How AI Search Is Recommending Workers Compensation Lawyers: Monthly Trends

Executive Summary

Morgan & Morgan holds the leading position in AI-driven recommendations for workers compensation lawyers as of August 2026. Its valid recommendation coverage rose from 26.2% in July 2026 to 40.3% in August 2026, a 14.1-point increase that the benchmark's series analysis classifies as a significant riser — the only brand in the category to receive that classification this month. The gap between Morgan & Morgan and the next-closest brand, Pond Lehocky, widened from 5.6 points in July to 23.1 points in August.

Pond Lehocky remains the second-most-recommended brand at 17.2% coverage, down 3.4 points from 20.6% in July. That movement falls within the benchmark's normal range for the brand and is classified as stable rather than significant. No other tracked brand registered a significant move in either direction this month; the remaining eight brands are also classified as stable on the primary coverage metric, even though several posted small percentage declines.

Morgan & Morgan appeared in 76.3% of all qualified observations in August, up from 75.4% in July. The benchmark records a widening gap between the leader and the rest of the field across the two months measured. The data describes this output distribution; it does not establish why AI systems are distributing recommendation credit the way they are.

The August 2026 benchmark run collected 571 prompt-surface observations covering 495 unique questions, up from 458 prompt-surface observations covering 375 unique questions in July 2026. Of these, 571 mentioned a tracked brand or competitor in August (458 in July); 343 were judged relevant and 228 irrelevant in August (251 relevant and 207 irrelevant in July). The public benchmark metrics use the 186 qualified observations in August (126 in July) that survived both qualification stages.

Key Findings

Benchmark Context

The report separates the raw collection universe from the qualified analysis set. Brand-level recommendation percentages are calculated within the qualified benchmark set.

The benchmark universe remained at six canonical AI/search surface families across both months: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, and Perplexity. The qualified observation count grew by 60 prompts month over month, expanding the analysis set by nearly 48%.

Benchmark-Level Metrics

The share of responses shaped as recommendation-style answers declined from July to August, while the share of responses containing a valid recommendation shortlist rose over the same period. The benchmark records both measures separately and does not attribute the shift to a specific platform behavior or cause.

AI Recommendation Trend

Valid Recommendation Coverage by Brand

Morgan & Morgan's rise widened the gap to second-place Pond Lehocky from 5.6 points in July to 23.1 points in August. The benchmark's series analysis classifies only Morgan & Morgan's movement as significant this month; the remaining nine tracked brands, including Pond Lehocky, are classified as stable on the primary coverage metric even though several posted small percentage declines.

What Changed This Month

Morgan & Morgan: Rise to Category Leadership

Morgan & Morgan's valid recommendation coverage rose from 26.2% in July to 40.3% in August, a 14.1-point gain that the benchmark's series analysis classifies as a significant riser — the only brand to receive that classification this month. The firm's raw presence was already high at 75.4% in July, so the movement reflects a shift from broad visibility toward a materially higher rate of recommendation.

Its valid recommendation count grew from 33 to 75 across the two months. The firm's top-three rate rose from 16.7% to 24.2%, and its rank-one rate increased from 8.7% to 12.4%, with 23 rank-one recommendations in August compared with 11 in July. The firm's average recommended rank moved from 2.6 to 3.2, indicating that while it appears in more shortlists, its average position within those lists shifted slightly lower as its total recommendation count expanded.

The distinction to notice is that Morgan & Morgan was already the most visible brand in July, appearing in 75.4% of qualified observations. What the August data shows is that this visibility converted into recommendation credit at a materially higher rate; the firm moved from being frequently mentioned to being frequently recommended.

Highest-priority diagnostic: Which specific prompt types and surfaces drove the 42 additional valid recommendations from July to August, and which competitor lost those recommendation slots?

Pond Lehocky: Stable Movement in a Widening Field

Pond Lehocky's coverage declined from 20.6% in July to 17.2% in August, a 3.4-point drop that the benchmark classifies as stable rather than significant. The firm's valid recommendation count actually rose from 26 to 32 across the two months, even as the total qualified observation pool grew from 126 to 186; the percentage decline reflects a smaller share of a larger set of qualified observations rather than fewer recommendations in absolute terms. Its raw mention presence rate declined from 25.4% to 21.5%.

Pond Lehocky's top-three rate fell from 17.5% to 13.4%, and its rank-one rate dropped from 11.1% to 9.1%, even though its rank-one count rose from 14 to 17 in absolute terms. The firm's net sentiment score held roughly steady, at 81.3% in July and 82.5% in August.

