AI has made it easier than ever to build testing agents, frameworks and automation toolchains. Teams can rapidly combine open-source tools, custom scripts and AI agents to create solutions tailored to their immediate needs.

But what happens when those solutions need to scale across an enterprise?

What begins as speed and flexibility can quickly evolve into fragmented frameworks, inconsistent standards, brittle integrations and an increasing maintenance burden. As more teams build independently, organisations often find themselves supporting multiple approaches, duplicating effort and struggling to maintain governance, compliance and long-term sustainability.

This session explores the realities of DIY enterprise testing in the age of AI and the trade-offs between building and adopting a platform approach. We'll examine how standardisation, scalability and built-in intelligence can help organisations shift their focus from maintaining infrastructure to delivering quality at scale.

More than a discussion about tools, this session offers a practical perspective on how architects and test leaders should approach build versus buy in an AI-first world. Because while AI makes building easier, enterprise success depends on what happens after the first deployment.