When people in the US ask AI which insurer to use, who gets named?
We put 100 real buyer questions — "best car insurance", "best home insurance", "broker or buy direct", "how to lower my premium" — to ChatGPT and Gemini and Perplexity and checked which of 26 identifiable U.S. insurance brands each answer named. 16 of the 100 questions got no named brand at all — AI answered them with regulators, review sites, or nobody. Run the check for your brand →
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Why we ran this
Buying or switching insurance is now something people ask an AI assistant, not just a comparison site. "Best car insurance", "is it cheaper to go direct or through a broker", "which insurer pays claims fastest" — the assistant reads a handful of sources and either names specific brands or it doesn't. This benchmark reads how visible U.S. insurance brands currently are, engine by engine, and where the openings are — including whether direct carriers, comparison platforms and digital-first insurtechs are treated differently.
How we measured it
We wrote 100 questions in the language a buyer actually uses, across six clusters (choosing an insurer or broker; car & motor; home & contents; life, health & income protection; price, switching & claims; business & specialist cover — all 100 are explorable in full below). Each question was sampled once per engine, and every answer was checked for a mention of any of 26 identifiable U.S. insurance brands — direct carriers like State Farm and Progressive, comparison marketplaces like Policygenius and The Zebra, and digital-first insurtechs like Lemonade and Root — using the same name matcher a live Inserviss scan uses. The tracked list is grounded in web research, not guesswork.
A single pass per engine establishes direction and priority; it is not a trend line. Citations are reported as measured; query volumes are labelled estimates, shown as ranges. Engines are never blended. This run covers ChatGPT and Gemini and Perplexity; Microsoft Copilot is not currently sampled.
The tracked list covers the 26 brands a buyer would plausibly consider, whether or not any engine named them. Eight were added after the first run — American Family, Erie, The Hartford, Chubb, AIG, MetLife, Auto-Owners and NerdWallet — and re-matched against the same stored answers, with no new sampling, so every figure here rests on identical evidence.
What we found, at a glance
- Visibility varies sharply by engine. ChatGPT named a tracked brand in 43 of 100 answers, Gemini named a tracked brand in 83 of 100 answers and Perplexity named a tracked brand in 53 of 100 answers.
- 16 of 100 questions got no tracked brand on any engine — the demand goes to regulators, publishers and review aggregators instead. Weakest: Life, health & income protection, Price, switching & claims.
- State Farm is the most-named brand, appearing in 103 of 300 answers. Only 25 of the 26 tracked brands were named even once.
- Distribution model matters less than you’d think. Whether a brand sells direct, sits on a comparison platform or is a digital-first insurtech, the table below shows how thin the coverage is across all three.
How often does AI name a brand at all?
Below is the share of each engine's answers that named at least one tracked brand — then the full table, every brand against every engine, sortable by any column.
Share of each engine's answers naming at least one of the 26 tracked brands. Per engine, never blended.
| Firm | ChatGPT | Gemini | Perplexity |
|---|---|---|---|
| State Farm | 16 | 55 | 32 |
| Progressive | 16 | 42 | 15 |
| USAA | 13 | 34 | 24 |
| GEICO | 12 | 35 | 18 |
| Travelers | 8 | 31 | 22 |
| Nationwide | 6 | 36 | 18 |
| Erie Insurance | 12 | 22 | 15 |
| Allstate | 4 | 24 | 7 |
| Chubb | 5 | 18 | 7 |
| Lemonadeinsurtech | 4 | 16 | 3 |
| Auto-Owners Insurance | 7 | 12 | 4 |
| NerdWallet | 17 | 0 | 5 |
| The Hartford | 3 | 11 | 3 |
| Liberty Mutual | 2 | 9 | 5 |
| Policygenius | 3 | 7 | 3 |
| Farmers Insurance | 0 | 10 | 2 |
| American Family Insurance | 2 | 2 | 5 |
| Insurify | 2 | 3 | 2 |
| The Zebra | 1 | 2 | 3 |
| AIG | 1 | 3 | 2 |
| Root Insuranceinsurtech | 1 | 2 | 2 |
| MetLife | 2 | 1 | 1 |
| Branchinsurtech | 0 | 2 | 0 |
| EverQuote | 0 | 1 | 0 |
| Hippoinsurtech | 0 | 1 | 0 |
| Goosehead Insurance | 0 | 0 | 0 |
Mentions across 100 answers per engine. Select a column heading to re-sort. Scroll the table sideways on a narrow screen.
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Where the openings are
Coverage varies by question type. Below is the best engine’s coverage for each cluster, out of ~17 questions.
Answers naming a brand on the strongest engine, per cluster (out of ~17).
How this is estimated
Estimate — a range, not a keyword-tool export. Illustrative estimate, not a keyword-tool export. The U.S. consumer-insurance search surface is roughly 4× the English-language European one for the same lines. Per cluster: a few head terms at ~8,000–35,000/mo, ~16 long-tail intent phrases at ~600–3,500/mo, and an AI-assistant reformulation multiplier of ×2–4 for the wider phrasing people use with an assistant. Ranges are rounded and widened. For a specific brand, the real number comes from the check, not this table.
| Cluster | Best-engine coverage | Est. questions / mo |
|---|---|---|
| Choosing an insurer or broker | 17/17 | 22,000–95,000 |
| Car & motor insurance | 17/17 | 50,000–200,000 |
| Home & contents insurance | 14/17 | 32,000–135,000 |
| ●Life, health & income protection | 10/17 | 26,000–110,000 |
| ●Price, switching & claims | 11/16 | 22,000–95,000 |
| Business & specialist cover | 14/16 | 18,000–78,000 |
Volumes are an estimate, shown as a range. Illustrative estimate, not a keyword-tool export. The U.S. consumer-insurance search surface is roughly 4× the English-language European one for the same lines. Per cluster: a few head terms at ~8,000–35,000/mo, ~16 long-tail intent phrases at ~600–3,500/mo, and an AI-assistant reformulation multiplier of ×2–4 for the wider phrasing people use with an assistant. Ranges are rounded and widened. For a specific brand, the real number comes from the check, not this table.
The check runs this same 100-question universe against your brand’s name, per engine, and returns your AI Visibility Score plus the exact questions where a competitor — or no one — is being named.
Check your brand’s AI visibility →Explore the 100 questions
Every question, and exactly what each engine did with it — which brands it named, and which sources it pulled from. Filter by cluster, search for your own name, or show only the questions no brand has claimed.
The 16 questions no tracked brand owns on any engine, with the sources AI cites instead — a ready-made content brief.
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Where each engine gets its answers
The domains each engine actually pulled from — never blended. The mix differs sharply by engine: some answers are built almost entirely from regulators, publishers and review aggregators, others from individual brand websites, comparison sites and Reddit threads. Where a tracked brand's own site appears, it's marked in blue below.
Times a domain was cited across the 100 answers. Blue = a tracked brand's own site.
About this study
- Single pass per engine. One sample per question per engine establishes direction; it does not average out run-to-run answer variance.
- Snapshot in time. Collected September 10, 2026. AI engines change their answers week to week; this is not a trend line.
- Slot-parallel question set to the Europe study. The two region-dedicated insurance studies use the same six clusters and the same buyer intent question by question, phrased for each market, so the U.S. and Europe are directly comparable.
- ChatGPT and Gemini and Perplexity this run. Microsoft Copilot is not currently sampled; the measurement is identical when it is.
How to cite this study
To request the underlying data or discuss a brand-specific analysis, contact igor@inserviss.app.
The same measurement, run against your name — per engine, question by question — with your score and the questions to claim first.
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