AI Visibility Best Practice Guide

The AI Visibility Best Practice Guide is a playbook for ensuring that your website, brand, or digital assets are discoverable, understandable, and accurately represented by modern AI-powered systems such as Google’s new AI Overviews, ChatGPT, Gemini, Perplexity, and other answer engines.

The guide distils the essential best practices for maximising your brand’s visibility in the AI era, from foundational sitemap audits through advanced schema strategies and extension frameworks like Brando. Each section delivers actionable steps, checklists, and real-world examples to help you structure, secure, and amplify your organisation’s knowledge.

Whether you’re a digital leader, SEO specialist, content strategist, or compliance professional, this guide will help you:

Audit and organise your content for full coverage
Select and implement the right schema types
Deploy and manage structured data at scale
Maintain ethical, compliant, and up-to-date markup
Leverage advanced brand and governance schemas
Monitor and optimise for both search and AI-driven results

Are you ready to make your brand and knowledge truly AI-visible? Let’s get started.

1. Sitemap Audit & Grouping

Purpose: A thorough sitemap audit is the essential first step in any schema project. By inventorying and categorising all site URLs, you ensure no content is overlooked and can plan a phased, efficient rollout of schema markup.

How to do it:

Export your current sitemap or via a crawl tool like Scrapy.
List all unique URLs and group them by content type (e.g., Product, Service, Article, Location, FAQ, Blog).
Identify templates and reusable page layouts; flag unique or one-off pages.
Note which pages are most important for business value, compliance, or traffic.
Document duplicate, outdated, or low-value URLs for possible exclusion from schema work.

Deliverable: A structured inventory (spreadsheet, Airtable, or other database) with URL, content type, template status, priority, and notes.

Why it matters: This foundational audit prevents gaps, uncovers hidden opportunities, and ensures you focus efforts where schema will deliver the most value.

Example Table

URL	Content Type	Template?	Priority	Notes
/products/widget-a	Product	Yes	High	Best-seller
/services/consulting	Service	Yes	Medium
/about/team/jane-doe	Team	No	Low	Key executive
/blog/2024/ai-visibility-guide	Article	Yes	High	Top traffic
/faq	FAQ	Yes	High	Legally required

Checklist

Full sitemap exported and reviewed
Pages grouped by content type
Templates and unique pages identified
Priorities and business-critical sections noted

2. Choose Schema Types

Purpose: Selecting the most relevant schema.org types (and any extension schemas) for each content group ensures your structured data accurately reflects your site’s real-world entities. This helps AI, search engines, and assistants understand and correctly represent your content.

How to do it:

For each content group (identified in your sitemap audit), select the most specific schema.org type available (e.g., Product, Article, FAQPage, Event, LocalBusiness, Service, Person, etc.).
If a content group represents something unique or not fully covered by schema.org, consider leveraging or defining an extension schema (e.g., Brando for brand-specific attributes).
Review the official schema.org documentation or use tools like Schema Markup Generator to explore and confirm your choices.
Document your mapping: each group should have a primary schema type, and note any properties or extensions that will be applied.

Deliverable: A content-to-schema mapping table, showing which schema types (and extensions) are assigned to each group.

Why it matters: Correct type selection is the foundation of effective structured data. The more precise your mapping, the better your site will perform in AI search and rich results.

Example Mapping Table

Content Group	Example URL	Schema Type	Extension Used?
Products	/products/widget-a	Product	No
Services	/services/consulting	Service	No
Blog	/blog/2024/ai-visibility...	BlogPosting	No
FAQ	/faq	FAQPage	No
Brand Profile	/about	Organization	Yes (Brando Schema)

Checklist

Each content group mapped to the most specific schema.org type
Extension schemas noted where standard types are insufficient
Documentation of all schema type assignments

3. Use JSON-LD Format

Purpose: JSON-LD (JavaScript Object Notation for Linked Data) is the preferred format for structured data. It is supported by Google, Bing, and all major AI answer systems (ChatGPT, Gemini, Claude, Perplexity etc), and is easier to maintain than inline microdata or RDFa.

How to do it:

Implement all schema markup on your pages using the JSON-LD format.
Place your JSON-LD scripts inside the <head> or <body> section of each page.
Ensure the data in your JSON-LD matches the visible content users see on the page.
Use tools like Schema Markup Generator to create JSON-LD snippets and Google’s Rich Results Test to validate.

