RingCentral Inc.‘s latest quarter shows a company that has quietly turned artificial intelligence from a future story into its primary engine for product differentiation, operational leverage and, increasingly, growth. What started as a unified-communications-as-a-service provider is evolving into an AI-first customer engagement platform, with RingCentral AIR and related products at the front door of every conversation.
In Q1 2026, RingCentral reported total revenue of 644 million, up about 5% year over year, with subscription revenue of 623 million, up 6% and still representing 97% of the mix. GAAP operating margin reached a record 7.8%, up from 1.7% a year ago, while non-GAAP operating margin expanded to 22.9%, up 110 basis points. Free cash flow totaled $141 million, or 21.8% of revenue, up 8% year over year. Management raised full-year guidance for revenue, margins and free cash flow to $590 million to $605 million.
The most important metric, though, wasn’t on the income statement: Annual recurring revenue from customers using at least one paid AI product is now over 10% of total ARR, has doubled year over year, and is growing double digits sequentially, with higher average revenue per unit and net retention than the rest of the base. As founder and Chief Executive Vlad Shmunis put it, “Our native AI products continue to gain traction, with ARR from customers using at least one paid AI product standing at over 10% of total ARR.”
Despite the strong numbers, the stock was trading slightly lower after-hours, although it is up 46% year to date and almost 60% over the past 12 months. After the call, I spoke with a handful of investors and equity analysts; many took profits, triggering a selloff, while others remain unsure of AI’s long-term impact on communications. Some believe the shift from people to AI agents could deflate the industry. I believe AI will drive usage of RingCentral and its peers, creating a rising tide for the industry.
This aligns with the thesis of several equity analysts. This morning, in her note to investor clients, Catharine Trebnick, an analyst at Rosenblatt Securities, wrote, “AI is now greater than 10% of ARR and has doubled year over year, with strong attach and retention; Customers using two or more AI products grew roughly 7x. Together, AIR, ACE, CEB, and RingCX are becoming a meaningful driver of revenue quality, even if they do not yet move the headline growth rate. We see this AI stack as a 2026/2027 upside driver rather than a near-term inflection.”
During its earnings call, RingCentral announced an expanded release of RingCentral AIR, its AI receptionist, featuring new capabilities that bring AI directly to the places where business-to-consumer interactions begin. AIR is now one of RingCentral’s fastest-growing AI products, with more than 11,800 paying customers and over 40% quarter-over-quarter growth.
With the release, RingCentral is wiring AI into real workflows:
Language is no longer a barrier to adoption. AIR can now autodetect and respond in the caller’s language in real time, initially supporting 10 languages, including English, Spanish, French, Italian, German and Portuguese.
The customer outcomes are the proof points. Keller Interiors cut average wait times from 12 minutes to 90 seconds across 33 locations, boosted customer satisfaction or CSAT by three points in four months, and did so without adding headcount. Maple Federal Credit Union reports a 90% reduction in hold times, less staff strain, and more time for “conversations that matter.” These are exactly the kinds of metrics business leaders and boards care about: wait time, abandonment, CSAT and cost to serve. The most basic thing businesses have to do is answer the phone, and AIR lets them answer 100% of them.
AIR is also priced to remove friction: Standalone plans start at $49 per month with 100 minutes, while existing RingEX customers can add AIR starting at 39 per month with the same minute bundle. That makes the decision feel closer to “add another line” than “launch a new contact center project.”
Underneath all of this is a broader transformation in how communications platforms are architected and monetized.
First, AI is dissolving the traditional boundaries among UCaaS, CCaaS, and CPaaS. RingCentral’s RCAI portfolio, which includes AIR/AIR Pro (front-end automation), AVA (real-time agent assist) and ACE (post-call analytics and coaching), is integrated across RingEX, RingCX and RingCentral WEM, so AI touches every phase of the conversation: before, during and after human involvement. That means the same platform that powers a sales rep’s softphone also automates an after-hours receptionist and scores calls in a regulated contact center.
Second, the point of differentiation is shifting from dial tone to AI orchestration. In the early UCaaS era, the battle was over uptime SLAs, global coverage and mobility; today, the strategic real estate is where a customer first expresses intent. RingCentral’s argument is that processing tens of billions of minutes and billions of calls and messages per year gives it a unique vantage point into that intent stream, and that AI agents like AIR and AIR Pro can now sit at that front door to triage, resolve, or route interactions in ways that weren’t economically feasible with human staff alone.
