Case Study
How UST boosted developer productivity by 25% with GitHub Copilot
Inside the GitHub Copilot rollout that transformed software delivery for a global telecommunications leader, without disrupting the teams it was built to help.
OUR CLIENT
This multinational telecommunications provider serves more than 100 million customers across North America and Europe. The company operates in a highly competitive market where rapid software delivery, digital innovation, and operational scalability directly influence customer experience and business growth.
THE CHALLENGE
The telecommunications company identified AI-assisted software engineering as a strategic requirement for sustaining innovation at scale. To accelerate enterprise adoption, the organization mandated GitHub Copilot for internal teams and external delivery partners.
The telco’s enterprise-wide mandate created significant organizational pressure, but the real business risk was not deployment itself. It was the possibility of a shallow rollout where adoption appeared successful on paper, while meaningful productivity gains failed to materialize.
Without rapid adoption of AI-assisted development workflows, the organization risked falling behind on innovation speed, operational efficiency, and software delivery competitiveness.
THE TRANSFORMATION
UST's approach was built on a single premise: AI adoption only creates value when it changes how work actually gets done, not just which tools are available.
The engagement focused on embedding GitHub Copilot directly into the workflows where engineering time was being lost: automated coding tasks, unit test generation, and the repetitive overhead that slows delivery without adding quality. The result was faster feature releases without sacrificing code standards, and engineering teams that could sustain higher output without additional headcount.
The transformation extended beyond engineering. GitHub Copilot was leveraged across reporting and documentation functions, reducing the manual effort that typically falls to technical teams after code ships and enabling support teams to resolve issues and generate accurate technical information faster than before.
What made the difference was treating AI adoption as an engineering discipline rather than a vendor initiative. Scalable, consistent workflows were established from the start, which meant the productivity gains didn't stay in the pilot; they compounded as adoption spread enterprise-wide.
The engagement shifted GitHub Copilot from an experiment to an embedded capability. AI-assisted development became the default, not the exception.
THE IMPACT
Enterprise AI tool rollouts routinely stall at partial adoption. Industry data consistently shows most plateau well below 50% as initial enthusiasm gives way to workflow friction. Achieving 95% adoption across a global delivery account, within months, represents an outcome that most enterprises treat as aspirational rather than achievable.
Most enterprises install AI tools. Fewer operationalize them. The difference between a tool that sits in the stack and one that changes how engineering teams work every day is what this engagement was built to deliver.
Ready to make AI-assisted development the default across your engineering teams?
Explore how UST helps enterprises operationalize GitHub Copilot and AI-assisted workflows at scale, from pilot to enterprise-wide adoption.