency, accuracy, and authority so that AI can trust it is essential.