Jan Dils Attorneys AI Market Strategy Report - Workers Compensation Lawyers

Answer Capsule

Jan Dils Attorneys holds a narrow but clean recommendation pocket in the September 2026 Workers Compensation Lawyers benchmark, with 0.81% valid recommendation coverage and a perfect 1.00 net sentiment score. The firm is visible but under-recommended: it appears in just 0.81% of qualified observations and converts every one of those appearances into a valid recommendation, but the absolute base is only two recommendations. The clearest win is rank quality, since both recommendations landed at an average position of 1.5 and one reached first position. The clearest gap is platform concentration, because all recommendation credit came from Google AI Mode and Google AI Overviews while ChatGPT, Copilot, Gemini, and Perplexity produced no qualified recommendation credit at all.

Who This Report Is For

This report is for Jan Dils Attorneys leadership, marketing, and business development teams evaluating how the firm is surfaced and recommended across AI search and answer platforms in the workers compensation legal category.

Report Card

Executive Summary

Jan Dils Attorneys is visible but under-recommended in the September 2026 Workers Compensation Lawyers benchmark. The firm recorded 2 raw mentions across 247 qualified observations, a 0.81% raw mention presence rate, and converted both mentions into valid recommendations, producing 0.81% valid recommendation coverage. That conversion rate is efficient on its own terms, but the absolute base is only two recommendations, which places the firm seventh by top-three rate in the category, alongside Gerber & Holder.

The framing quality is the strongest signal in the packet. Jan Dils Attorneys recorded 2 positive mentions, 0 neutral mentions, and 0 negative mentions, producing a net sentiment score of 1.00. No competitor in the tracked set matched that combination of zero negative framing and full positive classification at this sample size. The benchmark classifies the firm as stable rather than a significant riser or decliner, and the movement from August to September falls within normal variation.

The strongest cluster is C01, the Brand Recommendation cluster covering direct recommendation prompts such as workers compensation attorney, workers comp lawyer, and accident lawyers. All 247 qualified observations in September fell into this cluster, and all of the firm's recommendation credit came from it. The C02 comparison cluster and the C03 pricing and cost cluster produced zero qualified observations in the benchmark, so the firm cannot yet be evaluated on head-to-head comparison prompts or fee-related prompts.

The strongest platform signal is Google AI Overviews, where Jan Dils Attorneys recorded 1 valid recommendation at a 1.67% coverage rate and a rank-one placement, the firm's only first-position recommendation in the packet. Google AI Mode produced the second recommendation at a 1.37% coverage rate with an average recommended rank of 2.0. Together these two platforms account for all of the firm's recommendation credit.

The clearest platform gap is the absence of qualified recommendation credit on ChatGPT, Copilot, Gemini, and Perplexity. ChatGPT alone carried 36 qualified observations in the packet and produced zero mentions and zero recommendations for the firm, while Copilot carried 32 and Perplexity carried 21 with the same result. Gemini carried 25 qualified observations and produced zero mentions for Jan Dils Attorneys. These are not small samples, and the pattern suggests the firm's public evidence layer is not reaching the retrieval and synthesis paths those platforms rely on.

The clearest cluster gap is the complete absence of qualified observations in the Pricing & Value and Multi-Brand Comparison classes. The benchmark cannot yet characterize how AI systems address cost, fee structures, or head-to-head firm evaluations in this category, which means the firm has no measured position on the prompts that typically sit closest to a hiring decision. Morgan & Morgan, by contrast, holds 33.60% valid recommendation coverage and 9.31% rank-one rate across the same qualified set, and the gap between the category leader and Jan Dils Attorneys is 32.79 percentage points on the primary coverage metric.

What Jan Dils Attorneys Is Winning

Questions This Section Answers

• Where does Jan Dils Attorneys actually outperform larger competitors in the September benchmark?
• What does the firm's 100% mention-to-recommendation conversion say about how AI systems frame it when it does appear?
• Why should the firm's perfect net sentiment score be read as a clean pocket rather than a broad position?

The firm's clearest win is framing quality. Jan Dils Attorneys recorded a net sentiment score of 1.00 on 2 mentions, with 2 positive and 0 neutral or negative classifications. That is the cleanest possible framing outcome at this sample size, and it matches the top of the category on this metric alongside Gerber & Holder and Klezmer Maudlin.

