Background: Artificial intelligence (AI) is transforming work at a rapid pace, yet empirical evidence on its consequences for employee wellbeing remains mixed. Existing research on attitudes toward AI, AI-related stress, and AI-enabled work design does not explain why ostensibly similar forms of work with AI are experienced as developmental in some settings and psychologically destabilizing in others. Methods: This is a conceptual article that develops theory through narrative integration of established psychological frameworks, following recognized approaches for designing conceptual contributions. The model was built by synthesizing Conservation of Resources theory, job crafting and proactive work reconfiguration research, person-environment fit theory, and emotional labor research, and by deriving falsifiable propositions from this synthesis. Manuscript drafting and literature organization were supported by a large language model (AI) under full author direction, verification, and final approval of all content. Results: The article introduces psychological alignment as the mechanism linking AI at work to employee wellbeing: the degree of coherence among how employees do, understand, and feel their work with AI (horizontal alignment) and between individual experience and team or organizational norms (vertical alignment), analyzed across individual, team, and organizational levels. Drawing on Conservation of Resources theory, alignment is proposed to preserve psychological resources and support wellbeing, whereas misalignment accelerates resource loss. The model specifies employment type, AI type, and structural power asymmetries as boundary conditions that determine where this mechanism operates most strongly. Four propositions describe curvilinear effects of misalignment on adaptive capacity, the moderating role of vertical alignment, curvilinear effects of the pace of AI-related change, and the distinct risks of counterfeit alignment (compliance without genuine coherence). Conclusions: Psychological alignment offers an integrative account of when AI-related change supports versus undermines employee wellbeing, provides a research agenda centered on measuring alignment and its boundary conditions, and outlines practical implications for AI implementation and workforce wellbeing.