Sessions
Stop Maintaining, Start Evolving: Applying AI-Native Practices to Brownfield Codebases
The "AI-Native" dream is often sold as a greenfield paradise: starting from scratch with perfect prompts and zero technical debt. But for most engineers, reality is a mature "brownfield" estate, a highly successful product, with a complex codebase and a build pipeline held together by hope.
In this session we re-examine AI Native Engineering practices as the engine for architectural reclamation.
Drawing on real-world experience modernising complex legacy systems, I will demonstrate how to apply the Strangler Fig Pattern in an AI-native context. We will walk through the practical workflow of identifying high-risk areas in your codebase, using AI document functionality and creating test suites for undocumented code, and isolating legacy logic behind facades to facilitate a clean rewrite.
Stop using AI to add to your technical debt. Learn how to use it to pay it down.
About
Katie is a Technical Director and AI-Native Engineering Specialist with 20+ years of experience in large-scale software delivery and organisational transformation.
Her work focuses on the strategic evolution of engineering practices, moving beyond AI experimentation to enable teams to adopt AI-native ways of working at scale. Katie specialises in the socio-technical challenges of integrating AI-assisted workflows into mature, brownfield environments, where navigating legacy architecture, delivery risk, and operational stability is paramount.
She partners with senior stakeholders to introduce these practices pragmatically, focusing on measurable improvements. Her approach prioritises engineering excellence and sustainable change that enhances existing enterprise platforms.