How RingCentral creates AI-native operations across engineering and ops

How RingCentral Speeds Up AI Product Work with ChatGPT and Codex

Meta description: Learn how RingCentral uses ChatGPT Work and Codex to speed up AI product development and bring operational knowledge together in one place.

Tags: RingCentral, ChatGPT, Codex, AI product development, enterprise AI

Featured image: A friendly abstract illustration of interconnected nodes and chat bubbles flowing into a central hub, rendered in soft blues and oranges, with no people, logos, or text.

If you follow how large companies actually put AI to work, RingCentral is a useful example. The communications company has been using ChatGPT Work and Codex to do two things at once: move faster on AI-powered product development, and pull scattered operational knowledge into a single place that both engineering and operations teams can rely on. This article walks through what that looks like in practice, why the combination matters, and what other organizations can learn from the approach. No deep technical background is needed — if you understand the basics of AI assistants and coding tools, you will be able to follow along.

What ChatGPT Work and Codex Each Bring

ChatGPT Work and Codex play different but complementary roles in RingCentral's setup.

ChatGPT Work functions as a shared, centralized workspace. Rather than individual employees using AI tools in isolation, a work-oriented deployment lets teams collaborate around the same assistant, keep context in one place, and apply it across day-to-day tasks — drafting, summarizing, analyzing, and answering questions grounded in company knowledge.

Codex, on the other hand, is focused on software development. It helps engineers write, review, and refactor code, handle repetitive programming chores, and accelerate the build-out of new features. For a company like RingCentral, whose products are complex, cloud-based communication platforms, that kind of engineering acceleration directly affects how quickly AI features reach customers.

The pairing is deliberate: one tool broadens AI access across business functions, while the other deepens it where product code actually gets written.

Accelerating AI Product Development

The first pillar of RingCentral's approach is speed in product development. Building AI features into a communications product — things like intelligent call handling, meeting summaries, or automated workflows — requires a constant cycle of prototyping, testing, and refining. Codex supports that cycle by taking on much of the routine coding work, freeing engineers to focus on design decisions, architecture, and quality.

The practical effect is that experiments happen faster. When engineers can generate and iterate on code more quickly, the team can test more ideas in the same amount of time, discard the ones that do not work, and move promising ones forward sooner. That matters in a market where AI-driven communication features are becoming a competitive expectation rather than a differentiator.

It is worth noting a caveat: faster iteration does not automatically mean better products. Speed only pays off when it is paired with strong review practices and clear quality standards, which is where experienced engineering oversight remains essential.

Centralizing Operational Intelligence

The second pillar is arguably the more interesting one for non-engineers. Large organizations tend to accumulate operational knowledge in fragments — procedures living in one team's documents, troubleshooting steps in another's heads, and answers to common questions buried in tickets or chat threads.

By using ChatGPT Work as a central hub, RingCentral aims to bring that intelligence together. Both engineering and operations teams can query the same assistant, which means answers to "how does this process work?" or "what happened with this type of incident before?" come from a shared source rather than from whoever happens to be available. This reduces duplicated effort, shortens onboarding for new team members, and makes institutional knowledge less dependent on any single person's memory.

This kind of centralization is often what separates companies that get real value from AI tools from those that see only scattered, individual productivity gains.

Lessons for Other Organizations

RingCentral's example suggests a few transferable takeaways:

1. Pair breadth with depth. Use a general-purpose work assistant so everyone benefits, plus a specialized development tool where code is the bottleneck.
2. Centralize rather than fragment. AI value compounds when teams share the same context and knowledge base instead of each building private workflows.
3. Bridge teams. The most useful insights often come from connecting engineering knowledge with operational experience, and a shared assistant makes that connection easier.
4. Keep humans in the loop. AI-accelerated development still depends on human review, especially for customer-facing products.

Who should use this approach, and who should skip it

This combined approach fits organizations with sizable engineering teams and complex internal operations — companies where both code velocity and knowledge sharing are real pain points. It also suits businesses that already have some AI experience and want to move from scattered individual use to a coordinated rollout.

It is probably not the right fit for very small teams with simple workflows, where the overhead of setting up centralized, enterprise-grade AI tools may outweigh the benefits. Organizations without clear data governance practices should also hold off until they can define what information the tools are allowed to access.