Managing Digital Transformation: The Role of Artificial Intelligence and Reciprocal Symmetry in Business

Authors

  • Deng Ying Lecturer, Jiujiang Vocational and Technical College, Jiujiang, Jiangxi, China
  • Bhavik Patel PCB Design Engineer, Innovative Electronics Corporation, 750 Trumbull Dr, Pittsburgh, PA 15205, USA
  • Niravkumar Dhameliya PLC Programmer, Innovative Electronics Corporation, 750 Trumbull Dr, Pittsburgh, PA 15205, USA

DOI:

https://doi.org/10.18034/ra.v5i3.659

Keywords:

Digital Transformation, Artificial Intelligence (AI), Business Strategy, Reciprocal Symmetry, Technology Adoption, Disruptive Technologies, Strategic Management

Abstract

This study aims to understand better how corporate organizations may manage digital transformation by utilizing reciprocal symmetry and artificial intelligence (AI). The study aims to investigate the effects of artificial intelligence (AI) technologies on conventional business models, assess the possibilities and difficulties of attaining reciprocal symmetry in the context of digital transformation, and pinpoint methods for efficient AI integration that maintain organizational preparedness and alignment. Using a secondary data-based review methodology, the study synthesizes previous research on digital transformation, AI integration, organizational adaptation, and extant literature. Key conclusions show how crucial it is to have a culture of innovation, collaborate across functional boundaries, and plan strategically to maximize the advantages of digital transformation projects and achieve reciprocal symmetry. The policy implications underscore the necessity of allocating resources towards digital infrastructure, skills enhancement, and regulatory frameworks to facilitate the responsible adoption of AI and tackle obstacles such as limited resources, skills disparity, and cultural opposition. Organizations may handle technology upheavals and promote competitiveness and sustainable growth in the digital era by adopting reciprocal symmetry and supportive policies.

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References

Chen, M. H. (2017). The Analysis of Model for Electronic Commerce - Artificial Intelligence. Journal of Asian Business Strategy, 7(2), 39-43. https://doi.org/10.18488/journal.1006/2017.7.2/1006.2.39.43

Huang, T. H., Leu, Y. H. (2014). A Mutual Fund Investment Method Using Fruit Fly Optimization Algorithm and Neural Network. Applied Mechanics and Materials, 571-572, 318-325. https://doi.org/10.4028/www.scientific.net/AMM.571-572.318

Liu, F., Shi, Y., Liu, Y. (2017). Intelligence Quotient and Intelligence Grade of Artificial Intelligence. Annals of Data Science, 4(2), 179-191. https://doi.org/10.1007/s40745-017-0109-0

Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., Kim, C. (2017). Machine Learning in Materials Informatics: Recent Applications and Prospects. NPJ Computational Materials, 3, 1-13. https://doi.org/10.1038/s41524-017-0056-5

Tang, V., Yanine, F., Valenzuela, L. (2016). Data, Information, Knowledge, and Intelligence. International Journal of Innovation Science, 8(3), 199-216. https://doi.org/10.1108/IJIS-07-2016-0022

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Published

31-12-2017

How to Cite

Ying, D., Patel, B., & Dhameliya, N. (2017). Managing Digital Transformation: The Role of Artificial Intelligence and Reciprocal Symmetry in Business. ABC Research Alert, 5(3), 67-77. https://doi.org/10.18034/ra.v5i3.659