A practical approach to making reliable information accessible to search and AI systems
Quick Read Abstract
People use conversational AI to seek information, compare options and work through decisions. That creates a reason for organizations to test how their public information appears in AI-assisted research. It does not establish a universal formula for winning recommendations.
OpenAI's September 2025 consumer-usage study analyzed 1.5 million conversations. Its findings describe user behavior; they are not a ranking study or proof that a specific marketing tactic will produce citations. [1] The framework below is HRI's practical guidance, informed by published platform documentation.
Start With What the Platforms Actually Say
Google states that its established search practices remain relevant to AI Overviews and AI Mode. A page must be indexed and eligible for a search snippet to appear as a supporting link. Google does not require special AI markup or a new machine-readable file, and meeting its requirements does not guarantee inclusion. [2]
OpenAI documents separate controls for different uses of website content. OAI-SearchBot supports search discovery, while GPTBot concerns content that may be used for model training. ChatGPT-User handles certain user-initiated visits and is not the control for automatic search crawling. Website owners can make separate decisions about search and training. [3]
These are platform-specific requirements. A setting for one provider should not be assumed to govern another provider.
An HRI Framework for Improving Visibility
1. Make the underlying information useful
Begin with the questions a real reader needs answered: what the product or service does, who it is for, what it costs, what limitations apply and how a decision can be evaluated.
Give material claims a source. Distinguish measured results from examples, forecasts and editorial judgment. Explain the method behind comparisons, particularly where different products use different pricing units or cover different needs.
Tables can make a comparison easier to understand, but their usefulness comes from the quality and consistency of the information. Treat formatting as a way to help the reader, rather than a promise of preferential treatment by an AI system.
2. Check technical access
Ask the web team to verify whether important pages can be fetched and read by the intended search services. Check robots directives, indexing controls, authentication, CDN rules, page status codes and canonical URLs.
Choose crawler permissions deliberately. Follow the relevant provider's published documentation and verify the result in server logs or the provider's inspection tools. Search access and model-training permission serve different purposes.
Keep critical product facts available as readable page content. When structured data is used, ensure it agrees with the visible information. Google's guidance specifically calls for that consistency. [2]
3. Maintain credible information across channels
Review the places where customers encounter your organization: the website, industry directories, review services and relevant professional communities. Correct inconsistent product names, descriptions, pricing or contact details.
Do not manufacture reviews, disguise sponsorship or assume that appearing on a particular site guarantees an AI citation. A page may help customers evaluate a product without being selected by a model in a particular response.
The useful operating goal is consistent, attributable information wherever a prospective customer looks.
4. Update facts when they change
Assign responsibility for information that becomes stale: prices, product availability, compatibility, specifications and policies. Update the substance when it changes and show meaningful publication or revision dates.
Changing a timestamp without changing the content does not make a source more informative. Keep a record of material revisions so a reader can understand which version supports a claim.
5. Measure observed results
Create a repeatable set of realistic questions drawn from customer conversations. Include discovery questions, comparisons and questions about limitations. Record the date, platform, question and cited sources when testing.
Repeat observations because answers can vary. A single successful response is an example, not a reliable citation rate. If calculating a rate, define the denominator and keep the question set and method consistent.
Track useful business outcomes alongside visibility: qualified visits, questions answered, appropriate inquiries and conversion quality. An increase in mentions does not by itself demonstrate additional sales or better customer decisions.
Leadership Questions
Where should the team start?
Select a small group of commercially important pages that contain incomplete or confusing information. Establish a baseline, make the facts clearer, and test the results before expanding the work.
Coordinate content owners and technical owners. An accurate page hidden behind an unintended access restriction needs a different repair from an accessible page with misleading claims.
How should resources be allocated?
Prioritize corrections that help people regardless of how they reach the website: accurate descriptions, clear limitations, reliable sources, usable navigation and working links.
Treat new marketing tactics as experiments with a defined cost and success measure. Do not replace established channels solely because a vendor predicts that AI will become the dominant source of discovery.
How can staff contribute?
Sales and support teams can identify recurring questions. Product teams can verify capabilities and limitations. Editors can improve clarity and source quality. Web teams can verify access and measurement.
Give each important page an accountable owner. Have the people who understand the product review its claims before publication, then use customer feedback to identify the next improvement.
Test Prompt Variations Without Treating Them as Ranking Rules
Customers may ask the same underlying question in different ways. Test realistic variations to discover where explanations are incomplete or terminology is confusing.
Use those observations to improve the page's usefulness. There is no established universal three-tier hierarchy of preferred sources in the evidence cited here, and these sources do not show that AI systems categorically exclude affiliate or promotional pages. HRI's framework is a method for disciplined testing, not a description of a proprietary ranking algorithm.
An Implementation Sequence
- Choose the pages and customer questions to assess.
- Verify facts, sources, technical access and indexing controls.
- Improve unclear explanations and inconsistent information.
- Run a documented set of repeatable observations.
- Review results, customer outcomes and remaining gaps.
- Extend changes that prove useful, while continuing to check accuracy.
The durable objective is to make an organization's information trustworthy and easy to use. AI visibility is one outcome to observe as that work proceeds.