Applicant Tracking Systems AI Visibility Ranking Trends - November 2026

Answer capsule

The November 2026 Applicant Tracking Systems benchmark compares 10 companies across 533 AI answers that met its inclusion rules. Greenhouse leads with 235 recommendations (44.09% of answers). At least one tracked company was recommended in 259 answers; some answers recommended several companies.

The rankings distinguish being mentioned from receiving an affirmative recommendation, then examine placement and the wording used about each company. Earlier monthly results show how counts and rates changed, with each month using its own answer total. These results describe the answers reviewed, not product quality or customer satisfaction. Differences between monthly samples limit comparisons and do not establish why a result changed.

For the strategic interpretation of this benchmark, read CiteWorks Studio's analysis of How AI Search Is Recommending Applicant Tracking Systems

Industry leaderboard

Questions This Section Answers

• How do Applicant Tracking Systems rank by recorded AI recommendations in November 2026?

The leaderboard orders companies by how many answers affirmatively recommended them. That count is not the same as reach: a company can appear in many answers without being endorsed in most of them. Ties in recommendation counts would share a rank, so ordering should be read alongside the printed counts rather than assumed from position alone.

Mentions, recommendations, shortlist placements and first-place placements answer different questions. Placements are nested inside recommendations, so they should not be summed with them. Recommendation rates use the full answer total, which lets companies with different mention volumes be compared on a common base.

Key findings

Tracked-company answer coverage

Questions This Section Answers

• How often did the November 2026 Applicant Tracking Systems answer sample recommend any tracked company?

259 distinct answers recommended at least one tracked company. Counting each company recommendation separately gives 1419, because an answer can recommend several companies. Answers with no tracked recommendation may still have recommended companies outside this comparison.

Every included answer mentioned at least one tracked company, but only about half recommended one. That gap is the central coverage fact: appearing in an answer is common, while affirmative endorsement is not. Answers recommending several tracked companies are also common, which is why company-level recommendation counts exceed the number of answers with any recommendation.

Coverage describes the recorded answers, not market share. An answer with no tracked recommendation may still have named companies outside this comparison, so absence here is not evidence that no recommendation occurred.

Reach and affirmative recommendation

Greenhouse ranks 1 by recommendation count. Mentions: 514 (96.44%); recommendations: 235 (44.09%). Top 3 placements: 90 (16.89%); first-place placements: 71 (13.32%).

Wording about the company: 252 positive, 260 neutral, 2 negative mentions. The proportion of mentions that also included a recommendation was 45.7%. The net framing score was 0.49 (defined in Methodology).

279 mentions had no recorded recommendation. Of recorded recommendations, 38.30% were Top 3 and 30.21% were first-place.

Greenhouse illustrates why mention and recommendation must be separated. It appeared in nearly every answer but was affirmatively recommended in fewer than half, leaving a large block of mentions with no recorded recommendation. High reach therefore describes visibility in the answers, not endorsement.

Among its recorded recommendations, shortlist and first-place placements form nested subsets rather than additional wins. The wording around Greenhouse was overwhelmingly positive or neutral, with very few negative mentions. Framing describes how the company was described, not why a recommendation was or was not recorded.

Recommendation order and placement

Placement describes where a recommended company sat in the answer's ordering. Shortlist placements include first-place placements, so the two are nested rather than additive. A company with many recommendations but relatively few placements was endorsed often without being placed near the top.

Recommendation-to-mention ratios vary across companies and describe the recorded answers only. They are not conversion rates, customer preference or evidence of sales performance. Where a ratio rests on a smaller mention base, it should be read with more caution than one built on a larger base.

Classified framing in context

Framing classifies the wording used about each company in the selected answers. Positive and neutral descriptions dominate, and negative wording is rare across the tracked set. A neutral classification means the wording carried no clear positive or negative tone; it does not mean the answer was indifferent or that no recommendation occurred.

Net framing summarizes positive minus negative wording relative to all mentions. It is not customer sentiment, product quality or a measure of satisfaction. Differences in framing between companies should not be read as causes of their recommendation counts.

Company results in detail

Greenhouse

Questions This Section Answers

• What do the November 2026 AI answer results show about Greenhouse mentions, recommendations and placement?

Greenhouse ranks 1 by recommendation count. Mentions: 514 (96.44%); recommendations: 235 (44.09%). Top 3 placements: 90 (16.89%); first-place placements: 71 (13.32%).

Wording about the company: 252 positive, 260 neutral, 2 negative mentions. The proportion of mentions that also included a recommendation was 45.7%. The net framing score was 0.49 (defined in Methodology).

279 mentions had no recorded recommendation. Of recorded recommendations, 38.30% were Top 3 and 30.21% were first-place.

