AI-Driven Business Analytics for Economic Growth Leveraging Machine Learning and MIS for Data-Driven Decision-Making in the U.S. Economy

Urmi Haldar; Gazi Touhidul Alam; Habiba Rahman; Md Alamgir Miah; Partha Chakraborty; Abu Saleh Muhammad Saimon; Md Аsikur Rаhmаn Chy; Kazi Bushra Siddiqa; Mia Md Tofayel Gonee Manik

doi:10.63332/joph.v5i4.1178

Authors

Urmi Haldar Department of Management, Glasgow Caledonian University, UK
Gazi Touhidul Alam College of Graduate and Professional Studies, Trine University, Detroit, Michigan, USA
Habiba Rahman School of Business, International American University, Los Angeles, CA 90010, USA
Md Alamgir Miah School of Business, International American University, Los Angeles, CA 90010, USA
Partha Chakraborty School of Business, International American University, Los Angeles, CA 90010, USA
Abu Saleh Muhammad Saimon Department of Information Technology, Washington University of Science and Technology, Alexandria VA 22314, USA
Md Аsikur Rаhmаn Chy School of Business, International American University, Los Angeles, CA 90010, USA
Kazi Bushra Siddiqa School of Business, International American University, Los Angeles, CA 90010, USA
Mia Md Tofayel Gonee Manik College of Business, Westcliff University, Irvine, CA 92614, USA

DOI:

https://doi.org/10.63332/joph.v5i4.1178

Keywords:

Artificial Intelligence, Machine Learning, Business Analytics, Management Information Systems, Economic Growth, Data-Driven Decision-Making, Business Intelligence, AI-Driven Strategies

Abstract

Business analytics has undergone significant transformation because of artificial intelligence and machine learning evolution, which now fulfills a critical function in economic expansion and organizational decision-making operations. Organization within the United States economy utilizes Management Information Systems to gain AI-powered insights, which enhance productivity and optimize resources and market prediction accuracy. The research evaluates how AI analytics drive economic growth because they enhance predictions while reducing possible hazards and generate strategic decisions through data-based approaches. The research uses both quantitative data methods together with qualitative case studies to investigate the subject. A combination of secondary market data, business reports, and economic statistics undergoes ML algorithm analysis, which reveals economic patterns and correlations because of their impact on performance. The research obtains practical information and usage barriers from both business managers and policymakers by conducting structured interviews regarding their implementation experiences with AI-based economic decision systems. The paper examines three significant AI methods, including predictive analytics with natural language processing and deep learning, which are applied to business intelligence. Voluntary business analytics, which run on artificial intelligence systems, boost decision-making by generating instant analytic information and simplifying complicated economic analysis tasks. Organizations that integrate AI and MIS systems to build data-based strategies boost operational performance while gaining competitive markets and ensuring durable economic expansion. Organizations should resolve the integration challenges together with data privacy concerns and ethical issues. The research findings demonstrate why organizations need to adopt AI-based analytics systems for developing business resilience and economic innovation in the United States.