Digital pharmacy systems increasingly make medication tasks visible as data fields, alerts, rankings, recommendations, and documented actions. Yet these representations can conceal the work required to make digital outputs clinically meaningful, including reconstructing incomplete context, repairing medication information, interpreting uncertainty, negotiating feasible care, and retaining responsibility for decisions and follow-up. This conceptual practice article proposes the Hidden Work Preservation Framework to explain how artificial intelligence may support, remove, shift, create, delay, or obscure such work. The framework distinguishes visible digital task completion from five interdependent practice domains: context-recovery, information-repair, interpretive, relational, and responsibility-bearing work. It further proposes a preservation loop through which patient-specific context is recovered, information is repaired, outputs are interpreted with patients and care teams, decisions are made or escalated, ownership and rationale are documented, and consequences are monitored. Evaluation should therefore extend beyond model discrimination or task time to context fidelity, repair burden, calibrated reliance, relational continuity, responsibility traceability, medication safety, equity, workflow redistribution, and organizational sustainability. These domains require direct observation, workflow mapping, record and incident review, patient and staff inquiry, subgroup analysis, and prospective evaluation in the intended setting. The framework is not a validated clinical algorithm, staffing model, regulatory standard, or autonomous decision pathway. Its original contribution is to make preservation of context, judgment, and responsibility an explicit design and evaluation problem for AI-supported pharmacy care.