Customer Stories
Global Consumer Brand Turns AI into Reliable Productivity Lever
Xebia helped a global consumer brand accelerate the adoption of AI-assisted development across its engineering organization, turning AI experimentation into measurable developer productivity.
At a Glance
Challenge
Translating investment in AI tools such as GitHub Copilot into measurable productivity improvements
Solution
Embedding Developer Relations specialists into engineering teams to run structured AI experiments, coach developers in real workflows, and measure outcomes using Developer Experience Index (DXI) metrics.
Results
• 13 AI experiments across the software development lifecycle
• AI time savings improved from −24% to +23% vs industry benchmarks
• AI-generated code increased from +66% to +190% vs industry benchmarks
• Developers progressed from AI skeptics to confident AI collaborators
• Productivity improvements of up to 6 hours per developer per week
The Client
The customer is a globally recognized consumer brand known for creating imaginative, high‑quality products for children and families. With a strong heritage in creativity, play, and innovation, the organization operates worldwide and manages a highly complex digital and retail ecosystem. Its technology landscape supports large‑scale e‑commerce, digital content experiences, loyalty platforms and collaboration with partners across markets. The company places high emphasis on security, compliance, performance, and delivering seamless digital experiences that reflect its brand values.
The Challenge: Unlocking Real Value from AI Investments
The organization had invested in AI tools for developers, including access to AI assistants such as GitHub Copilot through GitHub Enterprise Cloud. The company was also measuring developer experience through the Developer Experience Index (DXI). What was missing was clarity. Leaders could not understand how AI was influencing day-to-day engineering work.
Teams were experimenting with AI tools, but each in their own way. There was no shared approach, no consistent guidance, and no reliable way to measure impact. Leaders needed clear answers:
- Which engineering workflows benefit most from AI?
- How can teams improve the way they collaborate with AI tools?
- What practices can scale across the organization?
Without these answers, AI would remain a tool people tried, not a capability the organization could rely on.
The Solution: Embedding AI Into Real Engineering Work for Measurable Outcomes
The organization partnered with Xebia for an experiment-led engagement grounded in real engineering work rather than theory. To understand how AI could meaningfully support software delivery, Xebia embedded Developer Relations specialists directly into two engineering teams responsible for identity and consent management platforms. This ensured that experimentation occurred in the real environments where developers build, maintain, and operate software.
Xebia followed a structured improvement loop to turn AI exploration into a repeatable framework grounded in evidence instead of assumptions:
- Define hypotheses - Identify where AI could enhance quality, speed, and developer experience across the SDLC.
- Run experiments - Test new AI-assisted workflows inside real product teams.
- Measure outcomes - Use DXI metrics and developer surveys to capture impact and identify improvement areas.
- Scale what works - Expand successful practices across teams and enable broader adoption.
Hands-on collaboration made the difference. In early sessions, developers worked alongside Xebia specialists, observing how AI was actually used. Small changes in prompting, workflow integration, and collaboration patterns quickly unlocked better results. In structured teams, productivity improved by up to six hours per developer per week, the equivalent of 300 hours saved annually per developer.. Across the engagement, 13 focused experiments tested how AI could support everything from coding to documentation and operations.
Beyond coding tasks, additional experiments explored how AI could support documentation, architecture design, development workflows, operations, and more. One of the most significant improvements was the introduction of an AI-enabled SDLC: a model where AI is embedded into every phase of software development. Instead of treating AI as an add-on, teams began integrating AI into planning, development, testing, deployment, and operations. This created a structured pathway for continuous improvement, allowing teams to refine AI use based on measurable results rather than assumptions.
The Results: Measurable Gains in Productivity and Confidence
By combining hands-on coaching, structured experimentation, and quantifiable outcomes, the engagement enabled the leaders to identify new opportunities, test improvements inside real teams, measure the impact, and systematically expand the practices that deliver value.
Leveraging DXI metrics through the GetDX platform, the organization was able to understand the ROI of its AI investment:
- AI improved time savings from −24% to +23% compared to industry benchmarks
- AI-generated code increased from +66% to +190% compared to industry benchmarks
- Developers shifted from cautious experimentation to confident, consistent collaboration with AI tools
The real shift was not just in metrics. It was in behavior. The experiments also demonstrated that improving documentation workflows contributed to noticeable improvements in developer experience and productivity, reinforcing the value of AI beyond code generation.
Looking Ahead
With successful experiments completed and validated, the organization now has a replicable framework to scale AI-assisted development
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