Building an Enterprise AI Quality Engineering Capability

Artificial Intelligence is transforming every stage of the software development lifecycle—from AI-powered applications such as chatbots, Retrieval-Augmented Generation (RAG), and agentic AI, to AI-generated software engineering artifacts including requirements, source code, test cases, and automation. As AI becomes embedded throughout the SDLC, traditional software testing alone is no longer sufficient. Organizations must evolve from validating application functionality to establishing an enterprise AI Quality Engineering capability that measures AI quality, trustworthiness, governance, and operational readiness. In this session, we'll share our practical approach to building an enterprise AI Quality Engineering Framework. We'll discuss how we're defining objective evaluation criteria, quality metrics, and maturity models to evaluate both AI-powered applications. We'll also explore how SQA and AI Development can partner to establish scalable evaluation processes, compare commercial and open-source AI evaluation solutions, and build an enterprise capability that supports trusted AI, continuous quality, governance, and release readiness throughout the software development lifecycle.