AI Search Optimization in Belgium 2026: method and checkpoints

Google states that no special optimisation, specific structured data or minimum text length is required for its AI features. The useful requirements remain familiar: indexable pages, relevant content, understandable internal links, current information and structured data consistent with visible content.

The llms.txt file is a non-standardised community proposal. Google states that it does not use it. It can be tested as supplementary documentation, but must never be presented as a ranking factor or a requirement for visibility.

OpenAI and Perplexity publish their crawler identities so that publishers can define a policy in robots.txt. These documents distinguish search crawlers, training crawlers and user-triggered requests. Allowing a crawler enables access, but guarantees neither indexing, use nor citation.

• Check that important pages respond correctly and are not blocked by mistake.
• Decide explicitly which crawlers may access the website according to your objectives and constraints.
• Maintain a clean sitemap and internal links that make strategic content discoverable.
• Check server logs when you need to confirm that a crawler actually visited.

Strong content answers a real question, distinguishes facts from analysis and links to primary sources where appropriate. There is no universal word count or guaranteed sentence format for being used.

• Give a clear answer before developing the nuances and exceptions.
• Date information that may change, particularly prices, rules and features.
• Identify the author, organisation and editorial responsibilities consistently.
• Use Schema.org only when the markup faithfully describes visible content.
• Avoid mass-produced pages that repeat the same claims without local or professional value.

1. Establish a baseline. Record indexing, pages already generating impressions, professional queries and mentions observed through a documented question set.
2. Fix the foundation. Address technical blocks, contradictory information, missing sources and pages that do not answer the intent precisely.
3. Measure over time. Repeat the same tests, noting the date, tool, mode used and sources displayed. Then compare trends, not an isolated answer.

Results vary according to sector, competition, website authority, information freshness and how the engine works at the time of the test. There is no universal timeline, fixed return on investment or guaranteed citation.

Google includes traffic from its AI features in Search Console data. For other platforms, combine references visible during reproducible tests, visits identified in analytics and, where necessary, server logs. A mention alone does not prove commercial progress.

Track technical discoverability, the frequency of observed mentions, referral visits and attributable conversions separately. This separation avoids turning a promising experiment into an unproven commercial result.

AI Search Optimization complements SEO without replacing it. It is primarily about improving accessibility, editorial quality, fact traceability and measurement. To compare the two perspectives without inventing model-specific rules, read our guide AEO vs SEO: AI answer engines.

To identify the technical and editorial points to examine on your website, request an AI Search audit.