Many organizations adopt AI coding tools expecting significant productivity improvements, but often see limited results because coding is only one part of software delivery. Challenges such as unclear requirements, fragmented knowledge, manual testing, approval of bottlenecks, and disconnected workflows can slow delivery regardless of how quickly code is written. According to Gartner, 80% of developers now use AI coding tools, yet the impact on overall software delivery performance has often fallen short of expectations. Research from Faros AI found that while AI increased individual task completion by 21%, broader organizational delivery metrics remained largely unchanged. Looking ahead, Gartner predicts that 90% of enterprises will integrate AI across multiple phases of the SDLC by 2029, making it even more critical to address the factors limiting ROI today. A key challenge is trust. The DORA report found that 30% of developers do not trust AI-generated code, creating a hidden "verification tax

Organizations can improve software delivery by creating stronger connections between the various stages of SDLC—requirements, architecture, development, testing, deployment, and operations. Combining AI-powered workflows, automation, engineering best practices, and end-to-end visibility helps teams reduce delays, minimize rework, and improve collaboration. This leads to faster delivery cycles, greater predictability, and more consistent business outcomes.

A connected software engineering system improves software delivery by reducing context loss, automating repetitive tasks, and ensuring alignment between business goals and engineering execution. With better visibility across the software lifecycle, teams can 

Engineering leaders measure the impact of AI using a combination of delivery, quality, and business metrics. Common indicators include deployment frequency, lead time changes, cycle time, developer productivity, defect rates, test coverage, system reliability, and customer satisfaction. The most successful organizations focus on end-to-end outcomes, evaluating how AI improves overall software delivery performance rather than measuring code generation speed alone.

AI-Native Software Engineering is a modern software development approach that embeds artificial intelligence throughout the entire software delivery lifecycle. Rather than using AI solely for code generation, it applies AI to requirements analysis, architecture design, development, testing, deployment, and operations. This helps organizations accelerate delivery, improve software quality, reduce manual effort, and create more predictable engineering outcomes.

The return on investment varies by organization, but AI-Native digital engineering typically delivers value through faster delivery cycles, improved developer productivity, reduced rework, better software quality, and more efficient modernization efforts. By optimizing the entire engineering lifecycle, organizations can achieve measurable improvements in both operational efficiency and business outcomes.

Xebia Ace is Xebia’s AI-Native digital engineering framework for accelerating software delivery and modernization across the full SDLC. It embeds AI into every stage of delivery, from requirements and architecture to development, testing, and deployment, helping teams move faster, improve traceability, and deliver better outcomes.

While tools like GitHub Copilot focus primarily on assisting developers with code generation, Xebia Ace extends AI across the entire software development lifecycle. It connects requirements, architecture, development, testing, deployment, and governance into a unified engineering approach. This helps organizations achieve end-to-end improvements in software delivery rather than productivity gains limited to coding activities.

Yes. Xebia Ace helps organizations modernize legacy applications by using AI to analyze existing systems, identify technical debt, document business logic, and support migration planning. By automating key aspects of modernization, teams can reduce risk, accelerate transformation initiatives, and improve the maintainability of critical business systems.

Xebia Ace incorporates quality and security throughout the software development lifecycle. It helps teams align requirements with implementation, automate testing activities, enforce engineering standards, and integrate security and quality checks into CI/CD pipelines. This approach enables organizations to identify issues earlier, reduce defects, and maintain consistent software quality at scale.

Yes. Xebia Ace is designed to integrate with existing development environments, cloud platforms, DevOps pipelines, and engineering tools. Organizations can adopt AI-native engineering practices without replacing their current technology investments, making it easier to accelerate transformation while leveraging existing workflows and platforms.

Xebia Ace is designed for organizations looking to improve software delivery performance, accelerate digital transformation, and scale AI adoption across engineering teams. It is particularly valuable for technology leaders, engineering managers, product teams, and enterprises seeking to modernize applications, increase development efficiency, and deliver higher-quality software faster.