In the AI era, trust is becoming a scarcer and more valuable commodity than technological capability. As AI models and data pipelines become more integrated, traditional downstream quality assurance methods are insufficient. Instead, quality engineering must shift upstream into the design, development, and governance phases to ensure the integrity of AI systems, data environments, and digital engineering processes. AI
IMPACT Shifts focus from AI capability to AI trustworthiness, emphasizing the need for integrated quality engineering practices.
RANK_REASON The item is an opinion piece discussing the strategic importance of quality engineering in the context of AI adoption, rather than reporting on a specific event or release.
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