Expense Management Software AI Visibility Ranking Trends - November 2026

Answer capsule

The November 2026 Expense Management Software benchmark compares 10 companies across 380 AI answers that met its inclusion rules. Ramp leads with 330 recommendations (86.84% of answers). At least one tracked company was recommended in 359 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 Expense Management Software

Industry leaderboard

Questions This Section Answers

• How do Expense Management Software rank by recorded AI recommendations in November 2026?

The leaderboard orders companies by how many answers recorded an affirmative recommendation, so it measures endorsement frequency in the reviewed AI answers rather than product quality or market share. A company can appear often yet be recommended rarely, and the two readings belong side by side.

Ties require equal recommendation counts; near-equal counts are not ties. Placement figures are nested inside recommendations, so first-place and shortlist results describe position within endorsements, not additional wins.

Key findings

Tracked-company answer coverage

Questions This Section Answers

• How often did the November 2026 Expense Management Software answer sample recommend any tracked company?

359 distinct answers recommended at least one tracked company. Counting each company recommendation separately gives 1410, because an answer can recommend several companies. Answers with no tracked recommendation may still have recommended companies outside this comparison.

Almost every qualified answer named at least one tracked company, and most recommended one. Because a single answer can endorse several companies, company-level totals exceed the number of answers, and summing them would double-count answers.

Coverage describes overlap in the reviewed answers, not overall market dominance. Answers without a tracked recommendation may still have named companies outside this comparison, so the gap is not evidence of an empty answer.

Reach and affirmative recommendation

Ramp ranks 1 by recommendation count. Mentions: 356 (93.68%); recommendations: 330 (86.84%). Top 3 placements: 247 (65.00%); first-place placements: 126 (33.16%).

Wording about the company: 335 positive, 21 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 92.7%. The net framing score was 0.94 (defined in Methodology).

26 mentions had no recorded recommendation. Of recorded recommendations, 74.85% were Top 3 and 38.18% were first-place.

Mention records that a company appeared in an answer; recommendation records an affirmative endorsement. Ramp's mentions and recommendations are close, meaning most appearances carried endorsement, though a portion of mentions did not.

A high recommendation count reflects frequency across the reviewed answers, not a unique win over peers. Where counts are equal, the ranking shows a tie rather than a decisive lead.

Recommendation order and placement

Placement describes where a recommended company sat in an answer's ordering. Shortlist placements sit inside recommendations, and first-place placements sit inside shortlist placements, so these are nested subsets rather than separate achievements.

The recommendation-to-mention ratio compares endorsements with appearances in the recorded answers. It is not customer conversion or sales evidence, and a strong ratio on a small base should be read cautiously.

Classified framing in context

Framing classifies the wording used about a company in the reviewed answers as positive, neutral or negative. It is not customer sentiment, reputation or a judgment of company quality, and neutral wording does not explain why a recommendation was or was not recorded.

Most tracked companies drew no negative wording in this sample. That absence describes the recorded answers only and does not establish that criticism was absent elsewhere.

Company results in detail

Ramp

Questions This Section Answers

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

Ramp ranks 1 by recommendation count. Mentions: 356 (93.68%); recommendations: 330 (86.84%). Top 3 placements: 247 (65.00%); first-place placements: 126 (33.16%).

Wording about the company: 335 positive, 21 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 92.7%. The net framing score was 0.94 (defined in Methodology).

26 mentions had no recorded recommendation. Of recorded recommendations, 74.85% were Top 3 and 38.18% were first-place.

From October 2026 to November 2026, Ramp's mention count fell from 432 to 356, while its recommendation count fell from 339 to 330. The recommendation rate was 72.28% and 86.84% respectively.

Ramp leads on recommendation count and pairs that reach with strong placement depth, including a substantial share of first-place positions. Its wording is overwhelmingly positive with little neutral framing, and most mentions also carried a recommendation.

Compared with the prior month, both mentions and recommendations declined while the recommendation rate rose. The changed sample does not establish why any of these movements occurred.

Brex

Questions This Section Answers

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

Brex ranks 2 by recommendation count. Mentions: 287 (75.53%); recommendations: 263 (69.21%). Top 3 placements: 176 (46.32%); first-place placements: 20 (5.26%).

Wording about the company: 266 positive, 21 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 91.6%. The net framing score was 0.93 (defined in Methodology).

24 mentions had no recorded recommendation. Of recorded recommendations, 66.92% were Top 3 and 7.60% were first-place.

From October 2026 to November 2026, Brex's mention count fell from 347 to 287, while its recommendation count rose from 262 to 263. The recommendation rate was 55.86% and 69.21% respectively.

Brex holds second place on recommendation count with a high share of mentions carrying endorsement. Its shortlist placements are frequent, but first-place positions are comparatively rare, so its strength lies in being named among options rather than ranked at the top.

