The Role of Machine Learning Adoption in Enterprises: Literature Review and European Insights

Machine learning (ML) represents one of the most transformative technologies shaping modern business environments. Its ability to process large volumes of data and generate predictive insights offers new opportunities for data-driven decision making and strategic management. However, despite its growing potential and benefits, the actual adoption of ML in enterprises remains relatively limited. The aim of this paper is to explore the current state of ML adoption across enterprises in the European Union and to synthesize existing research on how ML is applied in different business functions. Using secondary data from Eurostat and a structured literature review, the study identifies key areas of implementation, major benefits, and persistent barriers that influence the adoption and effective use of ML in organizations. Findings suggest that while ML enhances accuracy, efficiency, and strategic agility, its diffusion in European enterprises is still constrained by data related challenges, implementation costs, and shortages of skilled professionals. The paper underscores that closing widening cross-country gaps and moving beyond pilot stages will require stronger data pipelines and stewardship, practical model explainability, and change management to scale ML across enterprises, especially in lagging sectors such as manufacturing and transportation.