Aon Appears in 92.0% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Answer Capsule

In November 2026, Aon appeared in 207 of 225 AI answers included in the Professional & Management Liability Insurance benchmark (92.00%). It received 43 recommendations (19.11%), with 23 Top 3 placements and 2 first-place recommendations. A mention means the company appeared in an answer; a recommendation means the answer affirmatively endorsed it.

Our plan compares the company with its competitors, checks conflicting AI claims against reliable sources, and identifies website pages, third-party sources and buyer questions to address. The historical results show how the figures changed across available months. Different monthly samples limit those comparisons, and conflicting AI claims require verification before corrections are proposed.

Current AI visibility

Questions This Section Answers

• How do Aon mentions, recommendations and placements compare with its Professional & Management Liability Insurance peers?

Aon recorded 207 mentions (92.00%) and 43 recommendations (19.11%) across 225 answers included in the benchmark. Top 3 placements numbered 23; first-place recommendations numbered 2. Framing among its mentions was 58 positive, 149 neutral and 0 negative.

Aon appears in almost every answer in the benchmark, so reach is not the constraint. The gap sits between being named and being endorsed: recommendations are a much smaller share of mentions, and first-place finishes are rarer still. For a broker competing on advisory credibility, that pattern suggests the answers treat Aon as a familiar name rather than a preferred pick.

The practical question is what the answers say when they do endorse Aon, and whether that wording is specific enough to be useful to a buyer. Proposed work would review the recommending answers for the language used and compare it with how the leading competitor is described, without treating placement counts as additive or as conversion.

What changed since last month

Questions This Section Answers

• How did Aon counts and coverage differ across the available monthly snapshots?

From October 2026 to November 2026, Aon's mention count rose from 56 to 207, while its recommendation count rose from 30 to 43. The recommendation rate was 23.44% and 19.11% respectively.

Between the two reporting months, Aon's mention count and recommendation count both rose, while the recommendation rate was lower in the later sample. First-place finishes were fewer in the later month even as top-three placements increased. These are separate selected samples, so the movement describes what was recorded rather than a trend we can attribute to any cause.

The useful reading is that broader presence did not come with a proportional lift in endorsement. Proposed work would keep the monthly scorecard as a record of observed movement and flag the comparability limit alongside it, rather than building a narrative on a single month's shift.

Step 1. Measure

Questions This Section Answers

• What should the agency measure and verify first for Aon?

AI answers contained 10 conflicting claims about Aon: 0 critical, 0 high, 5 medium, 5 low. Of these, 0 were rated critical or high priority; the critical claims are included in that total. These ratings help prioritize fact-checking. They do not establish that a company or publisher made an error.

The benchmark surfaced a small set of conflicting claims about Aon, mostly rated medium or low priority, with none critical or high. That distribution matters: it tells the client where fact-checking effort is likely to pay off first, and where a claim may simply reflect different dates or definitions rather than an error by anyone.

Proposed work would build a visibility comparison alongside a scoped list of claims to check, each with an owner and an evidence requirement. The ratings guide order of work; they do not establish that Aon or any publisher got something wrong.

Step 2. Make the website consistent

Questions This Section Answers

• Which recorded claim differences about Aon should be verified before proposed page edits?

Both columns quote AI answers, not verified company facts. Resolve the entity, date and source before deciding whether a correction is warranted; different dates can explain an apparent conflict.

The disputed topics cluster around annual revenue and Aon's position in U.S. brokerage revenue rankings. Both recorded versions are AI claims, not verified facts, and a ranking difference can turn on the year, the metric or the source used. Resolving the entity, date and source comes before any decision about a correction.

Proposed work would verify each topic against current primary sources and prepare conditional page briefs. Where the company's own materials already state an approved figure or position, the brief would align the page to it; where the record is genuinely ambiguous, the item stays out of the correction queue.

Step 3. Align the external narrative

Questions This Section Answers

• Which cited third-party pages are relevant to fact review for Aon?

These pages were selected because they relate to the company in the captured answers. Their current contents still need checking before any correction request. A page linked to a conflicting claim is not necessarily its cause, and the same answer may contribute to more than one row.