Mentions fell while recommendations held roughly level month over month, and the recommendation rate rose. These are recorded comparisons, not evidence of cause.

SAP Concur

Questions This Section Answers

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

SAP Concur ranks 3 by recommendation count. Mentions: 270 (71.05%); recommendations: 245 (64.47%). Top 3 placements: 153 (40.26%); first-place placements: 62 (16.32%).

Wording about the company: 250 positive, 20 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 90.7%. The net framing score was 0.93 (defined in Methodology).

25 mentions had no recorded recommendation. Of recorded recommendations, 62.45% were Top 3 and 25.31% were first-place.

From October 2026 to November 2026, SAP Concur's mention count rose from 268 to 270, while its recommendation count rose from 208 to 245. The recommendation rate was 44.35% and 64.47% respectively.

SAP Concur ranks third on recommendations, with most mentions endorsed and a meaningful share of first-place positions. Its placement profile is deeper at the top than several higher-mention peers, giving it a distinctive position within endorsements.

Both mentions and recommendations rose from the prior month, alongside a higher recommendation rate. The separate samples do not explain the movement.

Expensify

Questions This Section Answers

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

Expensify ranks 4 by recommendation count. Mentions: 250 (65.79%); recommendations: 229 (60.26%). Top 3 placements: 134 (35.26%); first-place placements: 49 (12.89%).

Wording about the company: 234 positive, 16 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 91.6%. The net framing score was 0.94 (defined in Methodology).

21 mentions had no recorded recommendation. Of recorded recommendations, 58.52% were Top 3 and 21.40% were first-place.

From October 2026 to November 2026, Expensify's mention count rose from 248 to 250, while its recommendation count rose from 201 to 229. The recommendation rate was 42.86% and 60.26% respectively.

Expensify sits fourth on recommendation count, with a high proportion of mentions endorsed and moderate first-place depth. Its framing is almost entirely positive, with few neutral mentions recorded.

Mentions were nearly flat while recommendations rose month over month, and the recommendation rate increased. These recorded changes do not demonstrate improvement or causation.

Navan

Questions This Section Answers

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

Navan ranks 5 by recommendation count. Mentions: 187 (49.21%); recommendations: 167 (43.95%). Top 3 placements: 88 (23.16%); first-place placements: 44 (11.58%).

Wording about the company: 168 positive, 19 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 89.3%. The net framing score was 0.90 (defined in Methodology).

20 mentions had no recorded recommendation. Of recorded recommendations, 52.69% were Top 3 and 26.35% were first-place.

From October 2026 to November 2026, Navan's mention count rose from 156 to 187, while its recommendation count rose from 129 to 167. The recommendation rate was 27.51% and 43.95% respectively.

Navan ranks fifth, with roughly half of answers mentioning it and a slightly smaller share recommending it. Its first-place depth is notable relative to its shortlist placements, suggesting endorsement often placed it near the top when it appeared.

Both mentions and recommendations rose from the prior month, and the recommendation rate increased. The differing samples do not establish why.

BILL Spend & Expense

Questions This Section Answers

• What do the November 2026 AI answer results show about BILL Spend & Expense mentions, recommendations and placement?

BILL Spend & Expense ranks 6 by recommendation count. Mentions: 149 (39.21%); recommendations: 89 (23.42%). Top 3 placements: 44 (11.58%); first-place placements: 8 (2.11%).

Wording about the company: 90 positive, 59 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 59.7%. The net framing score was 0.60 (defined in Methodology).

60 mentions had no recorded recommendation. Of recorded recommendations, 49.44% were Top 3 and 8.99% were first-place.

From October 2026 to November 2026, BILL Spend & Expense's mention count rose from 134 to 149, while its recommendation count fell from 112 to 89. The recommendation rate was 23.88% and 23.42% respectively.

BILL Spend & Expense shows a marked gap between mentions and recommendations: it appears in many answers but is endorsed in a minority, and neutral framing is unusually common in its profile. Shortlist and first-place placements are correspondingly limited.

Mentions rose while recommendations fell month over month, with the recommendation rate roughly steady. The recorded comparison does not explain the divergence.

Emburse

Questions This Section Answers

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

Emburse ranks 7 by recommendation count. Mentions: 77 (20.26%); recommendations: 49 (12.89%). Top 3 placements: 14 (3.68%); first-place placements: 1 (0.26%).

Wording about the company: 52 positive, 25 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 63.6%. The net framing score was 0.68 (defined in Methodology).

28 mentions had no recorded recommendation. Of recorded recommendations, 28.57% were Top 3 and 2.04% were first-place.

From October 2026 to November 2026, Emburse's mention count rose from 56 to 77, while its recommendation count rose from 36 to 49. The recommendation rate was 7.68% and 12.89% respectively.

