Technology-enabled work redesign can improve efficiency while unintentionally removing the work through which employees build judgement and procedural fluency. This article examines that risk through Singapore's Model AI Governance Framework for Agentic AI, recent evidence on AI-exposed work, and an anonymised Singapore SME consulting case involving automation of manual operations and finance data-entry activities. The case shows why technology adoption should be coupled with task analysis, role redesign and employee transition support. Building on the case and evidence, the brief introduces the CARE Model for capability-preserving work redesign: identify capability-critical tasks, make explicit allocation decisions between people and technology, retain or replace developmental exposure, and evaluate retained capability. The model positions workplace learning as part of automation and job-redesign architecture rather than a training response added after work has been removed.
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