How AI Search Is Recommending VoIP Services: Monthly Trends

Executive Summary

RingCentral remains the clear coverage leader in AI search recommendations for VoIP services, holding 66.9% valid recommendation coverage in August 2026, essentially flat against July's 67.0%. The gap to the next brand, Nextiva at 63.9%, is roughly 3 points. Notably, RingCentral's rank-one rate fell 6.1 points from 38.5% in July to 32.4% in August, even as its overall presence and coverage held steady. This means AI systems are still recommending RingCentral broadly, but are less likely to name it as the single top choice.

The strongest upward mover this month was Dialpad Meetings, whose coverage rose 1.0 point from 40.2% in July to 41.2% in August. While not significant on its own, this movement extended Dialpad Meetings' lead over Grasshopper, with the gap between them widening from 3.7 points in July to 9.1 points in August. The sharpest decliner was Phone.com, which dropped 3.1 points from 5.8% in July to 2.7% in August, the only significant movement in the category this month.

The category was largely stable month over month, with nine of ten tracked brands classified as stable. The only significant movement was Phone.com's decline, which came alongside a corresponding drop in its top-three recommendation rate from 2.2% to 0.5%. Across the July to August series, RingCentral and Nextiva have held the top two positions, with Ooma a consistent third at 57.4% coverage in August.

Each monthly run begins with 800 prompt-surface observations (507 unique questions in July, 555 in August) across the benchmark's defined AI/search surface universe. Of those, 800 mentioned a tracked brand or competitor in both months; 791 were relevant and 9 were irrelevant in July, while 790 were relevant and 10 were irrelevant in August. The public metrics use the 587 qualified observations in July and 626 in August 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 benchmark-level metrics below summarize the aggregate volume and quality signals that sit beneath the brand-level figures that follow.

Benchmark-Level Metrics

The recommendation-shaped answer share declined while the valid recommendation shortlist share held steady, suggesting that while the overall volume of recommendation-shaped answers dipped, the proportion of qualifying shortlists remained consistent.

AI Recommendation Trend

Leadership Holds Steady at the Top, with a Notable Shift in Rank-One Placement

RingCentral and Nextiva continue to hold the top two positions by valid recommendation coverage, with Ooma a distant third. The competitive action in August sits below the surface: RingCentral's rank-one rate moved noticeably even as its overall coverage held, while Phone.com's coverage dropped to a new low.

No brand exceeded normal month-to-month variation on the primary coverage metric except Phone.com, whose 3.1-point decline stood out as the category's only significant movement this month; RingCentral's rank-one rate also shows a notable shift, examined in the section below.

What Changed This Month

Phone.com: Significant coverage decline

Phone.com's valid recommendation coverage fell 3.1 points from 5.8% in July to 2.7% in August, the only significant movement on the primary coverage metric this month.

In absolute terms, Phone.com earned just 17 valid recommendations in August, down from 34 in July. Its top-three recommendation rate also fell 1.7 points from 2.2% to 0.5%, meaning only 3 of 626 observations placed Phone.com in the top three. Its raw mention presence also declined, from 10.1% to 7.8%.

The distinction that matters: Phone.com was never a high-coverage brand, but August shows a compression at both the presence and recommendation levels across the board.

Highest-priority diagnostic: Which prompt types drove Phone.com's decline, and which competitor took its place in those answers?

RingCentral: A notable rank-one shift

RingCentral's overall coverage held essentially flat, down 0.1 points from 67.0% in July to 66.9% in August. Its raw mention presence also held, down 0.6 points to 93.3%. But its rank-one rate fell 6.1 points from 38.5% to 32.4%, the largest single movement recorded across any metric in the category this month.

In absolute terms, RingCentral was named the top recommendation in 203 of 626 observations in August, down from 226 of 587 in July. Its top-three rate also slipped 2.4 points to 54.3%.

The distinction a reader should notice: RingCentral remains the most recommended VoIP provider by a wide margin, but AI systems are more often placing it second or third rather than first. The brand with the most rank-one movement in the other direction is Nextiva, whose rank-one rate rose 0.8 points to 11.3%.

Highest-priority diagnostic: Which specific prompts shifted from RingCentral to another brand at rank one, and what evidence source supports the alternative?

Dialpad Meetings and Grasshopper: A widening gap

Dialpad Meetings rose 1.0 point from 40.2% in July to 41.2% in August, while Grasshopper fell 4.4 points from 36.5% to 32.1%. Neither movement was individually significant, but the combination widened the gap between the two brands from 3.7 points to 9.1 points.

Grasshopper's decline coincided with a 3.9-point drop in raw mention presence to 42.3%, meaning it appeared in fewer AI answers overall. Its top-three rate also fell 1.7 points to 8.0%, though its rank-one rate rose 0.5 points to 2.2%. Dialpad Meetings, by contrast, saw its raw mention presence edge higher to 50.5% while its valid recommendation count rose from 236 to 258.

The commercial consequence: Dialpad Meetings was already ahead of Grasshopper in July, and that lead extended further in August, leaving Dialpad Meetings clearly ahead in AI search visibility between the two brands.

Highest-priority diagnostic: What prompted the divergence, and is Grasshopper losing presence across all surfaces or concentrated in specific ones?

Buyer-Intent Interpretation

In August 2026, all 626 qualified observations fell into the Brand Recommendation cluster. The public benchmark did not capture any qualified observations in the Pricing & Value or Multi-Brand Comparison clusters. The current metrics describe which brands AI systems recommend for VoIP services, but they do not yet answer which brand wins on price, value, or head-to-head comparison. Those commercial questions require a deeper, prompt-level analysis beyond what the public benchmark exposes.

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 CiteWorks Studio AI Industry Market Discovery research program.

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

Report-Specific Interpretation Notes

• Small-count movement: Phone.com, magicJack, and GoTo Meeting operate on small absolute counts; their percentage movements should be read with that context in mind.
• Qualified denominator vs raw collection: all brand-level percentages are calculated within the 626 qualified observations, not the 800 raw prompts.
• Directional analysis: movement month over month 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.

Beneath the aggregate percentages sit specific questions: which high-intent prompts are won, which competitor takes the recommendation when a brand loses, what attributes AI systems associate with each option, and which external sources shape those answers. For RingCentral's rank-one shift, the diagnosis requires knowing which prompts moved and what evidence the AI cited for the alternative.

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy. It turns a benchmark signal into an actionable plan.

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