Part of the EPR GEO Scorecard series, Everything-PR's quarterly measure of AI Citation Share by vertical.
Disclosure: Everything-PR and 5W AI Communications share common ownership. Everything-PR reports independently on the communications industry, including on research produced by 5W. Editorial decisions are made by Everything-PR's editorial team.
"Insurance brokers spent a century teaching clients to disclose every risk in writing. The AI engines are simply the first client that reads the filing."
Ronn Torossian, Publisher, Everything-PR · Founder and chairman, 5W AI Communications
Executive Summary: Commercial Insurance Brokers GEO Performance
Commercial insurance brokers exhibit the highest GEO score floor of any professional services vertical measured, largely due to a high proportion of publicly traded firms. Marsh McLennan leads the cohort with a score of 86 (A), followed by Aon at 81 (A), and Gallagher at 76 (B+). WTW scored 71 (B), Brown & Brown 66 (C), Ryan Specialty Holdings 62 (C), Lockton 56 (D), and Alliant Insurance Services 50 (D).
Six of the eight firms evaluated are publicly traded, meaning they have SEC disclosure obligations. This regulatory requirement ensures a consistent flow of structured, dated information, which AI engines retrieve reliably, establishing a strong baseline for the industry.
Marsh McLennan's top performance stems from its scale, diversification, and public disclosure, augmented by its Oliver Wyman consulting arm, which serves as a secondary research-publication engine. This structural advantage, which was also scored independently in Vol. 9's management consulting volume, provides Marsh McLennan with an additional layer of retrievable content.
The two private firms in the cohort, Lockton and Alliant Insurance Services, rank at the bottom. Lockton, despite being the largest independently owned broker, demonstrates that private ownership remains the clearest predictor of a lower GEO score. This trend holds even in categories where public disclosure is common.
Who This Report Helps
This report targets risk managers and CFOs evaluating broker relationships, insurance carrier partnership teams, M&A advisors tracking broker consolidation, insurance industry journalists, and communications professionals advising insurance and risk-management clients on AI visibility.
Key Findings from the Commercial Insurance Broker GEO Scorecard
- Most firms in this cohort are publicly traded. Six of the eight firms in this cohort are publicly traded, representing the highest public-company share of any professional-services volume in the series. The category's overall GEO score floor is consequently the highest measured to date.
- Marsh McLennan has a distinct content advantage. Marsh McLennan's Oliver Wyman consulting arm, previously scored independently in Vol. 9, provides the parent company with a second content-publication engine that competitors like WTW and Gallagher lack.
- Private firms rank lowest in AI visibility. Lockton and Alliant Insurance Services, both private firms, occupy the bottom two positions despite Lockton being the largest independently owned broker globally by revenue.
- AI engines consistently name Marsh McLennan as the largest broker. Asked "largest insurance broker in the world," five of five AI engines name Marsh McLennan first. This aligns with its 15 consecutive years atop industry revenue rankings.
- Aon's unified brand narrative aids AI retrieval. Aon's "Aon United" rebrand and operating-model consolidation are retrieved on four of five engines, providing the firm with a structured corporate narrative that reinforces its long-held second-place industry position.
- WTW's divestiture remains a key part of its AI-retrieved narrative. WTW's 2025 divestiture of TRANZACT, its direct-to-consumer distribution business, is retrieved on reputation and corporate queries across three of five engines. This specific, dated financial event continues to shape the firm's AI-retrieved narrative months later.
- Gallagher's acquisitions drive significant citation. Gallagher's aggressive acquisition strategy, including its 2021 purchase of WTW's treaty reinsurance operations, is the most-cited growth narrative in the cohort. M&A activity generates retrievable trade coverage similar to CAA's dealmaking in Vol. 11.
- Commercial insurance brokers show high crawl access. Crawl Access is meaningfully higher across this cohort, with an average score of 58, compared to any prior professional-services volume. This reflects the greater indexability of public-company investor-relations sites, which is necessary due to regulatory requirements.
- Perplexity excels with disclosure-heavy categories. Perplexity produces the highest score for five of eight firms. Its real-time retrieval of SEC filings and quarterly earnings commentary directly matches the disclosure-heavy nature of this category.
- Ryan Specialty Holdings' specialty focus limits query breadth. Ryan Specialty Holdings, a public company focused on wholesale and specialty insurance distribution, scores 62 (C) despite its public status. Its narrower specialty focus limits Query-Type Breadth compared to the full-service "Big Four" brokers.