Third, AI is making communications data actionable. With ACE, more than 5,200 customers are turning raw call recordings into structured signals: sentiment, next-best actions, compliance gaps, and coaching opportunities. On the earnings call, one example cited was Cartelligent deploying AIR, AVA, and ACE together, which reduced lead abandonment to zero, connected 100% of live leads during business hours, achieved an 85% lead-to-sign-up rate, and delivered a CSAT score of 9.85/10. That kind of closed-loop pipeline, from inbound lead to conversation intelligence, was historically the domain of large, bespoke customer relationship management and analytics projects.
Importantly, RingCentral is leaning into a hybrid AI-plus-human model rather than a purely automated narrative. Shmunis was emphatic that AI will take over more and more interactions, but legal, regulatory and complexity constraints (think healthcare diagnoses or regulated financial advice) ensure a continued role for human agents. The opportunity, as he frames it, is AI before the human gets involved, AI assisting while the human is involved, and AI after the interaction to learn and improve the next one.
For RingCentral, AI is doing three things simultaneously: creating new revenue streams, increasing stickiness, and supporting margin expansion. On the revenue side, AI ARR is now material, over 10% of total ARR, and more than double what it was a year ago. According to management, customers using at least one AI product buy more, stay longer and achieve net retention above 100%. Products such as AIR, AIR Pro, ACE and the Customer Engagement Bundle (CEB) give RingCentral a clearer upsell path beyond “more seats” and into “more intelligence per interaction.”
AI is also deepening the moat. All of RingCentral’s RCAI and customer engagement products are natively built and owned rather than resold third-party tools, which matters for roadmap control, iteration speed and economics. When you combine that with a global network, omnichannel capabilities and scaled distribution through service providers like Cox Business and Spectrum Business, you get a platform that’s harder for point AI startups to displace.
Investors commonly fear that AI workloads will erode gross margins due to model and infrastructure costs. RingCentral has taken the opposite stance. The company says it is maintaining roughly the same gross margins on RCAI products by “using the right model for the right job” and by taking advantage of how quickly state-of-the-art models are commoditized or open-sourced. Combined with internal use of AI to drive its own efficiency, expanded offshoring and vendor consolidation, non-GAAP operating margins have roughly doubled over the last three to four years to the current 23% range, with a medium-term GAAP operating margin target of 20%.
For investors, RingCentral is no longer a pure “UCaaS growth at any cost” story (although it remains a Gartner MQ leader); it is a mid- to single-digit grower with strong cash-flow metrics and credible AI-driven expansion. The company is guiding to $590 million to $605 million in free cash flow this year. Management highlights free cash flow per share as a key metric and plans to use that cash to de-lever (targeting 1 billion in gross debt by the end of 2026) and return capital via buybacks and dividends.
The key debate will be whether AI-led products can eventually lift consolidated growth above today’s approximately 5% “same dance” level once COVID-era repricing and large-customer rationalization are fully behind them. With AI ARR already over 10% of the mix and growing much faster than the base, the setup is in place for AI to move the overall needle over time, especially as partners begin to scale these offerings into their own customer bases in 2027 and beyond.
In the meantime, RingCentral is an example of what an AI transition can look like in a mature software-as-a-service business: not a wholesale pivot, but a steady rewiring of the product portfolio, go-to-market motion and financial model around intelligence at every stage of the conversation.
For customers, it’s important to shed conventional thinking around communications and reimagine how every call, text and message is handled, with AI working alongside your human teams rather than replacing them. The new RingCentral AIR capabilities, from handling voice and SMS in one place to integrating with Shopify, Calendly and WhatsApp, let organizations start small (front-desk and after-hours automation, cutting wait times, reducing abandonment) and then layer on richer AI like AVA and ACE for agent assist and post-call analytics as use cases mature.
For information technology and business leaders, the practical takeaway is to pilot AI where pain is highest (missed calls, long holds, inconsistent follow-up), use measurable metrics like abandonment rate, CSAT and cost per interaction to track impact, and think in terms of a hybrid roadmap where AI is embedded before, during and after human interactions across the existing communications footprint.
Zeus Kerravala is a principal analyst at ZK Research, a division of Kerravala Consulting. He wrote this article for SiliconANGLE.
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