AI is reshaping quality engineering from a downstream testing function into an intelligent, predictive capability embedded across the entire software delivery lifecycle. Yet despite 89% of organizations experimenting with AI-augmented QE workflows, only 15% have scaled enterprise-wide. Sustainable value requires the right operating model: trusted data, governed automation, and human accountability working together.

This joint point of view from Capgemini and Tricentis outlines a practical, outcome-led path from AI experimentation to measurable enterprise scale. Whether your organization is at the beginning of its AI journey or looking to move beyond pilots, this paper gives quality and technology leaders a clear framework for scaling AI responsibly and with real business outcomes, not just faster tests.

What you’ll learn:

• How AI transforms every stage of the quality engineering lifecycle and what that means for your operating model
• A five-level maturity model for progressing from automated testing to governed, AI-augmented quality engineering, with priority use cases and KPIs to measure real impact
• A practical adoption roadmap for moving from controlled pilots to enterprise scale, including how to manage key risks like AI hallucination, data privacy, and skills gaps