The Role of Machine Learning in Predictive Business Management
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Abstract
The integration of machine learning (ML) tools into business processes has significantly transformed decision-making and predictive management in organizations. This research explores the relationship between ML adoption and predictive business outcomes, focusing on key factors such as data quality, managerial support, employee competency, and technological infrastructure. Using a survey of 250 respondents from various industries, the study tests five hypotheses concerning the role of ML in decision-making processes. Findings reveal that higher data quality and availability, along with effective managerial support, significantly enhance the effectiveness of predictive decision-making. Additionally, employee competency in ML tools positively impacts business performance, and technological infrastructure plays a critical role in the success of ML-driven management practices. The study concludes by discussing the implications of these findings for organizations looking to adopt ML solutions, providing recommendations for fostering a supportive environment that includes training, infrastructure, and leadership commitment to drive success. Future research may explore the long-term effects of ML integration in diverse sectors and its impact on organizational culture and employee engagement.
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