How RingCentral builds AI-native work from engineering to ops
With ChatGPT Work and Codex, RingCentral builds AI product features faster and centralizes operational intelligence.
With nearly three decades of innovation in business communications, RingCentral has grown into a global company generating more than $2.6 billion in annual revenue, with thousands of employees worldwide. Today, the company is extending its tradition of innovation by embracing AI-native ways of working. By giving every employee room to experiment with ChatGPT Work and Codex, RingCentral has ensured that anyone at the company, regardless of engineering experience, can build transformative products and infrastructure.
“When you put real AI tools in everyone’s hands, the whole company becomes a product organization. Every one of our products—including but not limited to our Agentic Voice AI portfolio of AIR, AVA, ACE—gets sharper as we compress the distance between an idea and a shipped feature, and that’s exactly what AI-native development lets us do.”
To encourage AI fluency across a global engineering organization, RingCentral’s Office of the CEO sponsored an AI-Native Challenge. Every participant was given ChatGPT Work and Codex, and asked to build a complete, end-to-end project—with no mandated workflow or other constraints.
More than a coding exercise, the challenge immersed employees in the full AI-native development lifecycle, from planning and implementation to testing, documentation, CI/CD, and iteration. Nearly every participant created a working repository, and thousands of employees, including non-technical staff and even executives, delivered functioning projects.
For RingCentral, the challenge is a working model of a broader strategy: using AI internally to build products faster for their customers. The company applies the same Codex-enabled approach to accelerate development of its own AI-powered product portfolio—RingCentral AI Receptionist (AIR), AI Virtual Assistant (AVA), and AI Conversation Expert (ACE)—shortening the distance between an idea and a shipped customer feature.
“The clearest lesson from the challenge was that AI-native development isn’t about replacing engineers—it’s about amplifying them. AI accelerates the entire development cycle, while humans remain in the loop, guiding product requirements, providing business context, making architectural decisions, and ensuring every outcome is tested and verified.”
—Engineering leader at RingCentral who spearheaded the project
Encouraged by initiatives like the AI-Native Challenge, non-engineering departments at RingCentral have adopted AI-native ways of working. The Program Management Office (PMO) has used ChatGPT Work to build what amounts to an operating system for program management, replacing scattered notes and chat history with AI-powered workflows for status tracking, reporting, release governance, and knowledge transfer.
“ChatGPT brings my project context together. With ChatGPT Work, I can turn that context into actions and execution.”
One application is automated status reporting: Using ChatGPT Work, the PMO team built workflows that generate notifications from issues tracked across Jira, Google Sheets, CRM systems, and other sources. It’s the difference between walking into a meeting asking what changed and walking in with blockers, owners, and actions already defined.
What started as an open invitation to experiment, with thousands of engineers building from scratch, has matured into the operational backbone of how teams like the PMO run their programs. By reducing manual coordination, ChatGPT Work enables RingCentral’s PMO to handle more projects with greater accuracy.
Across engineering and operations alike, the same pattern holds: giving employees room to experiment with AI doesn’t just build individual skills, it builds the infrastructure the company runs on.
