12 Weeks to AI Native: Reengineering an Enterprise SDLC with GenAI

Challenge

A global enterprise SaaS leader, serving more than a million practitioners and hundreds of thousands of board members across the C-suite, faced rising pressure to extend its leadership position. As the recognized AI leader in its category, it was navigating a market reshaped by AI-native challengers and customer expectations climbing fast.

The company’s engineering organization was operating against real constraints. Manual workflows across UI implementation, backend documentation, and test creation were absorbing engineering capacity that could have gone to roadmap acceleration. Some parts of the platform predated the company’s AI-native ambitions, adding modernization work to the path forward.

Meeting the moment required more than incremental productivity gains. The company needed to embed GenAI into the software development lifecycle itself, not as a set of isolated tools, but as a native engineering model. That model also had to extend beyond internal velocity. It needed to power a new generation of AI-driven product features for end users, without compromising the governance and quality standards their enterprise customers required.

The challenge was clear: reengineer the SDLC and the product simultaneously, at enterprise scale, on a timeline measured in weeks rather than quarters..

GlobalLogic helped the company go AI-native in 12 weeks, embedding GenAI across the SDLC and into the product itself to accelerate delivery, deepen customer value, and turn AI leadership from a market position into an operating model.

Value Created

GlobalLogic delivered a GenAI-first engineering model in under 12 weeks, operating across both the development lifecycle and the product itself.

A GenAI-Native Software Development Lifecycle

GlobalLogic embedded generative AI across every stage of the SDLC. AI-driven workflows converted Figma designs into production-ready React components, auto-generated API documentation inline within developer environments, and produced end-to-end test scripts directly from designs and wireframes. Legacy modernization moved through the same AI-augmented pipeline, accelerating refactoring without sacrificing engineering oversight.

AI-Native Product Capabilities for End Users

The transformation extended into the product itself. New AI-powered features compressed hours of governance work into minutes for end users, including a meeting documentation capability that produces formal records of board proceedings in under two minutes, and a compliance summarization capability that distills complex multi-document reports into reviewable briefings in under 90 seconds. Both were built to enterprise governance standards by default.

Governance and Adoption Built In

An AI adoption tracking layer gave engineering leadership real-time visibility into how AI was being used across teams, tools, and code areas. The result was a model that scaled responsibly, reinforcing governance without slowing the velocity gains the program had unlocked..