- Brown & Brown's regional focus impacts global AI visibility. Brown & Brown, publicly traded but regional in orientation relative to the global "Big Four," scores 66 (C). Public disclosure alone does not close the gap created by a smaller, more concentrated geographic footprint.
- Lockton's prestige does not translate to AI retrieval without publication. Lockton, the largest private broker globally, scores only 56 (D). This directly parallels Vol. 11's finding that institutional prestige, such as Egon Zehnder's, does not translate to AI retrieval without accompanying publication.
- Most public firms use Organization schema, but not FAQ schema. No firm in the cohort, public or private, utilizes FAQ schema on its public-facing website. However, five of six public firms use Organization schema, marking the first time a majority of any Scorecard cohort has cleared this standard.
- Marsh is the go-to for cyber insurance queries. Asked "which broker is best for cyber insurance placement," Marsh is named first by four of five engines. This reflects the firm's position as the largest cyber-insurance broker globally.
- The commercial insurance broker category has high potential for AI visibility. The commercial insurance broker category ceiling (86) is the second-highest of any professional-services vertical measured, trailing only management consulting (88). This confirms that public-disclosure obligations combined with genuine research-publication infrastructure produce the strongest AI visibility outcomes among tested professional-services business models.
EPR GEO Scorecard Methodology
Scores are based on observed AI engine outputs during an August 2026 test window. Five engines were used: ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. Researchers conducted 50 prompts per firm across five query buckets (Recommendation, Comparison, Capability, Reputation, Corporate), with 10 prompts for each bucket. This totaled 2,000 individual response audits across the eight firms.
Each response was scored on a binary (cited/not cited) and qualitative (primary mention, secondary mention, absent) basis. Raw observations were normalized into the five-dimension framework: Citation Frequency (40%), Cross-Engine Breadth (20%), Query-Type Breadth (20%), Extractability (15%), and Crawl Access (5%). The methodology is identical to Vols. 1-11 and is reproduced quarter-over-quarter. Full protocol details are available at the EPR GEO Scorecard hub.
What Do AI Engines Say About Insurance Brokers?
We ran queries that a CFO, risk manager, and portfolio company CEO would typically search. Here are the results of which firms the AI engines named first:
| Prompt | ChatGPT Names First | Perplexity Names First | Gemini Names First |
|---|---|---|---|
| "Largest commercial insurance broker" | Marsh McLennan (revenue) | Marsh McLennan (SEC filings) | Marsh McLennan (industry rank) |
| "Best broker for cyber insurance" | Marsh (cyber leadership) | Marsh (cyber leadership) | Aon (mentioned second) |
| "Which broker is growing fastest through acquisitions" | Gallagher (M&A pace) | Gallagher (M&A pace) | Gallagher (acquisition count) |
| "Largest independently owned insurance broker" | Lockton | Lockton | Lockton |
| "Best broker for employee benefits consulting" | Marsh McLennan (Mercer) | WTW | Marsh McLennan (Mercer) |
| "WTW TRANZACT sale" | Divestiture, direct-to-consumer exit | Divestiture, cash-flow headwind | Divestiture, strategic refocus |
Marsh McLennan consistently appears as the default answer across various queries, similar to McKinsey's performance in Vol. 9. This is due to its combination of scale, public disclosure, and its Oliver Wyman research arm. Specific competitive niches, however, show clear fragmentation.
Gallagher dominates the M&A growth narrative, while WTW holds mindshare for employee benefits on at least one engine. Lockton's private status is itself a retrievable fact that AI engines consistently surface. This is the first Scorecard volume where the category's strong public-disclosure norm means a firm's private status becomes a well-documented data point rather than an information vacuum.
Commercial Insurance Broker GEO Scores
The table below details the GEO Scorecard results for each firm across five key dimensions.
| Dimension (weight) | Marsh McLennan | Aon | Gallagher | WTW | Brown & Brown | Ryan Specialty | Lockton | Alliant |
|---|---|---|---|---|---|---|---|---|
| Citation Frequency (40%) | 90 | 86 | 80 | 76 | 70 | 66 | 58 | 52 |
| Cross-Engine Breadth (20%) | 88 | 84 | 78 | 74 | 68 | 64 | 56 | 50 |
| Query-Type Breadth (20%) | 84 | 78 | 72 | 68 | 62 | 56 | 52 | 46 |
| Extractability (15%) | 80 | 74 | 70 | 64 | 58 | 54 | 50 | 44 |
| Crawl Access (5%) | 72 | 68 | 64 | 60 | 56 | 52 | 40 | 36 |
| FINAL GRADE | 86 · A | 81 · A | 76 · B+ | 71 · B | 66 · C | 62 · C | 56 · D | 50 · D |
Engine Performance Heatmap for Insurance Brokers
This table illustrates how each firm performed across the five AI engines tested.
