Business Intelligence in the Age of AI: Evaluating Machine Learning's Impact on U.S. Economic Productivity

Abstract

Background: The integration of machine learning (ML) with business intelligence (BI) has transformed the ways in which organizations make decisions as well as how organizations streamline their operations and increase the level of productivity. As companies keep exploiting AI-based solutions, it is essential to understand how to learn the strategic contribution of the technologies on the productivity of the United States economy. The paper will explain the role of ML on business intelligence and how it influences efficiency in organizations and national economic development.

Objectives: The strategic value of machine learning in business intelligence will be analyzed using the study in terms of its implications on decision-making, cost saving and productivity. It will also be directed to defining the most significant barriers and impediments to the implementation of ML and to understand the opportunities of the further evolution of the sphere.

Methods: The data collection on 400 professionals in various sectors including healthcare, finance, technology, and manufacturing were collected using the quantitative research design and the structured questionnaire data collection tool. These values were perception of awareness, adoption, impact, challenges, and future perspectives in business intelligence and were considered in this survey. Their statistical analysis involved descriptive analysis, Pearson correlation, regression analysis, and one-way ANOVA in order to assess the occurrence of relationships among significant factors.

Results: The results indicate that the attitude towards the impact of machine learning on business intelligence is generally positive, and the awareness, adoption and the perceived impact have high levels of interrelations. The perceived impact of ML was more in bigger organizations than in small organizations. The challenges that were also named in the study were the high costs of implementation, the necessity to hire qualified personnel, and the issue of data security and ethical concerns. Nevertheless, the respondents remained hopeful about the future of ML in BI, and they believed that it would experience more adoption and more productivity.

Conclusion: Business intelligence touted by machine learning has huge potentials to increase productivity and decision making in U.S. economy. Nonetheless, issues affecting cost, expertise, and ethical issues must be resolved to enable wider adoption particularly to small and medium-sized businesses. The results indicate that the potential of ML can be tapped by creating awareness, managing obstacles, and developing a favorable perspective regarding the progress of the matter.