The second win is rank quality when the firm does appear. Both valid recommendations landed at an average recommended rank of 1.5, which is better than Morgan & Morgan's 2.94, Krasno Krasno & Onwudinjo's 2.86, and Pond Lehocky's 1.76. The firm's single rank-one recommendation on Google AI Overviews represents a 0.40% rank-one rate, which is a small but real first-position capture.

The third win is conversion efficiency. Every raw mention Jan Dils Attorneys received in September converted into a valid recommendation. Bross & Frankel, by contrast, recorded 1 raw mention and 0 valid recommendations, and Calhoun Meredith recorded no presence at all. The firm's 100% mention-to-recommendation conversion is a meaningful signal about how AI systems frame the firm when they do surface it.

These wins are real but narrow. The firm has very few wins in absolute terms, and the entire recommendation base rests on two observations. The evidence supports a clean pocket, not a broad position.

Where Jan Dils Attorneys Has the Clearest AI Visibility Gaps

Questions This Section Answers

• Which platforms account for the firm's zero qualified mentions, and what does Morgan & Morgan's presence on those platforms show by contrast?
• How wide is the recommendation coverage gap between Jan Dils Attorneys and the category leader?
• What does the absence of Pricing & Value and Multi-Brand Comparison observations mean for the firm's measured position on hiring-adjacent prompts?

The largest gap is platform coverage. Jan Dils Attorneys received zero qualified mentions and zero recommendations on ChatGPT, Copilot, Gemini, and Perplexity. Those four platforms carried 114 of the 247 qualified observations in the September benchmark, and the firm was absent from all of them. Morgan & Morgan, by contrast, recorded a 97.22% raw mention presence rate on ChatGPT, a 96.88% rate on Copilot, a 64.00% rate on Gemini, and a 100.00% rate on Perplexity. The gap is not a matter of degree; it is a matter of presence versus absence.

The second gap is scale against the category leader. Morgan & Morgan holds 33.60% valid recommendation coverage, 16.60% top-three rate, and 9.31% rank-one rate. Jan Dils Attorneys holds 0.81%, 0.81%, and 0.40% on the same measures. The benchmark records the Morgan & Morgan lead over the next brand widening to 23.90 percentage points in September, and the gap to Jan Dils Attorneys is wider still at 32.79 points. The category is splitting into a dominant leader and a compressed middle, and the firm currently sits at the bottom of that middle.

The third gap is cluster coverage. All 247 qualified observations in September fell into the Brand Recommendation cluster. The Pricing & Value and Multi-Brand Comparison clusters produced zero qualified observations across July, August, and September. That means the firm has no measured position on fee-related prompts or firm-versus-firm comparison prompts, which are the prompt types that typically sit closest to a hiring decision. The benchmark cannot yet say whether Jan Dils Attorneys is being recommended, displaced, or ignored on those prompts.

The fourth gap is displacement risk. The firm is tied at 0.81% coverage with Gerber & Holder, and both sit below Hensley Legal Group at 3.64%, Klezmer Maudlin at 1.62%, and Berger and Green at 1.62%. The middle of the category is compressed, and small movements in either direction can shift relative position quickly. The benchmark classifies the firm as stable this month, but the base is thin enough that a single lost recommendation would move the coverage rate materially.

Biggest Opportunity

Questions This Section Answers

• Why is extending the firm's recommendation pocket to ChatGPT, Copilot, Gemini, and Perplexity a citation and source-footprint problem rather than a messaging problem?
• What is the opportunity in building owned answer content for the unmeasured fee and comparison prompt types?

The clearest opportunity is to extend the firm's existing recommendation pocket from Google AI Mode and Google AI Overviews into the platforms where it currently has no presence. The firm already converts mentions into recommendations at a 100% rate and already earns strong rank placement when it appears. The constraint is not framing quality or rank quality; it is that the firm is not being retrieved or synthesized into answers on ChatGPT, Copilot, Gemini, and Perplexity at all.

