Moving from “Can We Build It?” to “Can We Trust It?” [Testμ 2026]

At Testμ Conf 2026, QualityAI CEO Andrew Duncan argued that organizations are moving from an experimental “intelligence era” of AI pilots to a “trust era” in which scalable deployment depends on continuous evidence that systems deliver intended business outcomes within agreed guardrails. He described an expanding AI trust gap between rapidly advancing capabilities and organizations’ ability to operationalize them, recommending that leaders assess deployments through adoption, quality, and trust rather than technical performance alone. Duncan emphasized that AI assurance should be continuous and end-to-end because data, context, connected systems, and model behavior can change after release, using a hypothetical wine-pouring agent whose small overfills gradually create substantial inventory losses to illustrate the financial effects of drift. He said acceptable risk should be defined collaboratively through accountability, measurable tolerances, human-intervention triggers, and visible evidence such as monitoring dashboards and trust scores. Although governance can slow initial deployment, he argued it is necessary for responsible scaling, particularly as business users increasingly build AI-enabled applications outside traditional IT structures. Duncan also maintained that AI-driven development will increase demand for testing, assurance, automation, and enterprise-wide governance, with success measured by reliable production outcomes rather than completed pilots or implementations.

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