What To Do

• Publish "answer blocks" — self-contained sections with an unambiguous definition or fact, followed by supporting evidence. Use headings phrased as user questions where natural.
• Build authority beyond owned media: credible third-party mentions, expert bylines, and independent references. AI systems rely heavily on broader reputation signals, not just your own claims.
• Verify that AI crawlers can access the content you want cited. A good robots.txt policy matters more than most marketers realise (see Q10).

What To Do

• Maintain a conventional SEO baseline — crawlability, topical coverage, internal linking, performance — then add a "citation readiness" layer: answer-first sections, clarity, verifiable evidence.
• Update your reporting. Treat "share of answer" and "share of AI voice" as first-class KPIs alongside traffic and rankings.

What To Do

• Create an internal glossary specifying what GEO / AEO / LLMO / AIO means in your organisation: which platforms, which metrics, which pages are in scope.
• Document acronym collisions for stakeholders — especially in regulated European contexts where "AEO" also refers to Authorised Economic Operator (customs). This confusion appears repeatedly in European practitioner sources.

What To Do

• Establish a clear "rights and policy" stance: decide whether you permit AI crawling for training vs. citation/search, and document that decision — it will increasingly need to be defensible.
• Build provenance into your content: human editorial review, explicit citations, and named author credentials. This makes content more citable and more compliant as EU transparency expectations evolve.
• If you generate AI-assisted content, start audit trails now. Draft EU guidance is moving toward disclosure requirements.

What To Do

• Build a repeatable test harness: a fixed set of prompts covering key topics, rerun on a regular schedule, recording which sources are cited and how answers evolve over time.
• Use Bing AI Performance in Webmaster Tools to observe your site's performance in AI-generated experiences — currently the most structured platform-native reporting available.
• Layer in server-log analysis to monitor AI bot access patterns and analytics tagging to capture AI referral traffic when it does occur.

What To Do

• Implement llms.txt as an experimental layer, not a replacement for well-structured crawlable HTML. Keep it accurate, minimal, and pointing to canonical pages.
• For developer-facing products, consider an "instructions" section (as Stripe has explored) to help LLM agents route users to the right documentation.
• Set a quarterly review cadence — this standard is actively evolving.

What To Do

• Treat "being cited" as a downstream result of being a clean, verifiable building block: auditable facts, regular updates, clear separation of opinion from factual claims.
• Monitor platform-specific citation patterns separately for AI Overviews, ChatGPT, Perplexity, and Copilot — they have different selection behaviours. Don't assume rank transfers across surfaces.

What To Do

• Prioritise schema types that map to "answerable chunks": FAQPage, HowTo, Article with clear metadata, and entity types (Organisation, Person) where relevant.
• Don't rely on markup alone. Structural clarity in the prose itself — concise answer blocks, descriptive headings, minimal jargon — is more important than schema without readable content behind it.

What To Do

• Start minimal: a prompt-based citation monitor (run key queries across ChatGPT, Perplexity, Gemini, Bing Chat on a schedule) + server log monitoring + standard SEO crawl audits.
• Add specialised AI visibility tooling only once you can validate it surfaces actionable findings you wouldn't have caught otherwise.

What To Do

• Define an explicit policy: list which bots are permitted for citation/search, which are disallowed for training, and why. Document the reasoning, not just the directives.
• Validate via server logs and citation tracking — don't assume directives are respected without evidence.
• Set a regular review cadence. New bots and more granular user agents are emerging frequently. A robots.txt from mid-2024 is likely already outdated.