Deliverable: Every key page type contains a <script type="application/ld+json">...</script> block with accurate, well-formatted schema data.

Why it matters: JSON-LD is machine-friendly, easy to debug, and doesn’t interfere with page layout or CMS templates. It’s the industry standard and gives you maximum flexibility for updates and extensions.

Example: Product Schema in JSON-LD

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Widget A",
  "description": "Our flagship widget.",
  "sku": "12345",
  "brand": {
    "@type": "Brand",
    "name": "Acme"
  }
}
</script>

Checklist

All structured data is implemented using JSON-LD
Scripts are placed in the page <head> or at the top of <body>
Content in JSON-LD matches what is shown to users

4. Deploy via Google Tag Manager (GTM)

Purpose: Google Tag Manager (GTM) allows you to centrally manage, update, and scale your schema markup—without direct code changes—especially useful for large, complex, or frequently updated sites.

How to do it:

Set up Google Tag Manager on your website if it isn’t already in place.
Create custom HTML tags in GTM containing your JSON-LD schema for each relevant page or template.
Use GTM triggers to specify which pages or page groups should receive which schema tags, based on URL patterns, page types, or other rules.
When changes are needed, update your schema in GTM and publish—no code deployment required.
Test changes in GTM’s preview mode and validate with tools like Google’s Rich Results Test before going live.

Deliverable: A centrally managed library of schema markup tags within GTM, mapped to the correct pages and easily updateable by authorised team members.

Why it matters: Using GTM streamlines the deployment and maintenance of structured data, reduces dependency on developer resources, speeds up updates, and supports governance and audit trails.

Example Use Cases

Deploy product schema only on /products/* pages.
Apply FAQPage schema to all URLs containing /faq.
Roll out brand or extension schema site-wide for consistent AI visibility.

Checklist

Google Tag Manager installed and configured
JSON-LD schema deployed using GTM tags and triggers
Schema updates and maintenance process documented

5. Ensure Accuracy and Consistency

Purpose: For structured data to be trusted by search engines, AI systems, and users, it must accurately represent the content that is visible on the page. Mismatches can lead to penalties, loss of rich results, or reduced trust in your brand.

How to do it:

Always ensure that information provided in your schema markup (e.g., product name, price, availability, event dates, organisation details) matches exactly what is shown to users on the page.
Avoid including schema properties for data not present or visible on the page.
If the page content is dynamic (e.g., prices, stock, reviews), make sure the schema updates in sync with the page.
Periodically audit your key pages to check for discrepancies between visible content and schema data.
Where possible, automate the extraction of schema data from the same source as the on-page content (e.g., from the CMS or product database).

Deliverable: All structured data is a truthful, up-to-date reflection of what’s visible to users on each corresponding page.

Why it matters: Accurate and consistent markup maintains eligibility for rich results, builds trust with search engines and AI, and protects against penalties or visibility loss.

Checklist

All schema properties reflect visible page content
No “hidden” or misleading markup included
Regular audits/checks for accuracy, especially on key pages

6. Validate Regularly

Purpose: Regular validation ensures your structured data is error-free, up to date, and eligible for rich results or enhanced AI interpretation. Proactive validation catches issues before they impact search performance or AI visibility.

How to do it:

Use Google’s Rich Results Test to check for errors, warnings, and eligibility for rich results on individual URLs.
Use the Schema Markup Validator to validate your JSON-LD, Microdata, or RDFa against schema.org standards.
Monitor Google Search Console (GSC) for site-wide structured data issues, enhancements, and coverage reports.
Address all errors and critical warnings promptly.
Schedule periodic (e.g., monthly or quarterly) reviews—especially after significant website or content changes.

Deliverable: A clean bill of health in GSC and other validators, with no unresolved structured data errors or warnings.

Why it matters: Valid, error-free schema markup maximises eligibility for rich results, supports AI discoverability, and prevents loss of visibility due to technical issues.

Checklist

All key pages tested with Google Rich Results Test
All schema validated with Schema Markup Validator
Google Search Console monitored for ongoing structured data issues
Validation scheduled as a recurring process

7. Prioritise Key Pages

Purpose: Focusing your schema efforts on the most important pages first delivers the quickest return on investment, maximises business impact, and helps secure early wins for your AI visibility strategy.