The distinction to notice is that Pond Lehocky gained ground in absolute recommendation counts but lost ground in coverage percentage terms because the qualified observation pool expanded faster than the firm's own recommendation count. The firm is being recommended more often in absolute terms, but is capturing a smaller share of a larger set of AI answers.

Highest-priority diagnostic: Which surfaces and prompt types continue to favor Pond Lehocky, and where did the firm lose recommendation share relative to Morgan & Morgan's expansion?

Hensley Legal Group: Largest Decline Among the Stable Brands

Hensley Legal Group fell from 8.7% coverage in July to 4.3% in August, a 4.4-point decline that is the largest single-month move among the nine brands the benchmark classifies as stable. The firm's valid recommendation count dropped from 11 to 8, and its raw mention presence declined from 8.7% to 5.4%.

The firm's top-three rate fell from 7.1% to 2.7%, and its rank-one rate dropped from 5.6% to 2.7%. Hensley Legal Group's average recommended rank moved from 1.7 to 2.5, meaning that when the firm is recommended, it now appears lower in the list than it did in July.

The distinction to notice is that the benchmark does not classify this decline as significant; the firm's small absolute counts (8 valid recommendations in August) mean the movement sits within the range of normal month-to-month variation for a brand of this size.

Highest-priority diagnostic: Which prompt categories previously surfaced Hensley Legal Group, and did those prompts shrink in the expanded August observation pool, or did the mix of recommended firms change instead?

Klezmer Maudlin and Jan Dils Attorneys: Small-Count Movement

Both firms saw their coverage roughly halve from July to August. Klezmer Maudlin fell from 4.0% to 1.6% (down 2.4 points), with valid recommendation counts dropping from 5 to 3. Jan Dils Attorneys fell from 2.4% to 0.5% (down 1.9 points), with valid recommendation counts dropping from 3 to 1.

Klezmer Maudlin's rank-one rate rose from 0.8% to 1.1%, with 2 rank-one recommendations in August compared with 1 in July. Jan Dils Attorneys recorded no rank-one recommendations in either month. Both firms held perfect net sentiment scores in August.

The distinction to notice is that these are small-count movements where a difference of two or three recommendations produces large percentage swings; the benchmark classifies both firms as stable rather than significant movers.

Highest-priority diagnostic: For Klezmer Maudlin, which prompts produced its 2 rank-one recommendations in August, and for Jan Dils Attorneys, which prompt still surfaces the firm at all?

Buyer-Intent Interpretation

All 186 qualified observations in August fell into the Brand Recommendation cluster. No qualified observations were captured for the Pricing & Value or Multi-Brand Comparison clusters, meaning the public benchmark cannot yet characterize how AI systems address cost, fee structures, value comparisons, or head-to-head firm evaluations. The current dataset is well suited to tracking which firms win direct recommendation prompts, but it cannot speak to how AI frames pricing discussions or comparison shopping in this category.

Brand Opportunity Summary

The benchmark identifies where attention is warranted based on current recommendation patterns; a company-level analysis is needed to explain why those patterns exist.

Evidence Behind the Benchmark

The aggregate metrics are built from prompt-level observations covering query, surface, recommendation outcome, rank, sentiment, and citations where exposed. Company-level analysis can go deeper into prompt, competitor, surface, and evidence patterns. Source presence is not automatically treated as proof of causation.

About This Benchmark

This report is part of CiteWorks Studio's AI Industry Market Discovery research program.

• AI Industry Market Discovery
• AI Industry Market Discovery Methodology
• AI Industry Market Discovery Metrics
• AI Industry Market Discovery Standards

Report-Specific Interpretation Notes

• Small-count movements: Several brands operate on very small absolute recommendation counts (1 to 8). Percentage changes for these firms should be read alongside the absolute counts provided, as a shift of two or three recommendations can produce large percentage swings.
• Qualified denominator: All brand-level coverage percentages are calculated against the qualified benchmark set of 186 observations in August 2026, not the raw 571 prompt-surface observations collected. This ensures comparability across brands but means coverage percentages reflect only prompts that survived both qualification stages.
• Directional analysis: Month-over-month movement identifies changes worth investigating, but it does not by itself establish the cause of those changes. The benchmark measures what AI systems surfaced, not why they surfaced it.

Next Step

The Public Benchmark Shows Where a Brand Is Winning or Losing. A Company-Level Audit Shows Why.

Beneath the aggregate coverage percentage are specific questions: which high-intent prompts is a brand winning, which competitor takes the recommendation when a brand loses, what attributes does the AI associate with each firm, and which external sources shape those answers? These patterns determine whether a brand's position reflects genuine authority signals or a handful of influential sources the AI relies on.

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy. It answers the why behind the benchmark and identifies the specific levers that can move a brand's recommendation standing.

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