Several cited pages carry multiple conflicting claims, which makes them sensible starting points for source review rather than targets for a correction request. A page linked to a disputed claim is not necessarily its cause, and the same answer can draw on more than one source. Current contents still need checking before anything is requested.

Proposed work would review the shortlisted pages for what they actually say now, then prepare evidence-backed requests only where a specific, verifiable discrepancy exists. Requests would be framed as clarifications with supporting primary sources, and responses recorded separately from confirmed edits.

Step 4. Optimize priority company pages

Questions This Section Answers

• Which observed Aon website pages should receive review briefs?

The company's website was cited 19 times across 16 distinct page addresses; the table shows 8 pages selected for review. Zeros in the answer columns mean no matching answers were recorded. A cited page is not automatically a page that accompanied a recommendation.

Aon's own pages were cited across a modest set of addresses, and the selected pages include corporate overview material alongside capability pages on cyber and risk management. Citation of a page does not mean it accompanied a recommendation, so the briefs should focus on whether the page gives an answer engine clear, current, approved facts.

Proposed work would draft page briefs using approved company facts and the buyer questions observed in the research, checking existing coverage before proposing anything new. Each brief would name the reviewer and the evidence needed before publication.

Step 5. Publish around buyer questions

Questions This Section Answers

• Which observed buyer questions support content briefs for Aon?

These are recorded answer observations, not search volume, verified demand, or proof of missing website content.

The recorded questions are largely comparative: top brokers, largest retail brokers, best broker, the big three. Aon is mentioned in nearly all of them, but recommendations vary by question, which suggests the answers reward specific, comparable claims rather than general reputation.

Proposed work would turn these observations into buyer-question content briefs, checking first whether Aon already covers each question on its site. The observations are answer records, not search volume or proof of missing content, so briefs would be justified by the question pattern and approved facts rather than by assumed demand.

Our 90-day action plan

Questions This Section Answers

• How should the agency sequence the proposed Aon work and judge completion?

The first month establishes the baseline scorecard, a scoped fact-review register and a shortlist of pages and sources, each with an owner and review status. The second month produces approved page briefs and justified publisher requests, with confirmed edits, no-change decisions and responses recorded separately. The third month adds buyer-content briefs and a fresh scorecard.

Delivered work is recorded apart from measured answer outcomes, and the comparability limits travel with any month-to-month reading. No part of the plan assumes that publishing or a request will produce a visibility gain.

Methodology

Questions This Section Answers

• How are Aon answer metrics and citation populations defined?

This report covers 2026-11. The exact dates when the original AI answers were collected have not been verified. The visibility rates use 225 answers that met the benchmark's inclusion rules. A company is recommended only when the answer both endorses it and uses positive wording. Top 3 and first-place counts are part of those recommendations, not extra recommendations.

An answer can mention or recommend several companies, so company counts cannot be added to find the number of distinct answers. The net sentiment score is positive minus negative mentions, divided by all company mentions; it describes answer wording, not customer satisfaction.

The wider citation analysis covers 700 captured answers and 3782 appearances of cited URLs. Citation appearances across all captured answers differ from the number of benchmark answers citing a page and the number that also recommend a company. The current contents of cited pages and disputed company claims still need independent verification.

Monthly sample coverage:

The answer samples differ between months. These comparisons show how the recorded results changed; they do not establish why they changed.

The report covers a single reporting month, and the exact collection dates for the original answers are unverified. Recommendation requires both endorsement and positive wording, and top-three and first-place counts sit inside recommendations rather than adding to them. Answers can name several companies, so counts cannot be summed into distinct answers.

Citation appearances across all captured answers differ from benchmark answers citing a page and from answers that also recommend a company. Cited page contents and disputed claims still need independent verification, and the monthly samples differ in coverage, so comparisons show what changed, not why.

Our first three priorities

First, verify the revenue and ranking claims against current primary sources and settle the entity, date and metric before deciding whether any correction is warranted. Second, review the most-cited external pages for what they say now, and prepare evidence-backed requests only where a specific discrepancy is confirmed. Third, draft page briefs for Aon's own overview and capability pages using approved facts and the comparative buyer questions observed.

Each item needs a named reviewer and a clear evidence standard. Work completed would be reported separately from measured answer outcomes, and ambiguous matches would stay outside the correction queue.