The Role of Machine Learning in Enhancing the Efficiency of Project Knowledge Management – A Critical Review
Abstract
The significance of knowledge in project management emerges as a critical factor in enhancing decision-making, improving processes, and bolstering adaptability to evolving challenges in modern projects. However, traditional tools employed in managing knowledge often lack effectiveness, dynamism, and the ability to address the complexities and diversity of contemporary projects. These limitations underscore the need for advanced techniques and methodologies capable of extracting insights from large datasets and transforming them into actionable knowledge that supports decision-making processes. In this context, machine learning, as a prominent branch of artificial intelligence, presents substantial potential for improving project knowledge management. By analyzing patterns and leveraging big data, machine learning enables proactive identification of future challenges and provides predictive insights to enhance administrative processes. This study aims to elucidate the effective roles that machine learning techniques can play in improving the efficiency of project knowledge management, with a focus on exploring their strengths, weaknesses, and the challenges hindering their implementation, such as a lack of technical expertise and the difficulty of integrating these technologies with traditional systems. The study offers a critical review of recent research addressing the application of machine learning in project management, highlighting its benefits and the limitations impacting its effectiveness in real-world environments. The findings emphasize that machine learning significantly enhances administrative efficiency despite the challenges associated with its adoption. The study recommends further applied research focused on developing practical solutions to enhance the efficiency of administrative processes, enabling projects to better adapt to shifting challenges and achieve their objectives with greater efficiency and sustainability.
https://doi.org/10.24897/acn.64.68.20255006
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