Customer Stories
How a Leading North American Airline Boosted Engineering Throughput and Quality with AI-Powered SDLC on AWS
Xebia partnered with a top North American airline to embed generative AI across the software development lifecycle (SDLC), delivering measurable productivity and quality gains through its Value Realization Framework (VRF).
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A Leading Global Airline
A major North American airline operating in a high-volume, safety-critical environment where software reliability, speed, and innovation directly impact customer experience and operational excellence.
Challenge
Fragmented practices and inconsistent automation across the SDLC caused uneven delivery speed, quality, and visibility.
WHY
Solution
An AI-powered transformation on AWS embedding GenAI into every phase of the SDLC under VRF governance.
WHAT
Results
~20% productivity improvement
~30% error-rate reduction
80%+ adoption across target teams
HOW
Fragmented SDLC Slowed Delivery
The airline struggled with inconsistent engineering practices and tool sprawl across design, development, testing, and operations. This created inefficiencies, uneven delivery velocity, and inconsistent quality. The business needed a standardized, measurable approach to accelerate releases while maintaining high reliability.
An AI-Powered SDLC Transformation
Xebia applied its Value Realization Framework (VRF)—anchored on Speed, Quality, Reliability, Cost, and Productivity—to baseline KPIs, prioritize interventions, and track measurable benefits.
The phases of the project included:
- Use-Case Catalog: Identified 49 AI-assisted use cases across requirements, design, UX, development, CI/CD, testing, and operations, mapped to engineering personas (BA/TPE, Architects, UX, SDE, DevOps, SDET, SRE).
- Phased Implementation: Deployed AI for story generation, architecture patterns, code generation, CI/CD optimization, test automation, and SRE tasks like alert triage, root cause analysis, and FinOps automation.
- Operating Model: Adopted a blended delivery model (managed services + T&M) with domain-level and joint governance, supported by real-time performance dashboards.
- Adoption & Change Management: Focused on persona-specific value (time saved, coverage gains, satisfaction) measured against cost/benefit targets.
Measurable Engineering Gains
By combining KPI baselines, a prioritized backlog, and disciplined governance, the airline achieved sustainable productivity and quality improvements.
- ~20% improvement in engineering productivity
- ~30% reduction in error rates
- 80%+ adoption across targeted teams and domains
- 49 AI-assisted use cases identified, with ~22–23 implemented and ~27 in progress
Looking Ahead
The airline is scaling adoption across additional SDLC phases, expanding AI-driven automation, and refining KPIs to strengthen long-term resilience and maintain a competitive edge.
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