How to do it:

Identify your website’s high-traffic, high-value, or legally required pages using analytics tools and business input.
Prioritise pages that support business goals (e.g., top-selling products, main service pages, key locations, FAQs, or articles with high engagement).
Address pages that are most likely to qualify for rich results, such as those with reviews, events, jobs, or FAQs.
Consider regulatory requirements—some content (e.g., healthcare, financial, or legal information) may have a compliance imperative for accurate schema.
After implementing and validating schema on these key pages, expand gradually to additional sections.

Deliverable: A phased implementation plan that starts with the most impactful pages, ensuring maximum value from the outset.

Why it matters: Quick wins help demonstrate the value of schema to stakeholders, accelerate learning, and establish best practices before scaling across your full site.

Checklist

High-impact pages identified and prioritised
Schema applied and validated on key sections first
Phased rollout plan documented for broader coverage

8. Keep Schema Up-to-Date

Purpose: Accurate schema markup is only as good as its currency. As your website content evolves—especially dynamic elements like products, events, or offers—your structured data must remain aligned.

How to do it:

Set a regular schedule (e.g., monthly or quarterly) to review and update schema markup across your site.
Immediately update schema whenever you add, change, or remove key page content, such as new products, pricing changes, event dates, or contact information.
Automate updates wherever possible by linking schema generation to your CMS, product database, or content APIs.
After major website updates or redesigns, perform a full audit to ensure schema remains accurate and complete.
Monitor for broken links, outdated properties, or deprecated schema types.

Deliverable: Continuously accurate and up-to-date structured data that reflects the current state of your site.

Why it matters: Up-to-date schema ensures your content remains eligible for rich results, maintains trust with search engines and AI, and avoids penalties or misinformation.

Checklist

Regular review/update schedule established
Schema updated promptly with site/content changes
Automated schema syncing implemented where possible
Full audit after major site updates

9. Document Deployment

Purpose: Comprehensive documentation makes schema management scalable, supports collaboration across teams, ensures continuity, and speeds up onboarding for new staff or agencies.

How to do it:

Maintain a central record of which schema types and properties are implemented on which page templates or URLs.
Document the location of all schema code: in-page, via CMS, or managed in Google Tag Manager.
Record update procedures, including who is responsible, how updates are made (manual/automated), and where version history is kept.
Note the date of last validation or audit, and schedule for the next review.
Store documentation in a location accessible to all relevant stakeholders (e.g., within your CMS, internal wiki, or project management system).

Deliverable: A living schema deployment document or knowledge base, updated with every change.

Why it matters: Documentation reduces errors, prevents loss of institutional knowledge, supports compliance, and ensures the schema strategy remains effective as your site evolves.

Checklist

Schema types and properties mapped to URLs/templates
Deployment methods and locations recorded
Update and review procedures clearly outlined
Documentation is accessible and regularly updated

10. Implement Ethically

Purpose: Trust is fundamental in structured data. Ethically implemented schema maintains credibility with users, search engines, and AI systems, and protects your brand from penalties or loss of trust.

How to do it:

Only mark up content that is genuinely present and visible to users on the page.
Never use schema to misrepresent, exaggerate, or mislead (e.g., claiming awards, reviews, or features that do not exist).
Follow Google’s structured data guidelines and avoid “spammy” or manipulative markup.
Ensure that automated or dynamic schema updates do not introduce inaccuracies or deceptive information.
If content changes, promptly update or remove corresponding schema markup.

Deliverable: All schema markup is honest, accurate, and transparently reflects on-page content.

Why it matters: Ethical implementation builds long-term trust with users and platforms, protects your search and AI visibility, and upholds your brand’s reputation.

Checklist

Only visible content is marked up
No misleading or exaggerated schema used
Automated updates are monitored for accuracy
Team understands and follows ethical guidelines

11. Optimise for Rich Results

Purpose: Rich results (also called rich snippets) enhance your search listings with additional information—such as ratings, FAQs, prices, or images—which can boost click-through rates and brand visibility.

How to do it:

Identify which rich results are most relevant to your content types (e.g., Product, FAQ, Review, Event, Recipe, HowTo).
Review Google’s list of supported rich result types.
Add the required and recommended schema properties for each rich result type you target (e.g., aggregateRating, price, image, acceptedAnswer).
Test your implementation with Google’s Rich Results Test to ensure eligibility.
Monitor performance and refine as needed, updating schema to qualify for new rich result types as they become available.