From October 2026 to November 2026, Greenhouse's mention count rose from 451 to 514, while its recommendation count fell from 280 to 235. The recommendation rate was 58.82% and 44.09% respectively.

Greenhouse leads on recommendation count and appears in almost every answer, giving it the broadest reach in the set. Its recommendation-to-mention ratio is moderate, so many appearances carried no affirmative endorsement. Wording was split between positive and neutral with almost no negative framing.

Between the two most recent months, mentions rose while recommendations fell, and the recommendation rate declined accordingly. These are separate selected samples, and the comparison does not establish why the counts moved.

Workable

Questions This Section Answers

• What do the November 2026 AI answer results show about Workable mentions, recommendations and placement?

Workable ranks 2 by recommendation count. Mentions: 368 (69.04%); recommendations: 204 (38.27%). Top 3 placements: 57 (10.69%); first-place placements: 14 (2.63%).

Wording about the company: 217 positive, 151 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 55.4%. The net framing score was 0.59 (defined in Methodology).

164 mentions had no recorded recommendation. Of recorded recommendations, 27.94% were Top 3 and 6.86% were first-place.

From October 2026 to November 2026, Workable's mention count rose from 334 to 368, while its recommendation count fell from 251 to 204. The recommendation rate was 52.73% and 38.27% respectively.

Workable holds the second recommendation count with a mention rate well below Greenhouse's, yet its recommendation-to-mention ratio is among the higher ones in the set. That combination means a larger share of its appearances included an endorsement. Wording was predominantly positive, with no negative mentions recorded.

Mentions rose between the two most recent months while recommendations fell, lowering the recommendation rate. The samples differ, so the shift is a recorded comparison rather than evidence of a cause.

Lever

Questions This Section Answers

• What do the November 2026 AI answer results show about Lever mentions, recommendations and placement?

Lever ranks 3 by recommendation count. Mentions: 422 (79.17%); recommendations: 196 (36.77%). Top 3 placements: 52 (9.76%); first-place placements: 19 (3.56%).

Wording about the company: 215 positive, 206 neutral, 1 negative mentions. The proportion of mentions that also included a recommendation was 46.4%. The net framing score was 0.51 (defined in Methodology).

226 mentions had no recorded recommendation. Of recorded recommendations, 26.53% were Top 3 and 9.69% were first-place.

From October 2026 to November 2026, Lever's mention count rose from 335 to 422, while its recommendation count rose from 193 to 196. The recommendation rate was 40.55% and 36.77% respectively.

Lever combines wide reach with a mid-range recommendation-to-mention ratio, so many appearances did not include an endorsement. Its first-place placements are a modest subset of its recommendations. Wording was largely positive or neutral, with a single negative mention.

Unlike several peers, Lever's recommendation count edged upward between the two most recent months even as its recommendation rate slipped slightly, because mentions grew faster. The comparison is descriptive and does not explain the movement.

iCIMS

Questions This Section Answers

• What do the November 2026 AI answer results show about iCIMS mentions, recommendations and placement?

iCIMS ranks 4 by recommendation count. Mentions: 279 (52.35%); recommendations: 141 (26.45%). Top 3 placements: 24 (4.50%); first-place placements: 11 (2.06%).

Wording about the company: 152 positive, 127 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 50.5%. The net framing score was 0.54 (defined in Methodology).

138 mentions had no recorded recommendation. Of recorded recommendations, 17.02% were Top 3 and 7.80% were first-place.

From October 2026 to November 2026, iCIMS's mention count rose from 220 to 279, while its recommendation count rose from 134 to 141. The recommendation rate was 28.15% and 26.45% respectively.

iCIMS sits in the middle of the recommendation ranking with moderate reach and a recommendation-to-mention ratio near the middle of the range. Its shortlist and first-place placements are small subsets of its recommendations. Wording was positive or neutral, with no negative mentions recorded.

Both mentions and recommendations rose between the two most recent months, while the recommendation rate eased slightly. Because the monthly samples differ, the change is reported as recorded rather than attributed to any factor.

BambooHR

Questions This Section Answers

• What do the November 2026 AI answer results show about BambooHR mentions, recommendations and placement?

BambooHR ranks 5 by recommendation count. Mentions: 286 (53.66%); recommendations: 137 (25.70%). Top 3 placements: 17 (3.19%); first-place placements: 12 (2.25%).

Wording about the company: 153 positive, 133 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 47.9%. The net framing score was 0.53 (defined in Methodology).

149 mentions had no recorded recommendation. Of recorded recommendations, 12.41% were Top 3 and 8.76% were first-place.

