How AI Search Is Recommending Web Conferencing: Monthly Trends

Executive Summary

September 2026 shows a two-brand leadership holding steady at the top while a naming shift reshapes the middle of the category. Zoom retains the lead with 62.1% valid recommendation coverage, followed closely by Google Meet at 59.9%, a gap of 2.2 points. Both leaders have been declining since July, with Zoom down 7.8 points and Google Meet down 8.3 points across the three-month series. The notable development this month is the disappearance of Cisco Webex as a tracked entity and the emergence of Cisco Webex App at 34.3% coverage, a significant single-month rise from no prior presence in the benchmark.

The strongest upward mover this month is Cisco Webex App, which registered at 34.3% valid recommendation coverage in its first tracked month. This coincides with the complete loss of coverage for Cisco Webex, which fell from 53.8% in August to no longer being tracked in September. The benchmark records this as an instrument-level naming change rather than a competitive loss, though the commercial effect is that Cisco's combined presence in AI recommendations is now measured under a different label. Whereby also rose 4.2 points month-over-month to 18.4%, the second-largest upward move but still within normal variation.

The sharpest decliner this month is RingCentral, down 9.2 points from July's 38.5% to 29.3% in September, though it recovered 5.8 points from its August low of 23.5%. The brand's fall from July to August was the category's largest single-month move at 15.0 points. Google Meet and Zoom continued gradual declines from their July peaks, with both now below 63% coverage. The category overall shows a mixed picture: one significant riser in Cisco Webex App, four significant decliners from baseline, and six stable brands.

Each monthly run begins with 800 prompt-surface observations (535 unique questions in July, 577 in August, 614 in September) across the benchmark's defined AI/search surface universe. Of those, all 800 mentioned a tracked brand or competitor in each month; 574 were relevant and 226 were irrelevant in July, 632 relevant and 168 irrelevant in August, and 542 relevant and 258 irrelevant in September. The public metrics use the 418 observations (July), 493 observations (August), and 396 observations (September) 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. September's funnel shows a smaller qualified set than August (396 versus 493 observations) despite the same 800 raw prompts, driven by a higher share of irrelevant prompts.

Benchmark-Level Metrics

The following metrics summarize the qualified benchmark set at the aggregate level.

The qualified observation set contracted from 493 in August to 396 in September, reversing the growth seen between July and August. The valid recommendation shortlist share fell from 70.6% in July to 63.9% in September, meaning a larger portion of observations are not recommendation-shortlist responses even as the recommendation-shaped answer share rose slightly. August's intermediate peak of 493 qualified observations sits between these two months in the series.

AI Recommendation Trend

The top two brands held rank while a naming shift replaced the third-place entity

The category-level change came from a naming shift rather than broad competitive movement. Cisco Webex App's 34.3-point entry from no tracked baseline and Cisco Webex's 61.5-point removal each exceed normal month-to-month variation, but these reflect a change in how the benchmark tracks the Cisco product line. Setting aside that naming shift, the remaining significant movements from baseline are downward across Google Meet, RingCentral, and Zoom, with no stable brand rising beyond normal variation.

What Changed This Month

Zoom

Zoom retained the leadership position with valid recommendation coverage of 62.1% in September, down from 69.9% in July, a decline of 7.8 points across the series. The month-over-month change was smaller: down 1.2 points from August's 63.3%. Zoom's raw mention presence eased from 98.3% in July to 96.2% in September, meaning the brand was surfaced in 381 of 396 qualifying observations.

The decline is concentrated in top placements rather than presence. Zoom's top-three rate fell from 63.6% in July to 53.5% in September, a drop of 10.1 points, and its rank-one rate fell from 54.5% to 44.2%, a drop of 10.3 points. Both moves exceed typical month-to-month variation. In absolute terms, rank-one placements fell from 228 in July to 175 in September, a decline of 53 placements even as the qualified observation set shrank.

The distinction to notice is that Zoom's raw mention presence stayed near-saturated while its share of top recommendation slots eroded steadily across all three months. The brand remains the most visible in the category by a wide margin, but the strength of that visibility, measured by how often it is the top or top-three pick, has declined each month.

Highest-priority diagnostic: Which surfaces or prompt categories drove the loss of 53 rank-one placements between July and September?

Google Meet

Google Meet's valid recommendation coverage fell from 68.2% in July to 59.9% in September, a decline of 8.3 points beyond typical month-to-month variation. Month-over-month, the brand was essentially flat, down just 0.1 points from August's 60.0%. Raw mention presence held steady around 91% in July and 90.7% in September.

The pattern mirrors Zoom but with a sharper interim drop. Google Meet fell 8.2 points between July and August, then stabilized. Its top-three rate fell from 55.7% in July to 43.4% in September, a decline of 12.3 points, while its rank-one rate actually rose slightly from 4.1% to 4.5%. The top-three decline exceeds typical month-to-month variation, but the rank-one rise does not.

The distinction is that Google Meet lost ground in the top-three conversation but held and slightly improved its standing as a first-choice recommendation, which may indicate a different competitive dynamic than Zoom's broad erosion across all top tiers.

Highest-priority diagnostic: Which competitor gained the top-three placements that Google Meet lost between July and September?

