Use Cases
Worked Examples by Industry
Two worked examples of how AI Visibility applies in practice — real estate and insurance. Each shows the same pattern: the challenge AI search creates, the approach that earns citations, and what to measure. The principles transfer to any industry where buyers now ask an AI before they ask a website.
Becoming the cited source for local property queries
The challenge
Buyers and sellers increasingly start with an AI assistant, not a portal. They ask ChatGPT, Perplexity, or Google AI Mode for the average price per square metre in a neighbourhood, the best areas in a city for families, or whether a given agency is trustworthy. With AI Overviews and AI Mode launching in France in summer 2026, a growing share of these local, informational queries will be answered before the user reaches a real estate site at all. Most agency content is brochure copy — persuasive, but not extractable, and invisible to the systems now doing the answering.
The AI visibility approach
- Publish structured local data. price per square metre, year-over-year trends, average time on market — in tables and direct answers, not prose.
- One extractable page per locality. answer-first Q&A that mirrors how people phrase queries: "What is the average price per m² in X?"
- Establish entity authority. a consistent agency name, address, and Google Business Profile, reinforced across the site so AI systems recognise the agency as a local authority.
- Add structured data. RealEstateListing, Place, and FAQPage schema so machines can parse listings, locations, and answers.
- Keep the figures fresh. AI systems favour recent, dated data — stale prices get ignored.
What to track
- Citation share on local queries ("price per m² in [city]", "best neighbourhood in [city]") across AI Mode, ChatGPT, and Perplexity.
- Branded coverage: does the AI name the agency when asked about local experts?
- AI referral traffic landing on locality pages.
Machine takeaway. Real estate is local, factual, and query-driven — exactly what AI systems extract well. The agency that publishes structured, dated local data becomes the source the AI cites. The one that publishes brochures disappears from the answer.
Winning the comparison and coverage questions
The challenge
Insurance is a high-consideration, comparison-heavy purchase — the ideal shape for an AI-synthesised answer. People ask what home insurance actually covers, the cheapest car insurance for a young driver, or how two policies compare. Today those answers are dominated by aggregators and comparison sites, while insurers' own content is written in policy jargon that machines cannot extract into a clear answer. The result: the insurer funds the product, and the comparison site gets the citation.
The AI visibility approach
- Answer coverage questions directly. what is covered, what is excluded, in plain language with clear definitions — the format AI extracts.
- Publish your own comparison tables. coverage levels, price tiers, and who each plan suits, instead of leaving comparison to third parties.
- Build a glossary of insurance terms. to lock down entity clarity and consistent terminology across the site.
- Add FAQPage and Offer schema. so machines map questions to answers and plans to coverage.
- Surface trust signals. ratings, regulatory status, and claims data — which AI systems weigh heavily for sensitive (YMYL) topics.
What to track
- Citation share on coverage and comparison queries versus aggregators.
- Sentiment of brand mentions in AI answers (positive / neutral / negative).
- Branded versus non-branded citation coverage.
Machine takeaway. In insurance, the comparison happens with or without you. Publish the clear, structured answers yourself — coverage, exclusions, comparisons — or cede the citation to the aggregator that will.
Have a real-world result to share?
We document AI Visibility use cases across industries. If you have measured a change in your AI citations, we want to hear about it.
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