| Firm | ChatGPT | Claude | Gemini | Perplexity | Google AIO | Avg |
|---|---|---|---|---|---|---|
| Marsh McLennan | 88 (A) | 84 (A) | 82 (A) | 92 (A) | 84 (A) | 86 |
| Aon | 82 (A) | 80 (A) | 78 (B) | 86 (A) | 78 (B) | 81 |
| Gallagher | 78 (B) | 74 (B) | 72 (B) | 82 (A) | 74 (B) | 76 |
| WTW | 72 (B) | 68 (C) | 66 (C) | 78 (B) | 68 (C) | 71 |
| Brown & Brown | 68 (C) | 64 (C) | 60 (D) | 72 (B) | 62 (D) | 65 |
| Ryan Specialty | 64 (C) | 60 (D) | 56 (D) | 68 (C) | 58 (D) | 61 |
| Lockton | 58 (D) | 54 (D) | 50 (D) | 62 (D) | 48 (F) | 54 |
| Alliant Insurance Services | 50 (D) | 46 (F) | 44 (F) | 56 (D) | 42 (F) | 48 |
Marsh McLennan stands out as the only firm in the cohort to achieve an "A" grade across all five AI engines. Aon also performs strongly, scoring an "A" on four of five engines, with Gemini and Google AIO being the exceptions at a "B" grade. Perplexity consistently produces the highest score for every firm in the cohort, marking its strongest performance across any Scorecard volume, driven by its direct retrieval of SEC filings and quarterly earnings transcripts. Conversely, Google AIO is the weakest engine specifically for the two private firms, yielding the only two "F" grade results in this entire volume.
Company Deep Dives
Marsh McLennan, 86 (A)
Marsh McLennan, founded in Chicago in 1905, trades on the NYSE as MRSH. The firm reported revenue of approximately $27.0 billion in 2025 and employs around 95,000 people. It serves 95% of Fortune 1000 companies and is led by CEO John Q. Doyle. Its subsidiaries include Marsh, Guy Carpenter, Mercer, and Oliver Wyman.
What's Working for Marsh McLennan
Marsh McLennan has been the world's largest insurance broker by revenue for 15 consecutive years, a fact consistently retrieved by AI engines and reinforced by SEC filings, quarterly earnings calls, and 17 consecutive years of reported margin expansion. The Oliver Wyman consulting arm, independently scored in Vol. 9 at 58 (D) for its consulting-market performance, provides the parent company with a second content-publication engine most competitors lack. Its leadership in cyber insurance is also a well-documented and frequently retrieved specialty.
What's Hurting Marsh McLennan
Crawl Access (72) is the highest in the cohort but still presents room for improvement relative to consulting's public leaders in Vol. 9. The four-subsidiary structure (Marsh, Guy Carpenter, Mercer, Oliver Wyman) occasionally leads to entity confusion on queries that do not specify the intended business line.
What Moves Marsh McLennan's Score
Marsh McLennan can achieve clearer entity disambiguation across its four subsidiary brands. The firm should extend crawl access on subsidiary-level content to match the parent company's site. Estimated lift: 2-4 points within two quarters.
Aon, 81 (A)
Aon's origins trace to predecessor firms founded in 1919, with its current form established through a 1982 merger. It trades on the NYSE as AON and reported revenue of approximately $15.7 billion in 2025. Aon has been the second-largest global broker for 15 consecutive years.
What's Working for Aon
Aon's "Aon United" operating-model consolidation is retrieved on four of five engines, providing the firm with a structured, current corporate narrative. Public-company disclosure generates reliable quarterly financial data that AI engines consistently retrieve. The firm also exhibits strong secondary retrieval on cyber-insurance queries, behind Marsh McLennan.
What's Hurting Aon
Gemini and Google AIO both cap Aon at a "B" grade rather than an "A," indicating these are the two weakest engines for the firm within this cohort. Aon lacks a distinct consulting-research arm comparable to Marsh McLennan's Oliver Wyman relationship.
