Affiliations: [a] Apeejay School of Management, New Delhi, India | [b] Amity Business School, Amity University, Gurugram, India
Correspondence:
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Corresponding author: Srirang Kumar Jha, Apeejay School of Management, New Delhi, India. E-mail: srirang.jha@learn.apeejay.edu.
Abstract: Analytic Hierarchy Process (AHP) is a unique tool which can help in improvising the usage of machine learning in attaining organizational effectiveness. True, machine learning has emerged as one of the most important tools in enhancing organizational effectiveness through improved strategic decision-making vis-à-vis key performance indicators. It has redefined the way companies can create, and measure value added, and experiences generated for the end users of their products, services, and other offerings. Machine learning algorithms are being leveraged for making more predictive and prescriptive key performance indicators which ultimately contribute towards optimizations of business processes and overall improvement in the competitiveness of the organizations. It also helps the organizations in attaining excellence in execution of strategic decisions through almost accurate predictive insights on various management functions related to HR, marketing, finance, and operations which in turn boost stakeholder satisfaction. In this study, the authors have developed an analytic hierarchy process framework based on review of 166 peer-reviewed research papers to determine how the organizations can priorities management functions and their attributes coupled with machine learning applications for higher levels of efficiencies. Insights from this article may help the practicing managers in prioritizing use of machine learning in management functions for optimizing results and improving overall organizational effectiveness.
