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This article looks at why technology projects consistently lose focus on user outcomes, what the evidence says about the impact of user-centred delivery, and how engineering leaders can build the discipline of tying every feature to genuine user value. ",{"path":315,"title":316,"author":317,"date":318,"image":319,"customExcerpt":320,"excerpt":98},"\u002Fblog\u002Fdata-foundations-for-scalable-ai","Enterprise Data Foundations: The Determinant of AI at Scale ","Ryan Crompton","2026-04-29T13:44:21.144Z","\u002Fimg\u002Fblog\u002Fdata-foundations-for-ai.png","Most enterprise AI projects stall not because the model fails, but because the data underneath is incomplete or inaccessible. This article sets out the five dimensions of AI data readiness, examines how data debt compounds across initiatives, and explores the architectural patterns that allow organisations to scale AI.",{"path":322,"title":323,"author":98,"date":324,"image":325,"customExcerpt":326,"excerpt":98},"\u002Fblog\u002Fwhat-ai-assisted-engineering-means-for-software-testing","What AI-Assisted Engineering Means for Software Testing ","2026-04-29T13:38:42.140Z","\u002Fimg\u002Fblog\u002Fai-engineering-impact-on-testing.png","AI coding tools are now embedded in most development workflows, but AI-generated code introduces more security vulnerabilities, duplication and critical defects than human-written code. 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