Machine Learning in Business Analytics: Evidence from US Industry Professionals

Abstract

PURPOSE--This paper will discuss the integration of machine learning (ML) in business analytics and assess its impact on predicting organizational performance among professionals in the industry in the United States. The study examines the awareness of professionals of ML, the levels at which it is implemented in organizations, its effects on business decision-making and predictive performance, barriers to its implementation, and how trust and ethical factors can help it to be implemented successfully.

DESIGN/METHODOLOGY/APPROACH--The study design was a quantitative cross-sectional survey that used a structured questionnaire to respondents who were 275 industry professionals representing various sectors such as finance, technology, healthcare, retail, and manufacturing. The questionnaire had twelve Likert scale items that covered five constructs; Awareness and Knowledge, Implementation and Usage, Impact on Performance Prediction, Challenges and Barriers, and Trust and Ethics. The analysis of data was done with the help of IBM SPSS Statistics Version 29 with the help of reliability analysis, descriptive statistics, and Chi-square tests.

FINDINGS--The results show that machine learning is fully aware and adopted by the organization among the respondents. Machine learning can greatly enhance accuracy in predictions, speed of making business decisions, and increase the performance of an organization. The organizations mentioned a growing adoption of ML in the analytical processes with sufficient technical infrastructure. Nevertheless, low data quality and lack of competent professionals are significant impediments to implementation. Despite the general trust of respondents in the insights provided by ML, ethical issues and data privacy concerns still affect the technology acceptance and organizational adoption strategies.

ORIGINALITY/VALUE--The current research offers empirical data on the issue of machine learning adoption as perceived by the U.S. industry professionals and fits into the expanding body of research on the topic of AI-based business analytics. Its results provide organizational managers, policymakers, and technology developers with practical suggestions regarding the significance of managing the workforce, governance of data, ethical use of AI, and organizational preparedness to extract the greatest strategic value of machine learning in performance prediction and business intelligence.