RingCentral shifts focus to AI as core business driver
RingCentral Inc. has moved artificial intelligence from a future product roadmap into its primary engine for growth and profitability. The company reported Q1 2026 revenue of $644 million, up 5% year over year, but the more significant metric sits below the headline numbers: annual recurring revenue from customers using at least one paid AI product now exceeds 10% of total ARR, has doubled in a year, and grows faster than the rest of the business.
The company's AI products-primarily RingCentral AIR (an AI receptionist), AVA (agent assist), and ACE (call analytics)-now command higher net retention and average revenue per customer than traditional offerings. Customers using two or more AI products grew roughly sevenfold year over year.
AIR gains traction with customers and pricing designed for adoption
RingCentral AIR, the AI receptionist, is the fastest-growing product in the portfolio. It now has more than 11,800 paying customers and grew 40% quarter over quarter. The company expanded AIR's capabilities to handle voice and text in a single interface, integrate with Shopify, Calendly and WhatsApp, and autodetect and respond in 10 languages.
The pricing removes friction. Standalone plans start at $49 per month with 100 minutes. Existing customers can add AIR for $39 per month. That positions the decision as an incremental purchase rather than a platform migration.
Customer results show measurable impact. Keller Interiors cut average wait times from 12 minutes to 90 seconds across 33 locations and raised customer satisfaction by three points in four months without adding staff. Maple Federal Credit Union reports a 90% reduction in hold times.
AI dissolves traditional software boundaries
RingCentral's AI products are dissolving the traditional lines between unified communications, contact center, and communications platform services. The same AI that powers a sales rep's phone can automate after-hours reception and analyze calls in a regulated contact center.
The strategic advantage has shifted from uptime and coverage to where customers first express intent. RingCentral processes tens of billions of minutes annually and argues that scale gives it unique insight into intent patterns. AI agents can now sit at the front door of those interactions to triage, resolve, or route them in ways that weren't economically feasible with human staff.
One customer example: Cartelligent deployed AIR, AVA, and ACE together and achieved zero lead abandonment, connected 100% of live leads during business hours, and achieved an 85% lead-to-sign-up rate.
AI margins differ from traditional software fears
Investors typically worry that AI workloads erode gross margins due to model and infrastructure costs. RingCentral has taken the opposite path. The company maintains similar gross margins on AI products by "using the right model for the right job" and taking advantage of how quickly state-of-the-art models become commoditized or open-sourced.
Non-GAAP operating margins expanded to 22.9%, up 110 basis points year over year. GAAP operating margin reached a record 7.8%, up from 1.7% a year ago. Free cash flow totaled $141 million, or 21.8% of revenue.
Management raised full-year revenue guidance to $590 million to $605 million and targets a GAAP operating margin of 20% over the medium term.
The hybrid model: AI before, during, and after
RingCentral is not pushing a narrative of full automation. The company emphasizes a hybrid approach where AI handles interactions before a human gets involved, assists while a human is engaged, and analyzes the conversation afterward.
Legal, regulatory, and complexity constraints-such as healthcare diagnoses or regulated financial advice-ensure continued roles for human agents. The opportunity, as the company frames it, is making those human interactions more efficient and higher-value.
For PR and communications professionals, this shift matters. AI for PR & Communications now touches every phase of customer interaction. Understanding how AI handles customer engagement and support directly impacts how organizations manage their brand voice and customer relationships.
What this means for communications leaders
Organizations should start by identifying where pain is highest: missed calls, long holds, inconsistent follow-up. Pilot AI in those areas and measure impact using abandonment rate, customer satisfaction, and cost per interaction.
The practical roadmap embeds AI before, during, and after human interactions across existing communications infrastructure. Front-desk and after-hours automation can start immediately. Richer AI for agent assist and post-call analytics layer in as use cases mature.
For teams managing customer communications, AI for Customer Support is no longer optional infrastructure. It's becoming the default way organizations answer phones, respond to messages, and analyze customer interactions at scale.
Updated May 8, 2026
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