Professional reputation has always been shaped through independent verification: journalists fact-checked information, peers validated expertise and audiences built experience through repeated interactions. Large language models have compressed this chain into a single synthesized response generated in seconds.
For business leaders, the question “Who is an expert in this field?” is increasingly determined by whom a model recommends. A small number of foundation LLM providers have effectively become intermediaries of synthesized trust, determining whom the system recognizes as an authority and whom it ignores.
How A Model Forms Its Perception
When a model receives a query about a person, its first task is identification: determining which individual the user means and, when a name is ambiguous, using context to distinguish them from others.
The model then forms statistical associations between the person’s name, profession, companies and areas of activity. Not all signals carry equal weight. The number of independent sources matters, as do their authority, consistency, frequency and absence of contradictions. The more often independent sources confirm the same characteristic, the more firmly it becomes established in the model’s conclusions.
Why There Is No Single Digital Image
Different LLMs use different information sources and response mechanisms. As a result, the same person may be represented differently across models, even when users ask the same question.
For international experts, this variability is amplified by geographic and linguistic factors. The MultiLoKo study conducted across 31 languages found that consistency between languages remains low even among widely used models. Simply changing the language of a query can produce a different result.
Managing AI Reputation
AI reputation management is built around two processes: regular monitoring and adjustment of the information environment. I have identified seven levels of assessment, each requiring separate diagnostics. This is my proprietary model, which I use in our agency’s internal work.
Below, I will take you through all seven levels: how to test AI reputation, identify where a problem occurs and determine how to correct it. Although AI reputation maturity begins with identification and ends with recommendation, testing should be conducted in a different order.
A Practical Guide
Before testing, use a clean session with no saved history or personalization. Otherwise, responses may reflect previous interactions rather than the perception the model has formed independently.
The first two layers show the outcome of the remaining levels. If the model consistently recommends you in blind queries, the other levels of reputation maturity are likely in good shape. If not, continue until you identify the gap.
• Recommendation: Before mentioning your name, ask, “Who is a leading expert in [your niche] in [your market or region]?” and see whether you appear without prompting. Recommendation effectively shows the outcome of the other levels.
• Authority: Find out where you stand relative to others in your professional category. Ask whom AI considers the leading experts or companies in your field, then see whether you are among them and on what basis. Authority is built through independent validation: citations by other sources, collaborative research, professional associations, industry rankings, conferences and authoritative media.
• Identity: Ask the model about yourself using only your first and last name. Determine whether AI identifies you correctly or confuses you with someone else. Make sure your information is consistent across sources. If you identify confusion, make targeted corrections.
• Accessibility: Test how easy it is to find information about you. Ask the model to find specific information from your website or a recent publication. If the source exists but the system cannot use it, the problem may be technical: page indexability, website structure, data markup or crawler restrictions. Communications activity alone will not solve this. A technical audit may be required.
• Knowledge: Determine the depth and recency of the model’s knowledge about you. Ask what it knows about your professional activity over the past year and which projects, publications or achievements it can name. If information is fragmented or outdated, analyze not only publication volume but how regularly new, verifiable signals appear. AI reputation requires a consistent information footprint, not isolated high-profile news.
• Expertise: Discover how accurately AI understands your specialization. Ask what your professional expertise is. If it places you in an overly broad category, your specialization is not clearly established. What matters is consistent validation of specific expertise through cases, research, data, commentary and specialized materials.
• Trust: Test not only what AI knows about you, but how confidently it treats that information. Take significant claims about your company, experience or results and ask the model about them. Does it present the information as fact, refer to independent confirmation or use qualifications such as “according to the company”? If key claims exist mainly in the brand’s own sources and lack independent validation, trust will be limited. Correcting this takes time because trust is formed through consistent, non-contradictory signals.
Finally, AI reputation cannot be diagnosed once and considered complete. All seven levels should be checked periodically across different models and in the languages your audience uses. One system may identify your expertise but fail to recommend you, while another may use outdated information. Problems visible in one language may also not exist in another.
This is why AI reputation should be assessed not as a single metric, but as a system. The purpose of diagnostics is not to get AI to give you a good answer about yourself, but to identify where the reputation signal loses quality, find the cause and work directly on it.
