The Impact of Artificial Intelligence on Business Processes
DOI:
https://doi.org/10.47672/ejt.1488Keywords:
Artificial Intelligence, Method, Business Process, AI-Based Methods, Systematic Review, Data Mining, Data Warehouse, Decision Support System, Supply Chain Management, Quality Management SystemAbstract
Purpose: The purpose of the study is to examine the challenges faced by businesses in integrating and effectively utilizing artificial intelligence (AI) technology. It aims to provide a comprehensive understanding of how AI technologies generate business value and the anticipated benefits they offer. The study also seeks to identify the facilitators and inhibitors of AI adoption and usage, explore different types of AI use in the organizational environment, and analyze their first- and second-order impacts.
Methodology: The study employed the comprehensive literature review research design. The researchers conducted a systematic search using predefined criteria in databases such as Scopus and Web of Science. The search yielded 21 relevant papers that were analyzed and synthesized for this study. The data collection method relied on the examination of existing literature. Data analysis involved identifying key themes, trends, and insights from the selected papers. The researchers conducted a qualitative analysis to extract relevant findings and synthesized the information to derive meaningful conclusions.
Findings: The study revealed several insights regarding the integration and use of AI in businesses. This indicated that organizations struggle with understanding how AI technologies can generate value and how to effectively incorporate them into their operations. Lack of comprehensive knowledge about AI and its value generation processes was identified as a major barrier. Additionally, the study highlighted the facilitators and inhibitors of AI adoption and usage. It identified various types of AI applications in the organizational environment and explored their impacts on business operations. The findings shed light on the challenges businesses face in leveraging AI technology and suggested areas for further research.
Recommendations: To practitioners: The study emphasizes the importance of acquiring comprehensive knowledge about AI technologies and their potential value generation processes. To policy makers: The study highlights the need for supportive policies and regulations to foster AI adoption. It suggests creating an enabling environment that promotes AI research and development. Theory and Validation: The study may have been informed by existing theories related to AI adoption, organizational change, or innovation. Practice: To practitioners, the study underscores the importance of understanding the value and potential of AI technologies. Policy: To policy makers, the study emphasizes the need for policy frameworks that promote AI adoption and address associated challenges.
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References
Benbya, H.; Pachidi, S.; Jarvenpaa, S. Special Issue Editorial: Artificial Intelligence in Organizations: Implications for Information Systems Research. J. Assoc. Inf. Syst. 2021, 22, 281-303. [Google Scholar] [CrossRef]
Berente, N.; Gu, B.; Recker, J.; Santhanam, R. Managing artificial intelligence. MIS Q. 2021, 45, 1433-1450. [Google Scholar]
Bharadiya , J. P., Tzenios, N. T., & Reddy , M. (2023). Forecasting of Crop Yield using Remote Sensing Data, Agrarian Factors and Machine Learning Approaches. Journal of Engineering Research and Reports, 24(12), 29-44. https://doi.org/10.9734/jerr/2023/v24i12858
Bharadiya, J. (2023). Artificial Intelligence in Transportation Systems A Critical Review. American Journal of Computing and Engineering, 6(1), 34 - 45. https://doi.org/10.47672/ajce.1487
Bharadiya, J. . (2023). A Comprehensive Survey of Deep Learning Techniques Natural Language Processing. European Journal of Technology, 7(1), 58 - 66. https://doi.org/10.47672/ejt.1473
Bharadiya, J. . (2023). Convolutional Neural Networks for Image Classification. International Journal of Innovative Science and Research Technology, 8(5), 673 - 677. https://doi.org/10.5281/zenodo.7952031
Bharadiya, J. . (2023). Machine Learning in Cybersecurity: Techniques and Challenges. European Journal of Technology, 7(2), 1 - 14. https://doi.org/10.47672/ejt.1486
Davenport, T.H.; Prusak, L. Working Knowledge: How Organizations Manage What They Know; Harvard Business Press: Boston, MA, USA, 1998. [Google Scholar]
Diorio, S. "Realizing the Growth Potential of AI": Forbes. Available online: https://www.forbes.com/sites/forbesinsights/2020/05/08/realizing-the-growth-potential-of-ai/?sh=567d044433f3 (accessed on 8 May 2020).
dos Santos Garcia, C.; Meincheim, A.; Junior, E.R.F.; Dallagassa, M.R.; Sato, D.M.V.; Carvalho, D.R.; Santos, E.A.P.; Scalabrin, E.E. Process mining techniques and applications-A systematic mapping study. Expert Syst. Appl. 2019, 133, 260-295. [Google Scholar] [CrossRef]
Maita, A.R.C.; Martins, L.C.; Lopez Paz, C.R.; Rafferty, L.; Hung, P.C.; Peres, S.M.; Fantinato, M. A systematic mapping study of process mining. Enterp. Inf. Syst. 2018, 12, 505-549. [Google Scholar] [CrossRef]
Majhi, S.G.; Mukherjee, A.; Anand, A. Business Value of Cognitive Analytics Technology: A Dynamic Capabilities Perspective. VINE J. Inf. Knowl. Manag. Syst. 2021, 1-19. [Google Scholar] [CrossRef]
March, S.T.; Smith, G.F. Design and natural science research on information technology. Decis. Support Syst. 15, 251-266. [Google Scholar] [CrossRef]
Nallamothu, P. T., & Bharadiya, J. P. (2023). Artificial Intelligence in Orthopedics: A Concise Review. Asian Journal of Orthopaedic Research, 6(1), 17-27. Retrieved from https://journalajorr.com/index.php/AJORR/article/view/164
Offermann, P.; Blom, S.; Schönherr, M.; Bub, U. Artifact types in information systems design science-A literature review. In Proceedings of the International Conference on Design Science Research in Information Systems, St. Gallen, Switzerland, 4-5 June 2010; Springer: Berlin/Heidelberg, Germany, 2010; pp. 77-92. [Google Scholar]
Taymouri, F.; La Rosa, M.; Dumas, M.; Maggi, F.M. Business process variant analysis: Survey and classification. Knowl.-Based Syst. 2021, 211, 106557. [Google Scholar] [CrossRef]
van de Wetering, R. Achieving Digital-Driven Patient Agility in the Era of Big Data. In Responsible AI and Analytics for an Ethical and Inclusive Digitized Society; Dennehy, D., Griva, A., Pouloudi, N., Dwivedi, Y.K., Pappas, I., Mäntymäki, M., Eds.; Lecture Notes in Computer Science; Springer International Publishing: Cham, Switzerland, 2021; Volume 12896, pp. 82-93. ISBN 978-3-030-85446-1. [Google Scholar]
Van Der Aalst, W.; Van Hee, K.M.; van Hee, K. Workflow Management: Models, Methods, and Systems; MIT Press: Cambridge, MA, USA, 2004. [Google Scholar]
Wetering, R.V.D. The impact of artificial intelligence ambidexterity and strategic flexibility on operational ambidexterity. In Proceedings of the PACIS 2022 Proceedings, Taipei, Sydney, 5-9 July 2022; p. 153. Available online: https://aisel.aisnet.org/pacis2022/153 (accessed on 1 January 2023).
Yu, E.S.; Mylopoulos, J.; Lesprance, Y. Al models for business process reengineering. IEEE Expert 1996, 11, 16-23. [Google Scholar] [CrossRef][Green Version]
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