International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 6 November-December 2024 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.

Artificial Intelligence and Machine Learning as Business Tools: A Framework for Diagnosing Value Destruction Potential

Author(s) Md Nadil Khan, Tanvirahmedshuvo, Md Risalat Hossain Ontor, Nahid Khan, Ashequr Rahman
Country United States
Abstract The use of intelligence (AI) and machine learning (ML), in business operations is becoming more common offering efficiency, decision making and innovation. However, there are risks of losing value if these technologies are not implemented and managed properly. This document suggests a way to identify the potential for value loss in AI and ML projects within companies. By considering factors, like data quality, model reliability, ethics and organizational readiness the framework helps evaluate the risk of losing value. By using this approach organizations can. Reduce the risks associated with AI and ML projects to ensure they contribute positively to business goals. The goal of this document is to help businesses understand how AI and ML projects can create or destroy value so they can make decisions while minimizing risks.
Keywords Artificial Intelligence (AI), Machine Learning (ML), Data Quality Assessment, Ethical Considerations, Organizational Readiness, Value Destruction, Business Tools
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-02-27
Cite This Artificial Intelligence and Machine Learning as Business Tools: A Framework for Diagnosing Value Destruction Potential - Md Nadil Khan, Tanvirahmedshuvo, Md Risalat Hossain Ontor, Nahid Khan, Ashequr Rahman - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.23680
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.23680
Short DOI https://doi.org/gzwpkq

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