Scaling Trust, Not Automation - Rethinking Quality for the AI Era | TestMu 2026
As AI reshapes how software systems are developed and delivered, most organizations still ask how to scale automation faster. In doing so they overlook a deeper challenge: AI doesn't merely automate tasks, it delegates judgment and decision-making in ways conventional systems never did.
Unlike traditional automation, which fails loudly and predictably, AI systems often fail silently — confident but incorrect outputs, context drift, and culturally misaligned responses that erode confidence over time. These failure modes demand a new approach to quality that goes beyond test coverage and accuracy metrics.
This session reframes quality engineering for AI-driven systems by treating trust as a first-class architectural concern. Attendees will learn why correctness must be defined in context, why benchmarks and pass/fail tests are insufficient, and how to design practical evaluation frameworks that scale with real-world complexity.
A clear distinction between scaling automation and scaling trust.
Understanding AI-native failure modes missed by traditional QA.
A framework for defining correctness in complex, context-dependent systems.
Practical guidance on assigning accountability for AI decisions.

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