An Empirical Socio-Technical Analysis of Machine Learning in Executive Decision-Making

Abstract

In this paper, we work from the ground reality of Machine Learning in Advanced Stages of Management: Evidence from global business leaders and leading global companies. Machine Learning (ML) is often advertised as a plug-and-play solution for executive decision-making, but the reality of the corporate world as ML is much more complex. This paper goes beyond the usual industry hype and looks at how advanced predictive models work within complex corporate cultures to understand how to use machine learning in such organizations. Based on empirical data and the analysis of the most relevant data from the world's top market leaders, we examine the friction between mathematical capability and organizational reality. Our findings show that algorithmic sophistication is rarely the sole factor of project success: long-term business value depends not just on technology but also on data governance, leadership alignment, and cultural readiness. The study concludes with an integrated socio-technical framework to help executives move ML projects from isolated experiments into sustainable corporate assets.