What Moves Aon's Score
Aon could improve its score with content specifically targeting Gemini and Google AIO's retrieval patterns, likely through deeper structured data and Wikipedia enhancement. Estimated lift: 3-5 points within two quarters.
Gallagher, 76 (B+)
Gallagher was founded in 1927 and trades on the NYSE as AJG. The firm reported revenue of approximately $11.3 billion in 2025. It is the third-largest global broker, a position achieved after overtaking WTW following its 2021 acquisition of WTW's treaty reinsurance operations.
What's Working for Gallagher
Gallagher's aggressive acquisition strategy generates the highest volume of trade-press M&A coverage in the cohort. This M&A coverage is a retrieval mechanism paralleling CAA's dealmaking coverage in Vol. 11. The 2021 WTW treaty reinsurance acquisition is a specific, well-documented, and frequently retrieved growth narrative.
What's Hurting Gallagher
Gallagher's Query-Type Breadth (72) trails both larger competitors. The firm's growth-through-acquisition narrative dominates retrieval at the expense of query types unrelated to M&A activity.
What Moves Gallagher's Score
Gallagher can diversify its published content beyond acquisition announcements to include broader risk-management and specialty-insurance thought leadership. Estimated lift: 4-7 points within three quarters.
WTW, 71 (B)
WTW's roots trace to Towers, Perrin, Forster & Crosby (1934) and Willis Group (1828), with its current form established via the 2016 Willis Towers Watson merger. It trades on the NASDAQ as WTW and reported revenue of approximately $9.9 billion in 2025. WTW is currently the fourth-largest global broker.
What's Working for WTW
WTW exhibits strong retrieval on employee-benefits consulting queries, named first by Perplexity specifically. The firm's long institutional history, spanning both the Willis and Towers Watson predecessor brands, provides deep Wikipedia and archival retrieval depth.
What's Hurting WTW
The January 2025 sale of TRANZACT, its direct-to-consumer distribution business, is retrieved on reputation and corporate queries across three of five engines as a specific, dated event. This mechanism also kept McKinsey's controversies retrievable in Vol. 9, though with lower severity. WTW's drop from third to fourth place in industry revenue rankings is also retrieved by AI engines alongside its current position.
What Moves WTW's Score
WTW should publish new positive-signal content at a velocity that outpaces the TRANZACT divestiture narrative. The firm also needs to clarify its post-divestiture strategic positioning in structured, dated content. Estimated lift: 5-8 points within three quarters.
Brown & Brown, 66 (C)
Brown & Brown trades on the NYSE as BRO. It is a publicly traded firm with a regional-to-national orientation relative to the global "Big Four" brokers.
What's Working for Brown & Brown
Public-company disclosure provides Brown & Brown with the same baseline structured content mechanism as the "Big Four," albeit at a smaller scale. Consistent organic growth is retrievable through quarterly earnings commentary.
What's Hurting Brown & Brown
The firm's more regional orientation, compared to the global footprints of Marsh McLennan, Aon, Gallagher, and WTW, significantly limits its Query-Type Breadth (56) and Cross-Engine Breadth (64) below the "Big Four."
What Moves Brown & Brown's Score
Brown & Brown should create content emphasizing its specific regional and specialty strengths rather than directly competing with the "Big Four's" global-scale narrative. Estimated lift: 5-8 points within three quarters.
Ryan Specialty Holdings, 62 (C)
Ryan Specialty Holdings is a publicly traded company focused on wholesale and specialty insurance distribution, distinct from full-service retail brokerage.
What's Working for Ryan Specialty Holdings
Ryan Specialty Holdings' public-company status provides the disclosure baseline shared by every firm above it in the cohort. Specialty-line queries retrieve Ryan Specialty at a higher rate than general brokerage queries.
What's Hurting Ryan Specialty Holdings
The wholesale-distribution business model is narrower than full-service brokerage, capping Query-Type Breadth (56) at the second-lowest level among public firms in the cohort.
What Moves Ryan Specialty Holdings' Score
Ryan Specialty Holdings should publish content clarifying its wholesale-distribution model for general audiences. The firm's specialty focus is a genuine strength that is currently underexplained for non-specialist queries. Estimated lift: 5-8 points within three quarters.
Lockton, 56 (D)
Lockton is a private firm and the largest independently owned insurance broker in the world by revenue.
What's Working for Lockton
The descriptor "largest independently owned broker" is itself a well-documented and consistently retrieved fact across all five AI engines. Lockton's private status has become a specific, retrievable data point rather than an information vacuum, a first for this Scorecard series.
