RingCentral, the business communications company generating over $2.6 billion in annual revenue, has adopted an AI-native approach that gives employees across the organization access to ChatGPT Work and Codex. The company ran an internal AI-Native Challenge where engineers and non-technical staff built end-to-end projects, compressing the time from idea to shipped feature. The initiative has since spread from engineering into operations, with the Program Management Office using ChatGPT Work to turn scattered notes into coordinated workflows for project tracking, reporting, and release governance.
Nearly every participant in the challenge created a working repository, and thousands of employees - including non-technical staff and executives - delivered functioning projects. The company now applies the same Codex-enabled approach to accelerate development of its own AI product portfolio, which includes RingCentral AI Receptionist (AIR), AI Virtual Assistant (AVA), and AI Conversation Expert (ACE).
Engineering teams using AI for full project cycles
The AI-Native Challenge, sponsored by RingCentral's Office of the CEO, gave participants ChatGPT Work and Codex with no mandated workflow. The goal was to immerse employees in the full AI-native development lifecycle - planning, implementation, testing, documentation, CI/CD, and iteration.
"The clearest lesson from the challenge was that AI-native development isn't about replacing engineers - it's about amplifying them," said an engineering leader at RingCentral who spearheaded the project. "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."
President and COO Kira Makagon said the effect extends across the company. "When you put real AI tools in everyone's hands, the whole company becomes a product organization," she said. "Every one of our products 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."
PMO operations run on AI tools
Since the challenge, non-engineering departments have adopted similar workflows. RingCentral's Program Management Office uses ChatGPT Work as 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. The PMO team built workflows that generate automated status updates across Jira, Google Sheets, CRM systems, and other data sources.
Vaneet Seth, Senior Manager, R&D Efficiency at the PMO, described it as the difference between walking into a meeting asking what changed and walking in with blockers, owners, and actions already defined. "ChatGPT brings my project context together," Seth said. "With ChatGPT Work, I can turn that context into actions and execution."
By reducing manual coordination, the PMO handles more projects with greater accuracy.
What this means for IT and development teams
For IT and development professionals, the article - based on RingCentral's experience - shows that AI-native workflows can be embedded across both engineering and operations. AI for IT & Development teams can expect the same pattern: giving employees room to experiment with AI doesn't just build individual skills, it builds the operational infrastructure a company runs on. The in-house AI challenge served as a working model of a broader strategy - using ChatGPT internally to build customer-facing products faster.
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