Machine learning for enterprises: Applications, algorithm selection, and challenges

By In Lee, Yongjae Shin. 2020

308 citations recorded by the source corpus.

Abstract Machine learning holds great promise for lowering product and service costs, speeding up business processes, and serving customers better. It is recognized as one of the most important application areas in this era of unprecedented technological development, and its adoption is gaining momentum across almost all industries. In view of this, we offer a brief discussion of categories of machine learning and then present three types of machine-learning usage at enterprises. We then discuss the trade-off between the accuracy and interpretability of machine-learning algorithms, a crucial consideration in selecting the right algorithm for the task at hand. We next outline three cases of machine-learning development in financial services. Finally, we discuss challenges all managers must confront in deploying machine-learning applications.

Subfields: Artificial Intelligence, Information Systems, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/8e97557329ed03cce27231f1d55aee559118c591

Citations in the corpus

• Six Human-Centered Artificial Intelligence Grand Challenges (2023, unknown): “…include the human-AI system ’ s interaction at work, at home, and at leisure, as well as the implications of the potential for AI technology to redefine humanity as we know it (Frischmann & Selinger, 2018; Karwowski, 2018, Aug 26; Karwowski & Zhang, 2021; Lee & Shin, 2020; Sawyer et al., 2021).”