From October 2026 to November 2026, BambooHR's mention count rose from 257 to 286, while its recommendation count fell from 157 to 137. The recommendation rate was 32.98% and 25.70% respectively.

BambooHR's reach is comparable to iCIMS's, but a smaller share of its mentions carried an endorsement. Its placements are modest relative to its recommendation count, and first-place placements account for a notable portion of its shortlist appearances. Wording was positive or neutral throughout.

Mentions rose between the two most recent months while recommendations fell, and the recommendation rate declined. The separate samples do not establish why the counts diverged.

Ashby

Questions This Section Answers

• What do the November 2026 AI answer results show about Ashby mentions, recommendations and placement?

Ashby ranks 6 by recommendation count. Mentions: 222 (41.65%); recommendations: 135 (25.33%). Top 3 placements: 28 (5.25%); first-place placements: 14 (2.63%).

Wording about the company: 142 positive, 80 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 60.8%. The net framing score was 0.64 (defined in Methodology).

87 mentions had no recorded recommendation. Of recorded recommendations, 20.74% were Top 3 and 10.37% were first-place.

From October 2026 to November 2026, Ashby's mention count rose from 203 to 222, while its recommendation count fell from 164 to 135. The recommendation rate was 34.45% and 25.33% respectively.

Ashby has the highest recommendation-to-mention ratio in the set, meaning a comparatively large share of its appearances included an endorsement, even though its overall reach is lower than the leaders'. Its placements are a modest subset of its recommendations. Wording was positive or neutral, with no negative mentions.

Mentions rose between the two most recent months while recommendations fell, lowering the recommendation rate. The ratio rests on a smaller mention base than the leaders', so it should be read with that qualification in mind.

Workday Recruiting

Questions This Section Answers

• What do the November 2026 AI answer results show about Workday Recruiting mentions, recommendations and placement?

Workday Recruiting ranks 7 by recommendation count. Mentions: 326 (61.16%); recommendations: 131 (24.58%). Top 3 placements: 26 (4.88%); first-place placements: 18 (3.38%).

Wording about the company: 147 positive, 179 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 40.2%. The net framing score was 0.45 (defined in Methodology).

195 mentions had no recorded recommendation. Of recorded recommendations, 19.85% were Top 3 and 13.74% were first-place.

From October 2026 to November 2026, Workday Recruiting's mention count rose from 258 to 326, while its recommendation count rose from 118 to 131. The recommendation rate was 24.79% and 24.58% respectively.

Workday Recruiting has substantial reach but the lowest recommendation-to-mention ratio among the tracked companies, so most of its appearances carried no endorsement. Its first-place placements form a relatively large share of its recommendations. Wording leaned neutral more than positive, with no negative mentions.

Both mentions and recommendations rose between the two most recent months, while the recommendation rate held nearly steady. The comparison describes recorded results and does not explain them.

JazzHR

Questions This Section Answers

• What do the November 2026 AI answer results show about JazzHR mentions, recommendations and placement?

JazzHR ranks 8 by recommendation count. Mentions: 214 (40.15%); recommendations: 96 (18.01%). Top 3 placements: 13 (2.44%); first-place placements: 10 (1.88%).

Wording about the company: 108 positive, 106 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 44.9%. The net framing score was 0.50 (defined in Methodology).

118 mentions had no recorded recommendation. Of recorded recommendations, 13.54% were Top 3 and 10.42% were first-place.

From October 2026 to November 2026, JazzHR's mention count rose from 146 to 214, while its recommendation count fell from 109 to 96. The recommendation rate was 22.90% and 18.01% respectively.

JazzHR's reach and recommendation count place it in the lower half of the ranking, with a recommendation-to-mention ratio near the middle of the range. Its placements are small subsets of its recommendations. Wording was evenly split between positive and neutral, with no negative mentions.

Mentions rose between the two most recent months while recommendations fell, and the recommendation rate declined. The samples differ, so the movement is a recorded comparison rather than a causal finding.

Bullhorn

Questions This Section Answers

• What do the November 2026 AI answer results show about Bullhorn mentions, recommendations and placement?

Bullhorn ranks 9 by recommendation count. Mentions: 174 (32.65%); recommendations: 93 (17.45%). Top 3 placements: 9 (1.69%); first-place placements: 7 (1.31%).

Wording about the company: 100 positive, 74 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 53.4%. The net framing score was 0.57 (defined in Methodology).

81 mentions had no recorded recommendation. Of recorded recommendations, 9.68% were Top 3 and 7.53% were first-place.

From October 2026 to November 2026, Bullhorn's mention count rose from 120 to 174, while its recommendation count rose from 81 to 93. The recommendation rate was 17.02% and 17.45% respectively.