RingCentral

RingCentral's valid recommendation coverage fell from 38.5% in July to 29.3% in September, a decline of 9.2 points from baseline that exceeds normal variation. The brand fell 15.0 points between July and August, the largest single-month move in the category, then recovered 5.8 points in September. The August drop was driven partly by a decline in raw presence, while September's recovery came with raw presence at 190 of 396 observations (48.0%).

RingCentral's current raw mention presence of 48.0% sits close to its July baseline of 50.2%, a difference of 2.2 points that is within normal variation. Valid recommendation coverage at 29.3% reflects 116 valid recommendations from 396 observations.

The September picture is one of partial stabilization after a sharp August decline. The brand's top-three rate rose from 7.7% in July to 10.1% in September, a 2.4-point gain that remains within typical month-to-month variation, while its rank-one rate held roughly flat at 2.3% versus 2.1%.

Highest-priority diagnostic: Which surfaces or prompt types drove the August collapse from which RingCentral has only partially recovered?

Cisco Webex and Cisco Webex App

September's most consequential change is a naming shift: Cisco Webex was removed from tracking and Cisco Webex App entered at 34.3% valid recommendation coverage. Cisco Webex had held 61.5% coverage in July and 53.8% in August before disappearing from the tracked list in September. Cisco Webex App's 34.3% entry represents its first tracked month, with 136 valid recommendations from 396 observations.

The relationship between these two figures is not a direct read on competitive performance. The benchmark records Cisco Webex as a significant decliner with a two-month downward streak ending in removal, while Cisco Webex App is a significant riser with a one-month upward streak from no baseline. The commercial question is whether AI systems in September were referring to the product under the "Cisco Webex App" name where they previously said "Cisco Webex," or whether the underlying recommendation patterns also changed.

Cisco Webex App's raw mention presence in September was 50.8%, with a top-three rate of 7.6% and no rank-one placements. Its net sentiment score was 0.8, and its average recommended rank was 3.6. These figures are lower than Cisco Webex's July profile of 80.9% presence and 11.2% top-three rate, suggesting that if the naming shift masks a continuous product, the benchmark is capturing a different slice of how that product is discussed.

Highest-priority diagnostic: Did AI systems in September recommend "Cisco Webex App" in the same prompt contexts where they recommended "Cisco Webex" in earlier months, or did the product's presence in AI answers genuinely narrow?

Whereby

Whereby posted the largest upward move among continuously tracked brands, rising from 14.3% in July to 18.4% in September, a gain of 4.1 points that remains within normal month-to-month variation. Month-over-month, the brand rose 4.2 points from August's 14.2%. Raw mention presence rose from 15.8% in July to 24.2% in September, an 8.4-point gain that exceeds typical month-to-month variation.

The brand's top-three rate rose from 1.0% in July to 5.8% in September, a 4.8-point gain beyond typical variation, while its rank-one rate moved from zero to 0.2%. The absolute counts are small: raw presence rose from 66 to 96 observations, and top-three placements rose from 4 to 23.

The distinction is that Whereby's upward movement appears broad, spanning presence, top-three placements, and a first-ever rank-one placement, even though the percentages remain well below the category leaders.

Highest-priority diagnostic: Which prompt contexts or surfaces drove the 8.4-point rise in raw mention presence, and is that growth sustainable?

Buyer-Intent Interpretation

In September, all 396 qualified observations fell into the Brand Recommendation cluster. None of the qualified observations were classified as Pricing & Value or Multi-Brand Comparison in any of the three tracked months, meaning the public benchmark cannot yet answer questions about price positioning, value perception, or head-to-head comparison. The commercial questions the benchmark does answer are narrower: which brand an AI system recommends, at what rank, and with what sentiment. The absence of pricing and comparison observations is itself a finding, since it suggests the current prompt universe is weighted toward direct recommendation rather than evaluative or cost-sensitive queries. Response types within the qualified set included 139 recommendation shortlists, 112 factual answers, 73 comparison analyses, 54 ranked lists, and 11 pricing analyses, but none of the comparison or pricing responses qualified as part of the buyer-intent clusters.

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.

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

Report-Specific Interpretation Notes

• Small-count movements: Dialpad Meetings, Zoho Inventory, and Microsoft SharePoint have small absolute observation counts, so their percentage movements carry more uncertainty than brands with larger bases.
• Instrument change: Cisco Webex was replaced by Cisco Webex App as a tracked entity in September. Movement between these two labels reflects a tracking change, not a direct competitive comparison.
• Qualified denominator vs raw collection: All brand-level percentages are calculated within the qualified benchmark set of 396 observations, not the 800 raw collected prompts.
• Directional analysis: Month-over-month movement identifies changes worth investigating; it does not by itself establish the cause of those changes.

Next Step

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

The aggregate percentages answer the question of who is being recommended, but they do not reveal which high-intent prompts are being won, which competitor takes the recommendation when a brand loses, what attributes AI systems associate with each option, or which external sources shape those answers. For a category where the naming shift from Cisco Webex to Cisco Webex App may mask a continuous product, those underlying patterns are the difference between understanding a tracking change and diagnosing a genuine competitive shift. The same applies to Zoom's steady erosion of top-tier placements and RingCentral's partial recovery.

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy. For Cisco, the audit would determine whether the name change alters how AI systems recommend its product and which sources drive each label. For Zoom, it would identify the specific prompts and surfaces where rank-one placements have been lost since July.

Request an AI visibility audit