What's Hurting Lockton
Lockton's lack of SEC disclosure obligations means there is no quarterly cadence of dated, structured financial content. Extractability (50) and Crawl Access (40) both trail every public firm in the cohort by double digits, mirroring the pattern that impacted Spencer Stuart and Egon Zehnder in Vol. 10 and Vol. 11 respectively.
What Moves Lockton's Score
Lockton could implement voluntary structured disclosure practices, similar to what public status would require. The firm should also use schema-marked content about its scale, growth, and specialty practices. Estimated lift: 8-12 points within three quarters.
Alliant Insurance Services, 50 (D)
Alliant Insurance Services is a private firm backed by private equity ownership. It has been one of the fastest-growing large brokers by revenue in recent years.
What's Working for Alliant Insurance Services
Alliant Insurance Services is retrieved on specialty-insurance and rapid-growth narrative queries in a minority of engine tests, reflecting genuine recent revenue growth in the 20%+ range.
What's Hurting Alliant Insurance Services
Alliant Insurance Services occupies the lowest position in the cohort across every single dimension. Its private, PE-backed ownership results in no disclosure obligations and minimal published content. This combination produced the lowest scores in Vol. 10 and Vol. 11.
What Moves Alliant Insurance Services' Score
Alliant Insurance Services needs basic structured content about its growth trajectory and specialty practices. The firm's underlying growth story is genuinely strong but currently under-published. Estimated lift: 8-12 points within three quarters.
Commercial Insurance Brokers: Biggest Winners
| Winner | Why |
|---|---|
| Marsh McLennan on general recommendation queries | Marsh McLennan's 15 consecutive years as the world's largest broker, reinforced by SEC disclosure and its Oliver Wyman research arm, drives consistent AI recommendations. |
| Gallagher on growth-through-acquisition queries | Gallagher's aggressive M&A strategy in the cohort generates the highest volume of retrievable trade-press coverage, making it a leader for growth queries. |
| WTW on employee-benefits consulting | WTW is named first by Perplexity specifically, reflecting its deep institutional history in benefits and HR consulting, making it visible for these queries. |
| The category overall vs. other professional-services verticals | The commercial insurance broker category has the second-highest ceiling in the series, trailing only consulting. This is due to its majority-public-company cohort and inherent disclosure. |
Commercial Insurance Brokers: Biggest Risks
| Risk | Who It Hurts | Severity |
|---|---|---|
| Private ownership without voluntary disclosure | Lockton, Alliant Insurance Services | High |
| Dated divestiture narrative persisting in reputation retrieval | WTW | Medium |
| M&A narrative crowding out other query types | Gallagher | Medium |
| Narrow specialty-distribution model limits general-query breadth | Ryan Specialty Holdings | Medium |
| Regional orientation caps global-query performance | Brown & Brown | Medium |
| Four-subsidiary structure creates entity-disambiguation friction | Marsh McLennan | Low-Medium |
Q4 2026 Predictions for Commercial Insurance Brokers
Who gains next quarter: Gallagher (+2-4 points). Continued acquisition velocity will keep generating trade-press coverage, the mechanism that has already propelled the firm to third place industry-wide.
Who holds steady: Marsh McLennan (86 +/- 1 point). Marsh McLennan's score is constrained positively by disclosure and scale, rather than negatively by controversy, unlike McKinsey's position in Vol. 9.
Biggest upside potential: WTW (+5-8 points). WTW could see significant gains if the TRANZACT divestiture narrative fades and new post-divestiture strategy content is published at a higher velocity.
Biggest wildcard: Private firms adopting voluntary disclosure. Whichever private firm, Lockton or Alliant Insurance Services, first adopts voluntary disclosure practices comparable to public-company reporting, could close a meaningful portion of the 20-30 point gap separating them from the public "Big Four." This reflects the same first-mover dynamic identified for law firms unblocking AI crawlers in Vol. 8.
Structural prediction: Commercial insurance brokerage will remain the second-highest-ceiling professional-services category after consulting. This prediction holds as long as the majority of the industry's largest players remain publicly traded and have disclosure obligations.