Deliverable: Key pages are marked up with schema properties that make them eligible for relevant rich results in search.

Why it matters: Rich results can improve search appearance, attract more clicks, and increase trust by providing users with helpful, detailed information at a glance.

Checklist

Target rich result types identified for each content group
Required and recommended schema properties included
Rich Results Test used to confirm eligibility
Performance monitored and optimisations applied

12. Monitor Performance

Purpose: Tracking performance ensures your schema markup is delivering value, remains error-free, and adapts to changes in search algorithms, user behaviour, or business priorities.

How to do it:

Regularly review Google Search Console (GSC) for structured data enhancements, errors, and warnings.
Monitor the appearance and performance of rich results in GSC’s “Performance” and “Enhancements” reports.
Track metrics such as impressions, clicks, click-through rates, and appearance of rich snippets for marked-up pages.
Use web analytics tools (e.g., Google Analytics) to analyse changes in user engagement or conversions on pages with schema markup.
Log incidents, errors, and the resolution of structured data issues for future reference.
Adjust your schema strategy based on what’s working—double down on successful content types and revise areas not driving value.

Deliverable: Ongoing reporting and analytics that demonstrate the impact of schema markup and inform continuous improvement.

Why it matters: Monitoring helps you prove ROI, spot and fix issues quickly, and refine your schema strategy for maximum business and search impact.

Checklist

GSC regularly reviewed for structured data performance and issues
Impressions, clicks, and CTR for rich results tracked
User engagement/conversion metrics monitored
Schema issues and resolutions logged
Strategy adjusted based on performance insights

13. Leverage Extension Schemas (e.g., Brando)

Purpose: Standard schema.org types cover common business needs, but extension schemas allow you to represent unique brand attributes, governance policies, or sector-specific details—giving your brand greater control and depth in AI and search environments.

How to do it:

Identify areas where your brand, governance, or sector requirements go beyond what schema.org provides (e.g., brand tone, approved messaging, compliance signals).
Use or develop an extension schema, such as Brando, to add these custom properties or types.
Define the vocabulary and document its use for your team and partners.
Reference your extension properties alongside standard schema.org types in your JSON-LD markup.
Ensure all custom properties are relevant, accurate, and valuable to downstream consumers like search engines, LLMs, and intelligent assistants.

Deliverable: Structured data that combines standard schema.org properties with brand-specific or governance-related attributes, enhancing discoverability and AI readiness.

Why it matters: Extension schemas enable brands to be more expressive, control their identity in AI-driven spaces, and future-proof their digital assets for emerging search and assistant technologies.

Example

{
  "@context": [
    "https://schema.org",
    "https://brandoschema.com/.well-known/context"
  ],
  "@type": "Organization",
  "name": "Acme Corp",
  "brando:toneOfVoice": "Reassuring, clear, expert"
}

Checklist

Brand/governance needs identified that exceed schema.org
Extension schema selected or defined (e.g., Brando)
Extension properties included in JSON-LD markup
Extension documented for team and partners

14. Host Extension Context at a Public URL

Purpose: To make your extension schema usable and discoverable by AI agents, search engines, and partners, its context file (the “dictionary” of your custom properties) must be hosted at a stable, public location.

How to do it:

Create a JSON-LD context file that defines your custom types and properties.
Host this file at a permanent, public URL—ideally at your main domain using the .well-known/ directory for standards alignment.
Example: https://yourdomain.com/.well-known/context
Alternatively: https://yourdomain.com/context
Ensure the file is accessible (no authentication or blocking in robots.txt).
Keep the context file up to date as your extension schema evolves.

Deliverable: A publicly accessible, standards-compliant URL where your extension schema context can be referenced in JSON-LD.

Why it matters: A well-hosted context file allows search engines, AI, and third parties to correctly interpret your extension properties, ensuring your custom schema is understood and reliably used.

Checklist

Context file created in valid JSON-LD format
Hosted at a stable, public URL (preferably .well-known/context)
File is accessible and not blocked by robots.txt or authentication
Updates managed as schema evolves

15. Reference All Contexts in JSON-LD

Purpose: Properly referencing both standard and extension contexts in your JSON-LD ensures that all properties—whether from schema.org or your own extension—are correctly defined and understood by machines.

