Customer Stories
Global Research and Analytics Leader Adopts AI at Scale to Drive Productivity Gains
Xebia helped a global analytics leader turn AI experimentation into measurable performance gains.
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At a Glance
Challenge
Inconsistent AI adoption and a lack of measurable evidence of productivity impact limited enterprise adoption.
Solution
Structured Proof of Value program combining AI enablement, hands-on training, and baseline metrics to measure GitHub Copilot’s impact across global teams.
Results
Data-backed validation of AI’s impact on engineering velocity and quality, enabling confident enterprise-wide adoption.
The Client
The client is a global market research leader with operations across 100 countries, delivering customer intelligence and insights that help global enterprises shape business strategies and drive growth.
The Challenge: Unanswered Questions Limiting AI Adoption
The organization had begun experimenting with AI-assisted development, making GitHub Copilot available to parts of the engineering workforce. Not everybody was excited about adopting AI tools and the leadership faced a familiar enterprise dilemma: early signals were promising, but inconsistent usage and anecdotal feedback were not enough to justify scaling AI across the company. The questions were straightforward but consequential. Would AI materially improve engineering velocity? Would it enhance code quality? Could those improvements be measured objectively and consistently across global teams? Without credible evidence, AI would remain an experiment rather than evolving into a strategic capability.
The Solution: Measured AI Enablement at Global Scale
Xebia partnered with the organization to design and execute a Proof of Value program that focused not only on tooling and adoption but on measurable impact. We trained hundreds of developers across the US, Europe, and India to collaborate effectively with AI and embed AI into their daily workflows. Baseline metrics were also established to objectively measure impact on productivity, quality, and deployment behavior.
Xebia delivered a structured enablement program centered around GitHub Copilot, including:
- Prompt engineering sessions to improve AI interaction
- Hands-on bootcamps focused on real development scenarios
- Office hours and working sessions to support teams in their own codebases
To support a globally distributed workforce, sessions were delivered across the US, Europe, and India, training hundreds of developers within a few months.
The Results: Proven Gains in Speed and Quality
The program moved AI from an experiment to a proven productivity lever, backed by data. With increased adoption across the organization:
- Engineering activity accelerated significantly across teams
- Test coverage improved, contributing to higher code quality
- Redeployments in lower environments were reduced, lowering friction and rework
Internal analysis identified tens of thousands of hours in annualized engineering time savings, with a potential to increase with broader AI adoption.
Looking Ahead
The Proof of Value has set the foundation for how engineering teams work moving forward, accelerating innovation while ensuring governance and quality. With clear evidence of value, the organization is now expanding AI-assisted development across the enterprise in a structured, measurable way.
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