That makes the highest-value work a citation and source-footprint problem rather than a messaging problem. The firm needs its public evidence layer, including practice pages, attorney profiles, case results, jurisdictional content, and third-party references, to be retrievable and attributable in the source patterns those platforms draw from. The benchmark's evidence layer retains citations and attributable sources where exposed, and the firm's current pattern suggests those sources are not surfacing on the four platforms where it is absent.

The secondary opportunity is the unmeasured clusters. Because Pricing & Value and Multi-Brand Comparison produced zero qualified observations, the firm has no baseline on fee-related or comparison prompts. Building owned answer content for those prompt types now would position the firm ahead of a measurement gap that the benchmark itself has flagged as unresolved.

Competitive Landscape

Questions This Section Answers

• Where does Jan Dils Attorneys rank by top-three rate against the other nine tracked firms?
• How does the firm's average recommended rank of 1.50 compare to the category leader, and why should that be read alongside its two-recommendation base?

Morgan & Morgan holds dominant recommendation power in the Workers Compensation Lawyers category, and the rest of the tracked set is compressed well below it. Jan Dils Attorneys sits in the lower portion of that compressed middle, tied with Gerber & Holder on valid recommendation coverage but ahead of Bross & Frankel and Calhoun Meredith, both of which recorded zero valid recommendations in September.

Average recommended rank covers rank-eligible recommendations only.

Jan Dils Attorneys ranks seventh by top-three rate and sits in a cluster of brands operating on very small absolute counts. The firm's rank quality, at an average recommended rank of 1.50, is better than the category leader's, but the base is two recommendations, so the position should be read alongside the absolute counts rather than as a stable ranking advantage.

Prompt Evidence

Google AI Overviews / Brand Recommendation Prompt: "workers compensation attorney" Result: Jan Dils Attorneys appeared as a valid recommendation at first position, contributing the firm's only rank-one placement in the September benchmark.

Google AI Mode / Brand Recommendation Prompt: "workers comp lawyer" Result: Jan Dils Attorneys appeared as a valid recommendation at an average recommended rank of 2.0, contributing the firm's second and final recommendation credit for the month.

ChatGPT / Brand Recommendation Prompt: "workers compensation attorney" Result: Jan Dils Attorneys did not appear in any qualified ChatGPT observation, despite the platform carrying 36 qualified observations in the packet.

Perplexity / Brand Recommendation Prompt: "workers compensation lawyer" Result: Jan Dils Attorneys recorded zero mentions across 21 qualified Perplexity observations, while Morgan & Morgan recorded a 100.00% raw mention presence rate on the same platform.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map every prompt, surface, competitor, and citation pattern behind the firm's two September recommendations and identify which prompts on ChatGPT, Copilot, Gemini, and Perplexity should be producing recommendations but are not.

Phase 2: Recommendation Readiness Plan Prioritize the platform and prompt combinations with the largest gap between the firm's current presence and the category leader's, starting with the four platforms where the firm recorded zero qualified mentions.

Phase 3: Owned Answer Layer Buildout Build practice, attorney, jurisdiction, and case-result content structured to answer the specific recommendation prompts the benchmark tracks, including the fee and comparison prompt types the benchmark has flagged as unmeasured.

Phase 4: Citation and Authority Layer Development Strengthen the public evidence layer the firm depends on, including third-party references, directory presence, and source pages that AI systems can retrieve and attribute when forming recommendations.

Phase 5: Monthly AI Visibility and Recommendation Tracking Track coverage, top-three rate, rank-one rate, and sentiment month over month against the same qualified denominator so movement can be read against the category rather than in isolation.

Why This Matters

AI presence alone is not enough. Jan Dils Attorneys already converts every mention into a recommendation and already earns strong rank placement when it appears, but it appears in fewer than 1% of qualified observations and is entirely absent from four of the six tracked platforms. The constraint is not how the firm is framed; it is whether the firm is being retrieved and synthesized into answers at all.

The next move is targeted correction of the prompt, page, and citation layers that determine whether the firm surfaces on ChatGPT, Copilot, Gemini, and Perplexity, and whether it holds its position on Google AI Mode and Google AI Overviews as the qualified observation pool grows. The benchmark shows where the firm stands. The work ahead is closing the platform gap before the category's compressed middle separates further.

