STEM-Driven Innovations in AI for Business Management: A Comparative Study of Traditional vs. AI-Powered Decision-Making

Abstract

Business decisions are in the process of a paradigm shift, where the heuristic of intuition is replaced by facts and science-driven knowledge, which is enabled by breakthroughs in the fields of Science, Technology, Engineering, and Mathematics (STEM). This paper examines how AI technologies, including real-time data combination, quantile regression, and multi-scale scenario simulation, transform strategic planning, forecasting, customer engagement, and supply-chain activities. In order to do the comparative analysis of the traditional and AI-powered decision-making methods, quantify the advantage of its use, define the drivers, challenges, and conscience managerial use should have. We reviewed the literature review, synthesized empirical case studies and benchmark reports, and created a four-dimension comparative framework (accuracy and predictability, efficiency and speed, scalability and adaptability, and ethical considerations). Forecast-error decreases and cost-cut per-support ticket quantitatively metric examples are examples from today's major industry sources (e.g., IMF, Gartner, Deloitte) and scholarly literature to prove the performance difference. Artificial intelligence models always provide 24-41% greater accuracy in forecasting and identifying a crisis before a traditional tool by as much as 11 weeks in advance. Latency in a decision has decreased by more than 98%, operations cost is declining by 22%, data-processing capacity is increasing 2,000 times, and market response times are rising by 41%. According to empirical cases, there is a 32 percent decrease in the interruption of the supply chain, an 18 percent increase in the accuracy of financial projections, a 35% rise in customer satisfaction, and an average asset lifespan of 19%. Nonetheless, algorithmic bias and data privacy concerns are supported by the demand to exercise an effective governance and human control system. Machine decision-making can be transformative in accuracy, swiftness, and scale; however, they need to be implemented into the hybrid models of human-AI interaction supported by AI literacy among the leaders and ethical checks and balances. Bias-reduction and governance of good practice will improve the computational input by ensuring that it complements, negating to surpass human judgment.