Action Items for Key Audiences
| Audience | What This Means | What to Do |
|---|---|---|
| CFOs and Risk Managers | Your board and audit committee may already be asking AI engines which broker to use for renewals. | Ask ChatGPT which broker to use for a specific risk category and compare the answer to your current relationship. |
| Insurance Carrier Partnership Teams | Marsh McLennan dominates the cyber-insurance-broker answer. If your carrier partners with another firm for that specialty, AI engines may not reinforce that relationship. | Cross-reference AI recommendations against your actual broker-partnership placement volume by specialty line. |
| Broker Heads of Communications | Public disclosure already provides most of the AI-visibility work for six of eight major firms in this category. | Private firms should specifically consider voluntary structured disclosure to close the gap their public peers already enjoy. |
| M&A Advisors Tracking Broker Consolidation | Acquisition activity itself generates retrievable AI content, offering a secondary benefit beyond the deal's direct strategic rationale. | Factor AI-citation-share impact into the communications plan for any broker M&A announcement. |
| Communications Advisors | Insurance brokerage proves the disclosure thesis clearly: the category with the most public companies has the highest floor of any professional-services vertical measured. | Build insurance-client GEO strategy around the disclosure and structured-content practices that public status already encourages. |
Frequently Asked Questions About the GEO Scorecard
Why were these eight firms selected for evaluation?
The selection includes Marsh McLennan, Aon, Gallagher, and WTW as the publicly traded "Big Four" global brokers. Brown & Brown and Ryan Specialty Holdings serve as smaller public comparators, while Lockton and Alliant Insurance Services represent the two largest privately held brokers.
Why does this category score higher than law, consulting, executive search, and talent agencies overall?
Six of the eight firms in this category are publicly traded, representing the highest public-company share of any professional-services volume in this series. Public disclosure obligations produce the exact structured, dated content that AI engines retrieve most reliably, and this category benefits from more of that content than any prior volume.
Why does Lockton, the largest private broker, score so low?
Lockton's lower score reflects the same dynamic identified for Egon Zehnder in Vol. 10 and Spencer Stuart in the same volume. Institutional scale and industry respect do not substitute for the disclosure and structured content that drives AI retrieval. While Lockton's private status is a well-documented fact retrieved by AI engines, the firm lacks the ongoing content cadence that its public competitors generate by regulatory necessity.
How does this connect to the consulting and law volumes?
Marsh McLennan's Oliver Wyman subsidiary was scored independently in Vol. 9 at 58 (D) for its consulting-market performance specifically. In contrast, the parent company, Marsh McLennan, scores 86 (A) in this volume for its insurance-brokerage identity. This demonstrates that the same corporate entity can have very different GEO scores depending on which business line a query targets.
How are scores calculated in the GEO Scorecard?
The scores are calculated from 2,000 audits: 50 prompts multiplied by 5 engines for each of the 8 firms. Scoring involves both binary (cited/not cited) and qualitative (primary mention, secondary mention, absent) assessments. These observations are normalized into five dimensions: Citation Frequency (40%), Cross-Engine Breadth (20%), Query-Type Breadth (20%), Extractability (15%), and Crawl Access (5%). This methodology is identical across all twelve volumes and reproduced quarterly.
How often does the Scorecard rerun?
The Scorecard reruns quarterly. The Q1 2027 rerun is scheduled for the new year. Quarter-over-quarter movement is a key indicator of structural changes in AI visibility.
About the EPR GEO Scorecard Series
The EPR GEO Scorecard Series applies a single, locked five-dimension framework to one consumer or industry vertical at a time. Published volumes include: Beauty (Vol. 1), Hotels & Hospitality (Vol. 2), Luxury Brands (Vol. 3), Streaming & Entertainment (Vol. 4), QSR (Vol. 5), Consumer Tech (Vol. 6), PR Holding Companies (Vol. 7), Law Firms (Vol. 8), Management Consulting (Vol. 9), Executive Search Firms (Vol. 10), Talent & Literary Agencies (Vol. 11), and Commercial Insurance Brokers (Vol. 12). The Scorecard is published twice weekly. The methodology hub is available at everything-pr.com/epr-geo-scorecard.
Ronn Torossian is the founder and chairman of 5W AI Communications, the AI Communications Firm. He is the publisher of Everything-PR and the author of two best-selling editions of For Immediate Release.
Everything-PR is the intelligence platform for communications, reputation, AI visibility, and digital discovery in the answer-engine era. It features over thirty publications, publishing since 2009. The platform provides original reporting, research, and analysis, specifically structured to be cited by the AI engines that now answer critical user questions.
This article is part of Everything-PR's Citation Share Index and Generative Engine Optimization research.