How to do it:

In your JSON-LD, use the @context property to specify every vocabulary your markup uses.
For standard schema.org markup, include https://schema.org in the @context.
If you use custom properties from an extension schema (like Brando), add your extension’s context URL as an additional entry in the @context array.
This makes your structured data fully interpretable and compliant with linked data standards.

Deliverable: All JSON-LD markup that uses extension properties includes both the schema.org context and your extension context in its @context array.

Why it matters: Referencing all contexts ensures interoperability, avoids confusion, and future-proofs your markup for more sophisticated AI, search, and partner applications.

Example

{
  "@context": [
    "https://schema.org",
    "https://yourdomain.com/.well-known/context"
  ],
  "@type": "Organization",
  "name": "Acme Corp",
  "brando:toneOfVoice": "Reassuring, clear, expert"
}

Checklist

All standard and extension contexts are referenced in @context
Markup is tested to confirm proper interpretation of all properties
Future extensions can be added easily by including new context URLs

16. Ensure Compatibility and Validate Extensions

Purpose: To ensure your extension schemas (like Brando) work seamlessly with standard schema.org and are correctly interpreted by all consumers—search engines, AI systems, and third-party tools.

How to do it:

Check that custom types and properties do not conflict with existing schema.org vocabulary.
Use established linked data practices for defining custom terms in your extension context file.
Validate your JSON-LD with both standard tools (like Schema Markup Validator) and custom or open-source linked data tools that support extension schemas.
Test interpretation of your markup using LLMs, AI assistants, or validation services that can consume both standard and extension schemas.
Stay up to date with schema.org releases and adapt your extension if standard types/properties become available.

Deliverable: Fully interoperable structured data that works with both core schema.org and your extension, error-free and aligned with best practices.

Why it matters: Compatibility and proper validation are crucial for adoption, interoperability, and ensuring your extension properties add value rather than causing confusion or errors.

Checklist

No conflicts between extension and schema.org types/properties
Custom terms defined with linked data best practice
Markup validated with standard and extension-aware tools
Markup tested for AI/LLM interpretation
Extension updated if schema.org adds relevant types

17. Document and Monitor Extension Usage

Purpose: Systematic documentation and monitoring of your extension schemas ensure consistency, governance, and the ability to scale or adapt your structured data strategy as needs evolve.

How to do it:

Maintain a central log or documentation of all places where your extension schema (e.g., Brando) is implemented across your website or digital properties.
Record which properties are used, in which content types or templates, and the purpose for each.
Track any updates, additions, or removals of extension properties—ideally using version control or changelogs.
Monitor for issues such as errors in Search Console, validation failures, or changes in how search engines/AI interpret your extensions.
Periodically review and audit extension usage for relevance, accuracy, and ongoing business value.

Deliverable: An up-to-date record of all extension schema usage and a clear process for monitoring, auditing, and updating as needed.

Why it matters: Good documentation and monitoring reduce risk, support team collaboration, and make it easier to scale or update your schema as technologies, regulations, or business requirements change.

Checklist

All uses of extension schemas are logged and documented
Updates and changes are version-controlled
Regular audits for accuracy and relevance
Monitoring in place for validation issues or changes in ecosystem support

18. Communicate Extension Value

Purpose: Ensuring all relevant stakeholders understand the purpose and benefits of your extension schemas (like Brando) drives adoption, supports consistent implementation, and maximises business value.

How to do it:

Develop clear documentation and internal guides that explain what your extension schema is, why it’s used, and how it benefits the organisation (e.g., improved brand control, AI discoverability, governance).
Present use cases, real-world examples, and measurable outcomes to non-technical and technical teams alike.
Provide training sessions or workshops for content, web, and SEO teams to build understanding and buy-in.
Include extension schema awareness in onboarding for new team members and external partners or agencies.
Share successes and improvements that result from extension schema use to reinforce ongoing commitment.

Deliverable: Stakeholders at all levels are informed, confident, and supportive of the extension schema strategy.

Why it matters: Widespread understanding and buy-in ensure correct, consistent implementation and unlock the full business and brand value of your extension schema investments.

Checklist

Clear, accessible documentation and guides available
Use cases and benefits communicated to stakeholders
Training/workshops offered for key teams
Extension schema included in onboarding
Success stories and business impact shared regularly