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Artificial Intelligence for Business: A Conceptual Review

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  • Isaac Onyeyirichukwu Chukwuma,
  • Fidelis Odinakachukwu Alaefule,
  • Ifeanyi Leo Madu,
  • Anthonia Nneka Egbosionu,
  • Matthew Arinze Okeke,
  • Patrick Chukwunwike Chukwuma
Isaac Onyeyirichukwu Chukwuma
University of Port Harcourt
Fidelis Odinakachukwu Alaefule
University of Nigeria
Ifeanyi Leo Madu
Gregory University
Anthonia Nneka Egbosionu
University of Nigeria
Matthew Arinze Okeke
Madonna University
Patrick Chukwunwike Chukwuma
University of Illinois
Published in : International Journal of Scientific Research and Management (IJSRM) , Vol. 12 No. 10 (2024)

Submission to VIJ 2024-10-03

Keywords

  • Artificial Intelligence,
  • Resource-based view theory

Copyright (c) 2024 Isaac Onyeyirichukwu Chukwuma, Fidelis Odinakachukwu Alaefule, Ifeanyi Leo Madu, Anthonia Nneka Egbosionu, Matthew Arinze Okeke, Patrick Chukwunwike Chukwuma

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This conceptual review examined the dynamics of artificial intelligence (AI) in the business landscape. As businesses traverse an increasingly complex industrial landscape, AI provides a strategic tool for improving effectiveness, innovation, and decision-making. This study aimed to examine and provide a comprehensive view of AI’s influence on business. This paper deployed a qualitative research approach; utilising a narrative literature review methodology to examine existing literature which provided a relevant comprehensive perspective of AI in businesses. The study specifically examined themes on the antecedents of AI for business, challenges of AI for business, dual role (necessity or advantage) of AI in organization operations, AI effect on business/organizational interest, and the resource-based view (RBV) theory perspective on AI for business. The study is of the position that businesses can position themselves for success in the rapidly evolving AI-driven environment by optimizing AI integration and deployment as a strategic approach to future market relevance.

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