RingCentral says it is deploying ChatGPT Work and Codex across product development and operational workflows, using the tools to support planning, testing, documentation, deployment and release coordination.
RingCentral is using OpenAI’s ChatGPT Work and Codex to support software development and operational coordination across the company, according to accounts published by OpenAI and RingCentral.
The collaboration is focused on making AI tools part of day-to-day work in engineering as well as business operations. OpenAI said RingCentral is using ChatGPT Work to centralize operational intelligence, while Codex is being used to help accelerate AI product development.
In a RingCentral press release, the communications software company said its internal AI-Native Challenge generated 2,500 projects in fewer than 30 days. The company said employees used ChatGPT Work and Codex through stages including planning, implementation, testing, documentation, continuous integration and continuous delivery, and deployment.
That account indicates that RingCentral’s effort is not limited to a single product team or isolated automation project. Instead, the company is positioning the tools as support for a broad range of technical and operational tasks.
The scale of participation and the number of projects are RingCentral’s own reported figures. Neither OpenAI’s overview nor the RingCentral announcement provides a detailed breakdown of how many projects moved into production or the measured business impact of each project.
OpenAI also highlighted an example involving RingCentral’s R&D Efficiency Manager. According to OpenAI, the manager used ChatGPT Work to turn manual monthly launch checks into a repeatable workflow.
Those checks had involved reviewing release plans, Jira tasks, and go-to-market schedules. The example illustrates a use of generative AI beyond writing code: bringing information from several planning and execution systems together so teams can conduct recurring release-readiness work more consistently.
For engineering organizations, this type of use case may be as consequential as code generation. Release processes often depend on information spread across planning documents, issue trackers, product schedules, and commercial readiness materials. A repeatable AI-assisted workflow could reduce manual coordination work, though OpenAI’s account does not quantify time savings or reliability improvements for RingCentral.
RingCentral’s approach reflects a wider enterprise interest in embedding generative AI across the software lifecycle and associated business processes. Codex is being presented as a tool for technical work, while ChatGPT Work is used for knowledge-intensive coordination and operational tasks.
The available source material frames the initiative as an effort to make AI a common working capability across functions, rather than a standalone feature. The longer-term value will depend on whether organizations can convert experimentation into durable workflows with clear ownership, reliable inputs, and measurable outcomes.
For now, RingCentral and OpenAI are emphasizing adoption across planning, development, release management, and operations as the central result of their collaboration.
The collaboration is focused on making AI tools part of day to day work in engineering as well as business operations.
OpenAI said RingCentral is using ChatGPT Work to centralize operational intelligence, while Codex is being used to help accelerate AI product development.
Company wide experimentation In a RingCentral press release, the communications software company said its internal AI Native Challenge generated 2,500 projects in fewer than 30 days.
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