Core Metrics

Sentiment Score

Questions This Section Answers

• How is the sentiment score calculated for Jan Dils Attorneys in September 2026, and what does its 1.00 result reflect?
• Why does counting all mentions as wins produce a misleading AI visibility number?

Sentiment Score = (positive mentions × 1 + neutral mentions × 0 + negative mentions × -1) / total mentions

For Jan Dils Attorneys in September 2026, that is (2 × 1 + 0 × 0 + 0 × -1) / 2, which equals 1.00.

This matters because unclassified mention counts are misleading. A raw mention total tells you a brand appeared, but it does not tell you whether the AI system recommended the firm, referenced it as context, framed it cautiously, or named it only to explain why a competitor was the better choice. Those are not equal outcomes, and counting them all as wins produces a visibility number that does not reflect buyer behavior.

Share of voice is a diagnostic metric, not a business KPI. It tells you how much of the conversation a brand occupies, not whether that occupation is helping a buyer choose the firm. A positive recommendation, a neutral reference, a cautionary mention, and a competitor-displaced mention all count the same in a raw mention total, and they should not.

Counting all mentions as wins is bad measurement. Classified sentiment is required before interpreting AI visibility, because the difference between being recommended and being mentioned is the difference between being on the buyer shortlist and being background noise. Jan Dils Attorneys currently sits at the favorable end of that distinction, with every mention classified positive and every mention converted into a valid recommendation, but the base is two observations and should be read as a clean pocket rather than a broad position.

Sentiment by Platform

Methodology

1. This report is a benchmark-based analysis of AI recommendation behavior in the Workers Compensation Lawyers category for September 2026. It is not a client result and does not describe work performed by CiteWorks Studio on behalf of Jan Dils Attorneys.
2. The reporting window is September 2026, with July 2026 as the baseline month and August 2026 as the intermediate month in the public series.
3. Six AI and search surface families were tracked: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, and Perplexity. All six registered qualified observations in September 2026.
4. The September 2026 benchmark run collected 631 prompt-surface observations covering 475 unique questions. Of these, 631 mentioned a tracked brand or competitor, 449 were judged relevant, 182 were judged irrelevant, and 247 qualified observations formed the public denominator.
5. The competitor universe contained 10 tracked brands: Morgan & Morgan, Berger and Green, Bross & Frankel, Calhoun Meredith, Gerber & Holder, Hensley Legal Group, Jan Dils Attorneys, Klezmer Maudlin, Krasno Krasno & Onwudinjo, and Pond Lehocky.
6. Three public high-intent clusters were defined: C01 Brand Recommendation, C02 Multi-Brand Comparison, and C03 Pricing & Value. All 247 qualified September observations fell into C01. C02 and C03 produced zero qualified observations across July, August, and September.
7. Stage 0 extraction retained the query, AI or search surface, answer, brand outcome, recommendation placement, sentiment, and, where exposed, citations or attributable evidence sources for each observation.
8. A mention is counted when a tracked brand appears in an AI response within a qualified observation, regardless of recommendation status.
9. A valid recommendation is counted when a tracked brand is recommended with a valid, attributable recommendation in a qualified observation. Negative, neutral, cautionary, comparison-anchor, and listed-only mentions are not counted as valid recommendations unless the dataset explicitly marks them as such.
10. Brand-level percentages use the 247 qualified observations as the public denominator, not the raw 631 prompt-surface observations collected. The qualified pool roughly doubled from July to September, which mechanically spreads any fixed recommendation count across a larger denominator.
11. Several brands in the tracked set operate on very small absolute recommendation counts. Jan Dils Attorneys recorded 2 valid recommendations, and percentage changes for the firm should be read alongside the absolute counts, since a shift of one recommendation produces a large percentage swing.
12. The benchmark measures what AI systems surfaced, not why they surfaced it. Month-over-month movement identifies changes worth investigating but does not by itself establish causation. Source presence is evidence about the information environment and is not automatically proof that a source caused a recommendation.

See Where AI Is Recommending Your Brand

The public benchmark shows where Jan Dils Attorneys stands in the Workers Compensation Lawyers category. A company-level AI visibility audit shows which prompts, competitors, and sources are driving that position, and which platform gaps are holding the firm back.