Emburse ranks seventh, with modest reach and a recommendation-to-mention ratio below the leading group. Neutral framing appears in a substantial portion of its mentions, and first-place positions are rare.

Both mentions and recommendations rose from the prior month, and the recommendation rate increased. These are recorded changes across separate samples, not evidence of cause.

Airbase

Questions This Section Answers

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

Airbase ranks 8 by recommendation count. Mentions: 24 (6.32%); recommendations: 22 (5.79%). Top 3 placements: 6 (1.58%); first-place placements: 2 (0.53%).

Wording about the company: 22 positive, 2 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 91.7%. The net framing score was 0.92 (defined in Methodology).

2 mentions had no recorded recommendation. Of recorded recommendations, 27.27% were Top 3 and 9.09% were first-place.

From October 2026 to November 2026, Airbase's mention count fell from 48 to 24, while its recommendation count fell from 34 to 22. The recommendation rate was 7.25% and 5.79% respectively.

Airbase has a small answer base, so its counts are low in absolute terms even though most of its mentions carried endorsement. Its placement depth is limited, with few shortlist and first-place positions.

Both mentions and recommendations fell from the prior month. Given the sparse base, ratios should be read alongside the small counts rather than as stable measures.

Rydoo

Questions This Section Answers

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

Rydoo ranks 9 by recommendation count. Mentions: 37 (9.74%); recommendations: 16 (4.21%). Top 3 placements: 4 (1.05%); first-place placements: 0 (0.00%).

Wording about the company: 17 positive, 20 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 43.2%. The net framing score was 0.46 (defined in Methodology).

21 mentions had no recorded recommendation. Of recorded recommendations, 25.00% were Top 3 and 0.00% were first-place.

From October 2026 to November 2026, Rydoo's mention count rose from 12 to 37, while its recommendation count rose from 8 to 16. The recommendation rate was 1.71% and 4.21% respectively.

Rydoo combines modest reach with a low recommendation-to-mention ratio, and neutral framing appears in more than half of its mentions. It recorded no first-place positions in this sample, and shortlist placements were few.

Both mentions and recommendations rose from the prior month, though the base remains small. The recorded comparison does not establish why the counts changed.

Zoho Inventory

Questions This Section Answers

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

Zoho Inventory ranks 10 by recommendation count. Mentions: 177 (46.58%); recommendations: 0 (0.00%). Top 3 placements: 0 (0.00%); first-place placements: 0 (0.00%).

Wording about the company: 0 positive, 177 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 0.0%. The net framing score was 0.00 (defined in Methodology).

177 mentions had no recorded recommendation. Of recorded recommendations, Unavailable were Top 3 and Unavailable were first-place.

From October 2026 to November 2026, Zoho Inventory's mention count rose from 0 to 177, while its recommendation count stayed at 0. The recommendation rate was 0.00% and 0.00% respectively.

Zoho Inventory was mentioned in many answers but received no recorded recommendations, and every mention was classified as neutral. Its placement counts are therefore zero, which is a measured result rather than a missing observation.

Mentions rose from none in the prior month while recommendations stayed at zero. Ratios requiring a nonzero recommendation base are undefined here, and the change does not explain the absence of endorsement.

Earlier ranking snapshot context

Questions This Section Answers

• How do the available Expense Management Software 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.

Each month is a separate sample of AI answers, and the totals differ between months, so counts and rates should be read together rather than as a continuous series. A higher or lower count in one month does not by itself show a change in standing.

The earlier snapshots show how recorded results moved across selected months. They do not establish rank stability, equivalent sampling or the reasons behind any shift.

What the November Rankings Show

These sections answer different questions. Reach asks whether a company appeared; recommendation asks whether an answer affirmatively endorsed it; placement asks where an endorsement sat; framing asks how the wording was classified. A company can be visible without being endorsed, or endorsed without ranking first.

Because placements nest inside recommendations and answers can cover several companies, the figures should not be added into a single score. The rankings describe the reviewed answers, not customers, sales or verified product facts, and the monthly comparisons show recorded change without explaining it.

Methodology

Questions This Section Answers

• How were the November 2026 Expense Management Software 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:

August 2026 measured a different set of companies, so it is excluded from this comparison.

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 planned question categories, and findings apply only to the answers reviewed. The reporting month labels the period, but exact collection dates were not verified, and the source does not define the screening label reported as reserved.

Each company is counted at most once per answer, and recommendation requires both affirmative endorsement and positive wording. Rates use the full benchmark answer total, while placement results are subsets of recommendations. Net framing measures wording, not customer satisfaction or company quality.

Monthly samples differ in size and composition, so comparisons show recorded change without establishing cause. The results describe AI answers, not independently verified facts about the companies or their products.