Bullhorn has the lowest reach among the tracked companies but a recommendation-to-mention ratio above the middle of the range, so a fair share of its appearances included an endorsement. Its placements are small in absolute terms. Wording was mostly positive or neutral, with no negative mentions.

Both mentions and recommendations rose between the two most recent months, and the recommendation rate edged up slightly. The comparison is descriptive and does not establish why the counts changed.

SmartRecruiters

Questions This Section Answers

• What do the November 2026 AI answer results show about SmartRecruiters mentions, recommendations and placement?

SmartRecruiters ranks 10 by recommendation count. Mentions: 108 (20.26%); recommendations: 51 (9.57%). Top 3 placements: 6 (1.13%); first-place placements: 4 (0.75%).

Wording about the company: 54 positive, 54 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 47.2%. The net framing score was 0.50 (defined in Methodology).

57 mentions had no recorded recommendation. Of recorded recommendations, 11.76% were Top 3 and 7.84% were first-place.

From October 2026 to November 2026, SmartRecruiters' mention count rose from 106 to 108, while its recommendation count fell from 75 to 51. The recommendation rate was 15.76% and 9.57% respectively.

SmartRecruiters has the lowest recommendation count and the lowest reach in the set, with a recommendation-to-mention ratio near the middle of the range. Its placements are correspondingly small. Wording was evenly divided between positive and neutral, with no negative mentions.

Mentions were nearly flat between the two most recent months while recommendations fell, and the recommendation rate declined. The separate samples do not explain the movement, and the smaller base warrants caution in reading the ratio.

Earlier ranking snapshot context

Questions This Section Answers

• How do the available Applicant Tracking Systems snapshots from July 2026 to November 2026 differ in recommendation coverage and measurement scope?

Only months with verified results are shown. Monthly sample coverage is listed in Methodology.

The monthly snapshots come from separate selected samples of AI answers, so counts and rates are not directly interchangeable across months. A company's rank can shift because its own count changed, because peers' counts changed, or both. The printed history shows recorded results only; it does not establish rank stability or equivalent sampling between periods.

August is absent because reliable results were unavailable, which is a gap rather than a zero. Comparisons across months describe how the recorded results differed, not why.

What the November Rankings Show

The November results answer several distinct questions. Reach asks how often a company appeared; recommendation asks how often an answer affirmatively endorsed it; placement asks where an endorsed company sat in the ordering; framing asks how the wording was toned. A company can be highly visible yet rarely endorsed, or endorsed often without leading the ordering.

Because placements are nested within recommendations, and recommendations within mentions, the measures should be read as layers rather than competing scores. Recommendation-to-mention ratios describe the recorded answers, not customers or sales. Framing is not sentiment or quality. The monthly comparisons are separate samples, so changes are reported without causal explanation.

Methodology

Questions This Section Answers

• How were the November 2026 Applicant Tracking Systems AI visibility measures counted, and what do they not establish?

This benchmark has partial coverage: 3 of 3 planned question categories contributed usable observations; 0 contributed none. This describes question-category coverage, not demonstrated platform outages. Findings apply to the available answer set.

The month in the title is the reporting period. The exact dates when the original AI answers were collected have not been verified.

Each company is counted at most once in each answer that meets the benchmark rules. A recommendation requires both an affirmative endorsement and positive wording. Mention, recommendation, Top 3 and first-place rates use the full benchmark answer total. Top 3 and first-place results are included within recommendations, not added to them.

The recommendation-to-mention ratio divides recommendations by mentions. It describes AI answer wording, not customer conversion. Net framing is positive minus negative mentions, divided by all mentions, on a scale from -1 to +1. It does not measure customer satisfaction or company quality.

Market: US; 10 tracked companies. AI services represented: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity.

Research screening totals (answer observations can include repeated questions):

The source does not provide a reader-facing definition of “reserved”; that screening label is reported without inferring a reason for exclusion. Only the included benchmark answers form the rate denominator.

One answer can mention or recommend several companies. Adding company totals would therefore count some answers more than once. The results describe the AI answers reviewed, not independently verified facts about the companies or their products.

Monthly sample coverage:

Reliable results for August 2026 are unavailable. This is a gap in the history, not a result of zero.

The answer samples differ between months. These comparisons show how the recorded results changed; they do not establish why they changed.

The benchmark covers a partial set of answers from several AI services, and the exact collection dates have not been verified. Each company is counted at most once per answer, and a recommendation requires both affirmative endorsement and positive wording. Rates use the full included answer total, while placements are subsets of recommendations.

The recommendation-to-mention ratio and net framing describe answer wording, not customer behavior or company quality. Monthly samples differ, so comparisons show recorded change without establishing cause. One answer can name several companies, so company totals should not be summed as if they were distinct answers.