AI Visibility Benchmarks by Industry: What 8 NYC Markets Show in 2026

The benchmark most dashboards omit is the market itself

An AI visibility score becomes meaningful only after you know the shape of the category. Ten percent share can mean leadership in a fragmented market, mediocrity in a concentrated one, or a fragile result produced by a single assistant. A generic benchmark erases all three distinctions.

To make the comparison concrete, Viclaro reconciled one complete active-prompt run for each of eight New York City categories: divorce, personal injury, immigration, plaintiff-side employment, estate planning, cosmetic dentistry, IVF, and cosmetic surgery. The current-state rebuild contains 13,368 ranking-eligible firm citations across 3,172 ranked firm records.

These are category snapshots, not estimates of market share or consumer demand. They measure recommendation share inside the sampled answers. The value is comparative: they show why a target must be calibrated against the field a firm is actually trying to enter.

Top-five concentration varies by more than 30 points

Plaintiff-side employment is the most concentrated category at 57.1%. Immigration follows at 47.5%, divorce at 44.6%, IVF at 37.7%, estate planning at 37.1%, cosmetic surgery at 29.7%, cosmetic dentistry at 25.3%, and personal injury at 23.6%.

Put differently, the top-five share in plaintiff employment is about 2.4 times the personal-injury share. The expanded extraction revision widened several fields substantially, which is why extraction version belongs beside every benchmark.

This is why the advice in our AI share-of-voice guide avoids a universal pass mark. The same five-percent share can be a foothold in IVF and a far less distinctive result in cosmetic surgery.

Concentration and consensus are different variables

Market concentration tells you how much recommendation volume the leaders hold. Model coverage tells you how broadly those leaders are recognized. The two do not move in lockstep.

Among each category’s top ten, nine IVF firms were named by all four assistants. Personal injury had eight, cosmetic surgery seven, estate planning six, divorce four, immigration four, plaintiff employment four, and cosmetic dentistry two.

Our model-level analysis found that none of the eight categories had the same leader across all four assistants. Reading rank without model coverage hides that difference; our guide to AI search rankings shows how to keep them separate.

The eight-market benchmark

Plaintiff-side employment: 792 citations across 186 firms; top five 57.1%; four top-ten firms have four-model coverage.

Immigration: 731 citations across 202 firms; top five 47.5%; four top-ten firms have four-model coverage.

Divorce: 912 citations across 191 firms; top five 44.6%; four top-ten firms have four-model coverage.

IVF: 5,302 citations across 677 firms; top five 37.7%; nine top-ten firms have four-model coverage.

Estate planning: 1,225 citations across 584 firms; top five 37.1%; six top-ten firms have four-model coverage.

Cosmetic surgery: 2,003 citations across 516 firms; top five 29.7%; seven top-ten firms have four-model coverage.

Cosmetic dentistry: 833 citations across 412 firms; top five 25.3%; two top-ten firms have four-model coverage.

Personal injury: 1,570 citations across 404 firms; top five 23.6%; eight top-ten firms have four-model coverage.

What a realistic AI visibility target looks like

Start with three comparisons, not one score. First, compare your share with the fifth-place and tenth-place firms; that shows the distance to a commercially visible tier. Second, compare model coverage; a lower aggregate share spread across four assistants may be more resilient than a higher share driven by one. Third, compare prompt-bucket coverage; a firm can own crisis prompts while disappearing from validation and decision prompts.

Then set the objective in observable terms. “Move from two-model to three-model coverage while reaching the category’s top-ten share threshold” is testable. “Improve GEO authority” is not. In a low-consensus category, gaining a new model may matter more than adding a percentage point on the assistant that already knows you. In a high-consensus category, the more urgent gap may be the buyer situation where every incumbent appears and you do not.

Confidence intervals and repeated samples still matter. A category benchmark is a baseline, not a guarantee that a one-place movement is real. Freeze the prompt set, preserve model-level results, and use the process in our recommendation-variance field note before declaring a win.

The strategic lesson is category fit, not content volume

These markets do not support one universal GEO playbook. Cosmetic surgery has a wide field but strong cross-model coverage among its leaders. Plaintiff employment is concentrated while IVF has the broadest top-ten model coverage. Each structure calls for a different competitive plan.

The practical sequence is to map the category, find the firm’s missing models and buyer situations, inspect the evidence used for the firms already winning those cells, and publish the smallest credible asset that closes the gap. That might be a physician profile, a service comparison, a jurisdiction-specific answer, a documented result, or a clearer location page.

AI visibility benchmarks are useful when they narrow the next decision. The market-wide number does not tell a firm what to write. It tells the firm what kind of contest it is entering—and prevents it from celebrating a score that would be weak in its own category. The full 2026 NYC AI Recommendation Index preserves the snapshot scope and limitations behind these benchmarks.