How AI Search Is Recommending Applicant Tracking Systems: Monthly Trends

Executive Summary

Applicant Tracking Systems' AI recommendation coverage moved within the category's established baseline-to-current pattern through September 2026. Greenhouse remains the category leader at 53.4% valid recommendation coverage, essentially unchanged from its July baseline of 53.6% (down 0.2 points), a change the benchmark classifies as stable.

Coverage across all ten tracked brands fell sharply in August before recovering in September. Ashby recorded the largest month-over-month gain, rising from 4.0% coverage in August to 36.8% in September, up 32.8 points; against its July baseline of 33.1%, that places Ashby up a more modest 3.7 points, a change the benchmark classifies as stable. Bullhorn is the only brand the benchmark classifies as a significant decliner against baseline, falling from 17.9% coverage in July to 13.3% in September, down 4.6 points, even after rebounding 12.1 points from its August low of 1.2%.

Nine of the ten tracked brands — all except Bullhorn — are classified as stable against their July baseline levels. The September figures largely restore the category to its July configuration. The benchmark data does not establish why August recorded lower coverage across the board.

Each monthly run begins with 800 prompt-surface observations across the benchmark's defined AI/search surface universe. July produced 515 unique questions, 800 brand-mentioned prompts, 636 relevant and 164 irrelevant, yielding 526 qualified observations. August's funnel was reported with a smaller qualified set of 346 observations. September produced 556 unique questions, 800 brand-mentioned prompts, 631 relevant and 169 irrelevant, yielding 513 qualified observations. The public metrics in this report use the qualified observation counts that survive 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 qualified observation count returned to near-July levels, rising from 346 in August to 513 in September. This restored base supports the current month's brand-level percentages.

Benchmark-Level Metrics

The intermediate month tells the sharper story: recommendation-shaped answer share fell to 3.5% in August before recovering to 38.8% in September, and valid recommendation shortlist share fell to 4.0% before recovering to 55.0%.

AI Recommendation Trend

The Category Reverted to July's Configuration, With Most Brands Stable Against Baseline

September's field returned to a familiar shape. Greenhouse leads with 53.4% coverage, followed by Workable at 48.3%, Lever at 37.4%, and Ashby at 36.8%. Nine of ten brands are classified as stable against their July baselines, meaning the August contraction did not persist into September.

The category-level change in September came from the combination of several movements relative to July, with no brand except Bullhorn exceeding the range the benchmark treats as normal month-to-month variation against baseline. The August dip affected all brands, and September's recovery restored most to near their July positions.

What Changed This Month

Greenhouse: Stable Leader at the Top

Greenhouse holds 53.4% valid recommendation coverage in September, down a marginal 0.2 points from 53.6% in July, a change the benchmark classifies as stable. Against August's low of 5.8%, September's figure reflects a 47.6-point rebound.

Placement quality held steady. Greenhouse's rank-one rate rose from 25.1% in July to 26.1% in September. The brand earned 134 rank-one placements out of 274 valid recommendations in September, versus 132 out of 282 in July.

The distinction to notice: Greenhouse is both the most-visible brand, present in 93.0% of September observations, and the most-recommended. Its leadership is consistent across the three-month series despite the August interruption.

Highest-priority diagnostic: Which prompt patterns sustain the 134 rank-one placements, and do they concentrate in particular AI surfaces?

Ashby: Strongest Rebound From the Prior Month

Ashby climbed from 4.0% valid recommendation coverage in August to 36.8% in September, up 32.8 points, the largest month-over-month gain in the category. Against its July baseline of 33.1%, the brand is up 3.7 points, a change the benchmark classifies as stable.

Ashby's raw mention presence rose from 40.1% in July to 46.6% in September, up 6.5 points. Its top-three rate rose from 12.2% to 16.8%. The brand earned 189 valid recommendations in September versus 174 in July, including 13 rank-one placements versus 7 in July.

The distinction to notice: Ashby gained both presence and recommendation depth, but its rank-one rate of 2.5% remains modest. The brand is being recommended more often without yet winning the top spot at scale.

Highest-priority diagnostic: Which prompts convert Ashby from a top-three mention into a rank-one recommendation, and which competitor currently holds that position instead?

