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<p>This study examines the barriers and enablers of knowledge sharing (KS) in machine learning (ML) teams through grounded theory analysis. The findings emphasize the role of organizational culture: environments that promote openness, risk-taking, and teamwork foster adaptive KS. To strengthen KS, the study recommends formalized practices such as standardized procedures, cross-disciplinary communication, and continuous learning, offering actionable strategies to build cohesive, high-performing ML teams.</p>

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Integrating minds: adaptive knowledge sharing strategies for ML team synergy

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Giulio Toscani

摘要

This study examines the barriers and enablers of knowledge sharing (KS) in machine learning (ML) teams through grounded theory analysis. The findings emphasize the role of organizational culture: environments that promote openness, risk-taking, and teamwork foster adaptive KS. To strengthen KS, the study recommends formalized practices such as standardized procedures, cross-disciplinary communication, and continuous learning, offering actionable strategies to build cohesive, high-performing ML teams.