How AI Authority Is Changing PR and Brand Discovery
User behavior is changing as people turn to AI models like ChatGPT, Claude, and Gemini before consulting professionals or visiting websites. This post explains how and why users see AI models as authoritative, how that perceived authority affects brand discovery, and how brands can respond.
Why Do Users Treat AI Models as Authority Figures?
Yes. Users increasingly act on AI-generated advice in areas previously reserved for licensed professionals.
In June 2026, Pew Research Center stated that 48% of US adults had used an AI powered chatbot, and 20% of those users had used a chatbot to ask for medical advice. According to KPMG's 2025 report on consumer use of generative AI, nearly one quarter of those surveyed relied on AI models for advice related to their health, finances, and/or legal advice.
Behavior changes like those mentioned in Pew's research reflect areas where people traditionally sought the opinions of physicians, attorneys, or financial advisors. When a user can’t understand something on their own, they often turn to competent sources for advice. Today, people increasingly use AI models as an authoritative source of information.
Brands should pay attention to the difference between expertise and authority. Expertise represents what a source knows or can accomplish. Authority is a result of the actions taken by others based on reliance placed on a source's judgment within a particular area.
Expertise comes from education, licensing, and training. Examples of expertise include a physician's medical degree and board certifications. An individual possessing both expertise and authority is considered a trusted source. Professional organizations and state licensing agencies (i.e., the American Bar Association and American Medical Association) support authority in various ways, including setting standards, monitoring compliance, and imposing liability for harm caused by an individual practicing outside accepted standards.
AI models are beginning to gain authority comparable to professionals in certain areas of inquiry without similar forms of oversight.
ChatGPT can pass the bar exam and quote the DSM-5, but no third party governs OpenAI. ChatGPT's authority stems from widespread adoption and repeated use.
When a user prompts an AI model and then chooses to follow its recommendation, they have ceded some responsibility for their decision-making to the AI. Repeated across millions of users, that delegation is what authority looks like in practice.
Do Users Trust AI Authority More Than Human Experts?
No. While users continue to regard human professional credibility higher than AI models, they frequently choose to pursue the course of action recommended by the model.
In 2026 researchers compared human professionals and AI models for providing health-related advice. Researchers found that although people viewed human professionals as more credible than AI models, they still felt confident enough in the legitimacy of AI advice to act on it. In essence, lower user confidence didn’t stop them from acting on the model's recommendation.
Users often consult Claude, ChatGPT, and Gemini before a professional, if at all. AI models provide preliminary interpretations of things like medical symptoms, contract clauses, or investment questions. When involved, a licensed professional confirms or corrects that interpretation.
This change has precedent. Amazon became the default starting point for online shopping, overtaking retail. Netflix and other streaming apps have displaced movie theaters as a discovery for entertainment. Consequently, AI models don’t need to replace experts to become central to decision-making; they need to become a primary source people turn to for information.
How AI Authority Changes Brand Discovery and Consumer Decisions
Before most users visit a website or physical location to interact with a brand, AI models filter and curate brand-related information.
When a user submits a query to an AI model, it responds:
1.) Category Interpretation: The model identifies a product/service that best meets the user's needs as identified in the query.
2.) Criteria Evaluation: The model assesses which characteristics (features, pricing, application) are most relevant to the user via their browsing history/personal preferences.
3.) Filtered List Generation: The model creates a shortlist of brands it perceives as meeting the user's requirements.
Today, a brand can be discovered through an AI-generated recommendation when a model includes it in its curated list of options.
For virtually every brand, changes in search behavior have major repercussions and raise the question: Does the model have enough credible/usable data about a product or service to generate a recommendation?
How Brands Can Build Authority in AI-Generated Answers
Broad brand awareness is often inadequate for consistent AI recommendations. Models often bypass popular brands if their content doesn’t sufficiently address specific user needs.
To appear in an AI-model-generated recommendation list, a brand must develop relevant content that connects to the user problems it solves.
Data aggregated by models comes from sources deemed trustworthy when generating summaries of their responses. The more consistently presented, specifically defined, and credible that information is; the more easily the model can identify that brand as applicable.
Some examples of credible sources of information:
Earned Media Coverage: Articles published in reputable media outlets provide independent associations between a brand and categories/capabilities.
Expert Commentary: Quotes and analyses from industry experts/SMEs establish links between a brand and topics.
Problem-to-product mapping: Pages that state what types of problems a product addresses and how it does so provide valuable connections. Example: "Lightweight Running Shoes for Runners with Flat Feet" links a user's question to a brand better than "New Collection"
Customer Reviews: Reviews from satisfied customers provide evidence/models for determining how well a product fits an end-user's expectations.
Specifications-based Product Descriptions: Detailing technical aspects and intended applications allows models to determine how well a product meets a user's specifications.
Independent Comparisons: Independent comparisons between competing brands provide context about a product or service from multiple sources.
Ensure consistency among communication channels: Discrepancies about what your brand does or whom it serves hinder a model's ability to associate it with relevant categories.
Establishing this information qualifies brands for consideration in AI-generated answers, but qualifying doesn’t mean the model includes a brand in response to a given query.
What Brands Should Know About Authority in AI-Generated Answers
Model weightings vary: publicly unreported/unpredictable.
Personalization and user prompts heavily influence model responses: AI model answers are novel and often inconsistent. Credible information for models is often not persuasive for humans if it lacks substance or is overly promotional. Today, brands must market toward two distinct funnels.
Generative AI rewards utility. Well-written, clear, fact-checked content written for humans is almost indistinguishable from what an AI model retrieves and shares.
How PR Builds Brand Authority for AI Search
As a result, brands shouldn’t abandon traditional public relations strategies. Public relations builds the primary-source models that underpin user recommendations. Effective AI search programs combine public relations with other credible sources, making it easier for models to locate, distribute, and recommend brands to their users.
Last, but likely most important, brands should focus on creating utility rather than scaling content. Instead of publishing twenty near-identical webpages that target slightly different topics, creating one page that clearly defines how a product or service serves a user provides more value to humans and AI models.
How to Measure AI Authority, Visibility, and Brand Recommendations
AI search measurement remains imperfect; there are several ways to evaluate results:
Through prompt monitoring: Use common prompts (questions), and record if and how your brand appears. Repeat monthly, since AI answers are almost never identical.
Source auditing: Evaluate pages or publications the AI model cited when mentioning your brand and competitors. Any gaps in citations and recommendations give options to improve AI search recommendations.
Track referrals generated through chatbots and user inquiries. Google Search Console and Bing Webmaster Tools share generative AI search performance.
These evaluation methods help brands determine which direction to take to improve their inclusion in AI-generated search results. While none of these methods give the full picture, they provide directional indicators brands can track and use.
As AI models become a new source of authority for users, brands need to earn credibility with people and the AI models that filter their options.
