AI-Driven Business Analytics for Sustainable Decision-Making in U.S. Organizations: Integrating the Technology Acceptance Model and Sustainability Theory

This study looks at how next-generation business analytics powered by artificial intelligence (AI) are disrupting sustainability decision-making in the modern US economy. We investigate how AI technologies like machine learning, predictive analytics, and data mining enhance organizational efficiency, transparency, and long-term environmental/economic sustainability, drawing on the Sustainability Theory and the Technology Acceptance Model (TAM). The study employs mixed methodologies, integrating qualitative insights from technology, manufacturing, and financial firms with quantitative evidence from corporate sustainability reporting and financial performance data. While the qualitative results provide insight into emerging trends, implementation challenges, and management perceptions, the quantitative analysis shows how AI adoption is linked to various sustainability performance metrics. The results show that AI-enhanced analytics support ethical governance, resource efficiency, and strategic foresight, all of which lead to improved sustainability performance. However, the study draws attention to issues with algorithmic bias and data privacy, as well as the cost of integrating AI. The study offers a more encompassing perspective on the ethical deployment of AI for decision-making, which significantly adds to the body of knowledge on digital transformation and sustainable practices. It provides insightful guidance for researchers, policymakers, and corporate executives who want to strike that crucial balance between innovation, competitiveness, and sustainability for the US economy in the future.

Artificial Intelligence, Business Analytics, Sustainable Decision Making, U.S. Economy, Technology Acceptance Model

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