Lockton Appears in 33.8% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Answer Capsule

In November 2026, Lockton appeared in 76 of 225 AI answers included in the Professional & Management Liability Insurance benchmark (33.78%). It received 11 recommendations (4.89%), with 2 Top 3 placements and 1 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 Lockton mentions, recommendations and placements compare with its Professional & Management Liability Insurance peers?

Lockton recorded 76 mentions (33.78%) and 11 recommendations (4.89%) across 225 answers included in the benchmark. Top 3 placements numbered 2; first-place recommendations numbered 1. Framing among its mentions was 19 positive, 57 neutral and 0 negative.

Lockton appears in roughly a third of the benchmark answers, which gives it real reach among the brokers discussed. The gap sits in endorsement: recommendations are far fewer than mentions, and top placements are rarer still. Reach and recommendation are separate measures, so a strong mention base does not by itself translate into being named as the preferred broker.

Framing is largely neutral, with positive mentions outweighing negative ones and no negative wording recorded. That is a reasonable starting position: the company is present and not being criticised, but it is rarely the answer's chosen recommendation. The agency would treat this as a recommendation problem rather than a visibility problem, and focus first on where Lockton is mentioned but not endorsed.

What changed since last month

Questions This Section Answers

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

From October 2026 to November 2026, Lockton's mention count rose from 29 to 76, while its recommendation count fell from 13 to 11. The recommendation rate was 10.16% and 4.89% respectively.

Between the two reporting months, Lockton's mention count rose while its recommendation count fell slightly. The recommendation rate moved in the same direction as the count. These are separate selected samples, and the different answer totals mean the two months are not directly comparable; the movement is recorded, not explained.

The useful reading is that reach and endorsement moved apart. A larger mention base with fewer recommendations suggests the company is being discussed in more answers without being endorsed in proportion. The agency would avoid treating either month as a trend and would use the next scorecard to see whether the pattern holds across comparable samples.

Step 1. Measure

Questions This Section Answers

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

AI answers contained 7 conflicting claims about Lockton: 0 critical, 0 high, 1 medium, 6 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 recorded a small set of conflicting claims about Lockton, mostly low severity, with one rated medium. None were rated critical or high. The ratings indicate where fact-checking effort is best spent; they do not show that the company or any publisher got something wrong.

A visibility comparison alongside a short claims list would let the client see where Lockton stands relative to peers and which disputed points deserve attention first. The agency would build a scoped fact-review register, assign an owner to each item, and require a primary source before any correction is considered.

Step 2. Make the website consistent

Questions This Section Answers

• Which recorded claim differences about Lockton 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 concern employee headcount and annual revenue, including figures that differ by date or scope. Both sides of each conflict are AI answer wording, not verified company facts. Some apparent conflicts may dissolve once the relevant date and source are pinned down.

The agency would verify each topic against current primary sources before drafting anything. Where a page brief is warranted, it would use approved facts and state the reporting period clearly. Items that cannot be resolved, or that involve an ambiguous company match, would stay outside the correction queue rather than being forced into a fix.

Step 3. Align the external narrative

Questions This Section Answers

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

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 relate to Lockton and to the disputed claims, including industry tables, a study release, an encyclopedia entry and employer-review sites. Citation does not establish that a page influenced an answer, and a page linked to a conflicting claim is not necessarily its cause. Current contents still need checking.

The agency would review the most relevant pages first, confirm what they currently say, and only then decide whether an evidence-backed request to a publisher is justified. Requests would be framed around verified facts, with no assumption of publisher error and no expectation of control over what a third party publishes.

Step 4. Optimize priority company pages

Questions This Section Answers

• Which observed Lockton website pages should receive review briefs?

The company's website was cited 23 times across 20 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.

Lockton's own site was cited across many distinct pages, though the selected pages show little overlap with answers that recommend the company. A cited page is not automatically one that accompanied a recommendation, so this is a signal to examine rather than a verdict.

The agency would prepare page briefs for the priority pages using approved facts and the questions buyers actually ask. Each brief would note the audience, the verified figures and the reporting period, and would check existing coverage before proposing new content. Pages with no recorded recommending answers would be reviewed for clarity rather than assumed to be underperforming.

Step 5. Publish around buyer questions

Questions This Section Answers

• Which observed buyer questions support content briefs for Lockton?

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

The recorded questions cluster around broker comparisons and category-specific needs, such as commercial liability, cyber, property and employee benefits. These are answer observations, not search volume or proof of demand, and the company was mentioned in only some of the answers to each.

The agency would check whether Lockton already covers each question on its site before proposing new pages. Where coverage exists, briefs would sharpen the answer; where it does not, a buyer-question brief would set out the verified facts and the audience. Recommendations in these answers are sparse, so the content would aim at being endorsement-ready rather than merely present.

Our 90-day action plan

Questions This Section Answers

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

The first month establishes the baseline: a scorecard, a scoped fact-review register and a shortlist of pages and sources, each with an owner, an evidence requirement and a review status. This gives later work something verifiable to stand on.

The second month produces approved page briefs and any justified publisher requests, with confirmed edits, no-change decisions, requests and responses recorded separately. The third month adds buyer-content briefs and a fresh scorecard. Completed work would be reported separately from measured answer outcomes, with comparability limits stated plainly and no claim of guaranteed gains.

Methodology

Questions This Section Answers

• How are Lockton 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 answers were collected on dates that have not been verified, and the two months used different samples and different sets of AI services. Comparisons show how recorded results changed, not why. A company counts as recommended only when an answer endorses it with positive wording, and top placements sit inside those recommendations rather than adding to them.

Answers can mention several companies, so counts cannot be summed to find distinct answers. Sentiment describes answer wording, not customer satisfaction. Cited pages and disputed claims still require independent verification before any correction is proposed.

Our first three priorities

First, verify the headcount and revenue claims against current primary sources, recording the date and scope for each. This settles which conflicts are real and which reflect different reporting periods, and it keeps unverified items out of any correction request.

Second, prepare page briefs for the priority company pages, using approved facts and the buyer questions observed in the research. Existing coverage would be checked first, so new content is proposed only where a genuine gap remains.

Third, review the most relevant cited sources and decide whether any evidence-backed publisher request is warranted. Throughout, the agency would keep completed work separate from measured visibility, and would use the next scorecard to see whether recommendation reach improves without claiming that any single action caused it.