The integration of Artificial Intelligence (AI) into professional practice is reshaping how knowledge is created, validated, and transmitted within organisations. While AI-driven decision support systems offer considerable analytical power, many rely on deep learning architectures that operate as black boxes, producing outputs that are difficult for human users to understand or critically evaluate. Explainable Artificial Intelligence (XAI) has emerged as a response to this opacity, yet its role has been framed predominantly in terms of technical transparency and user trust, underexploring its potential as a lever for professional development and organisational learning. This paper makes a theoretical-methodological contribution by introducing the XAI-OLL (Explainable Artificial Intelligence as an Organisational Learning Lever) framework, which reconceptualises XAI-generated explanations not merely as transparency mechanisms but as cognitive and organisational artefacts capable of activating processes of professional upskilling and knowledge integration. The framework proposes a sequential cognitive mechanism in which exposure to XAI explanations stimulates reflective reasoning — the deliberate comparison between algorithmic outputs and one’s own judgment — which in turn fosters interpretive competence, defined as the capacity to critically engage with AI-generated evidence. This pathway is conditioned by explanation quality: imperfect or misleading explanations can attenuate or reverse the effect on calibrated trust. Interpretive competence enables calibrated trust, a proportionate form of reliance grounded in evidence quality rather than automation deference. Calibrated trust, in turn, facilitates the integration of AI-derived insights into shared professional knowledge, supporting organisational learning at the collective level. The XAI-OLL framework generates five testable propositions, operationalised through a pilot quasi-experimental protocol in AI-supported histopathology decision-making. The paper contributes to knowledge management, organisational learning, and HR transformation literature by positioning AI interpretive literacy — the competence to engage critically with XAI outputs — as a new dimension of professional capability in AI-augmented workplaces. Practical implications address HR professionals as active designers of XAI-enabled upskilling pathways, governance conditions, and organisational learning infrastructures.