Human-AI complementarity is the claim that a human supported by an AI system can outperform either alone in a decision-making process. Since its introduction in the humanAI interaction literature, it has gained traction by generalizing the reliance paradigm and by offering a more practical alternative to the contested construct of trust in AI. Yet complementarity faces key theoretical challenges: it lacks precise theoretical anchoring, it is formalized only as a post hoc indicator of relative predictive accuracy, it remains silent about other desiderata of human-AI interactions, and it abstracts away from the magnitude-cost profile of its performance gain. As a result, complementarity is difficult to obtain in empirical settings. In this work, we leverage epistemology to address these challenges by reframing complementarity within the discourse on justificatory AI. Drawing on computational reliabilism, we argue that historical instances of complementarity function as evidence that a given human-AI interaction is a reliable epistemic process for a given predictive task. Together with other reliability indicators assessing the alignment of the human-AI team with the epistemic standards and socio-technical practices, complementarity contributes to the degree of reliability of human-AI teams when generating predictions. This repositioning supports the practical reasoning of those affected by these outputs -- patients, managers, regulators, and others. Our approach suggests that the role and value of complementarity lie not in providing a stand-alone measure of relative predictive accuracy, but in helping calibrate decision-making to the reliability of AI-supported processes. We conclude by translating this repositioning into design- and governance-oriented recommendations, including a minimal reporting checklist for justificatory human-AI interactions and measures of efficient complementarity.

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