What changed
RingCentral’s approach shifts AI from a one off assistant into something closer to an operating layer for everyday work. The case study describes using ChatGPT Work alongside Codex to support development tasks and to bring operational intelligence into engineering and operations workflows, aiming to reduce friction between building products and running them.
Why this matters for UK business teams
If you are planning an AI rollout, the practical question is not whether AI can answer questions, it is whether it can fit into how teams already deliver work. This example focuses on how AI is used across functions, from engineering execution to operational visibility, which is often where adoption stalls.
How teams can apply the playbook
- Start by mapping where engineering work meets operations needs, then design AI support around those handoffs
- Use an AI toolchain that covers both content assisted work and code related tasks, so teams do not have to switch tools mid workflow
- Centralize operational intelligence so engineering and operations share the same working view, rather than operating on separate fragments
- Measure time saved and reduction in rework in the specific workflows you connect, for example development cycles and operational issue response
Practical test for adoption: pick one end to end workflow that crosses engineering and operations, then deploy AI support inside that workflow and track whether it shortens cycle time or reduces rework.
Next steps for your organisation
Next, identify one workflow that currently slows down because information is scattered across teams, and then define what operational intelligence should look like inside that workflow. From there, align the AI capabilities your teams need, including both work assistance and code support, and set success criteria based on productivity and operational outcomes.