Bullhorn: The Category's Only Significant Decliner From Baseline

Bullhorn fell from 17.9% valid recommendation coverage in July to 13.3% in September, down 4.6 points, the only movement the benchmark classifies as a significant decliner against baseline in this period. The brand's August level was 1.2%, so September represents a rebound of 12.1 points, but the recovery stopped short of the July baseline.

Bullhorn earned 68 valid recommendations in September versus 94 in July. Its top-three rate rose from 2.7% to 4.1%, and its rank-one rate rose from 0.9% to 2.7%. Raw mention presence fell from 24.1% to 22.0%.

The distinction to notice: Bullhorn is being recommended more prominently when it appears, with a rank-one rate gain, but appears in fewer answers overall. Its September coverage gap reflects presence, not placement quality.

Highest-priority diagnostic: Which surfaces reduced Bullhorn's overall presence, and are the rank-one gains concentrated in a narrow set of prompts?

Workable: Gaining Presence While Losing Rank-One Share

Workable rose from 46.6% valid recommendation coverage in July to 48.3% in September, up 1.7 points, a change the benchmark classifies as stable. Its raw mention presence rose from 66.2% to 72.9%, up 6.7 points, one of the largest presence gains in the category alongside Ashby.

Workable's rank-one rate fell from 4.6% in July to 2.1% in September, down 2.5 points. The brand earned 11 rank-one placements in September versus 24 in July, even as its total valid recommendations rose from 245 to 248.

The distinction to notice: Workable is appearing in more AI answers but winning the top recommendation slot less often. Its coverage is holding through breadth, not through rank-one dominance.

Highest-priority diagnostic: Which competitor is capturing the rank-one position in prompts where Workable appears but does not lead?

iCIMS and Workday Recruiting: Top-Three Share Eroded Despite Stable Coverage

iCIMS fell from 29.8% valid recommendation coverage in July to 26.5% in September, down 3.3 points, a change the benchmark classifies as stable. Its top-three rate fell from 6.3% to 3.5%, down 2.8 points. Workday Recruiting fell from 28.7% to 26.3%, down 2.4 points, with its top-three rate falling from 7.6% to 4.3%, down 3.3 points.

iCIMS earned 136 valid recommendations in September versus 157 in July, including 18 top-three placements versus 33. Workday Recruiting earned 135 valid recommendations versus 151, including 22 top-three placements versus 40.

The distinction to notice: both brands maintained most of their overall coverage but lost ground in the top-three placement tier. Their recommendations are becoming less prominent relative to their total presence.

Highest-priority diagnostic: Which prompt types shifted these brands from top-three mentions into lower placement tiers, and which brands moved up in their place?

Buyer-Intent Interpretation

The qualified observations in July, August, and September all fell entirely into the Brand Recommendation class. No observations qualified as Pricing & Value or Multi-Brand Comparison in any of the three months. The public benchmark therefore captures which brands AI systems recommend for direct asks, but it cannot yet answer how those same systems handle price, value, or head-to-head comparison questions for applicant tracking systems.

Brand Opportunity Summary

The benchmark identifies where attention is warranted; a company-level analysis is needed to explain why.

Evidence Behind the Benchmark

The aggregate metrics are built from prompt-level observations (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 the LLM Authority Index AI Market Discovery research program. The canonical research links are:

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

Report-Specific Interpretation Notes

• September 2026 figures rest on a qualified base of 513 observations, close to July's 526. August's smaller base of 346 observations means prior-month comparisons should be read with that denominator change in mind.
• Nine of ten brands are classified as stable against their July baselines. Bullhorn is the only significant decliner, down 4.6 points from 17.9% to 13.3%.
• The August contraction affected all brands simultaneously and did not persist into September. This benchmark identifies where attention is warranted, not what caused the movement.

Next Step

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

The aggregate percentages raise the questions that matter: which high-intent prompts are won, which competitor takes the recommendation when a brand loses, what attributes AI associates with each option, and which external sources shape those answers. The September rebound restored most brands to their July positions, but it also exposed specific pressure points, including Bullhorn's incomplete recovery, Workable's declining rank-one share, and the erosion of top-three placements for iCIMS and Workday Recruiting. Knowing that coverage moved is only the first step. Knowing which prompts, surfaces, and evidence sources produce those outcomes is what separates a visibility problem from a positioning problem.

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy.

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