The Impact of AI on Organizational Change in Digital Transformation
Keywords:
artificial intelligence, organizational changeAbstract
Digital revolution introduces artificial intelligence into many spheres. This paper exposed how artificial intelligence influences the architecture, practices, decision-making, and culture of firms undergoing digital transformation. The report claims that data-driven artificial intelligence projects could increase output, adaptability, and corporate efficiency. Artificial intelligence's autonomous decision-making, predictive modeling, and real-time data processing need a rethink of strategy. Changing their culture, staff, and capabilities to meet ethical, data governance, and regulatory rules, AI-powered digital transformation companies adapt.
This research exposes the paradigm shift in artificial intelligence decision-making. Artificial intelligence led data-based judgments are replacing hierarchies. Artificial intelligence helps all business decision-makers to respond faster to changing customer wants and market conditions by means of machine learning and data analytics. This streamlines decision-making and distributes authority, therefore boosting corporate flexibility. According to the study, change caused by artificial intelligence shapes employee duties. Artificial intelligence integration might boost organizational efficiency and creativity by means of reskilling and upgrading of staff members.
References
J. McCarthy, "The AI revolution: A new era of technological advancement," Communications of the ACM, vol. 62, no. 4, pp. 34-36, Apr. 2019.
J. Manyika et al., "AI, automation, and the future of work: The impact on jobs, skills, and wages," McKinsey Global Institute, Tech. Rep. 2017.
H. J. Kim and H. K. Kim, "Digital transformation through AI: Organizational change and implications," Journal of Business Research, vol. 120, pp. 103-115, Jan. 2020.
A. M. DeNero, "Machine learning in organizations: Strategic implications," Harvard Business Review, vol. 97, no. 2, pp. 88-96, Mar. 2019.
B. W. Boehm and L. Chen, "Agile software development: The transformation of software engineering," IEEE Software, vol. 35, no. 6, pp. 50-56, Nov.-Dec. 2018.
Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.
S. Kumari, “AI-Powered Cloud Security for Agile Transformation: Leveraging Machine Learning for Threat Detection and Automated Incident Response ”, Distrib Learn Broad Appl Sci Res, vol. 6, pp. 467–488, Oct. 2020
Zhu, Yue, and Johnathan Crowell. "Systematic Review of Advancing Machine Learning Through Cross-Domain Analysis of Unlabeled Data." Journal of Science & Technology 4.1 (2023): 136-155.
Tamanampudi, Venkata Mohit. "Leveraging Machine Learning for Dynamic Resource Allocation in DevOps: A Scalable Approach to Managing Microservices Architectures." Journal of Science & Technology 1.1 (2020): 709-748.
S. Kumari, “Kanban and Agile for AI-Powered Product Management in Cloud-Native Platforms: Improving Workflow Efficiency Through Machine Learning-Driven Decision Support Systems”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 867–885, Aug. 2019
A. G. I. Mohammed, S. Al-Sharhan, and S. K. Wong, "Human-AI collaboration: Redefining the role of humans in AI-driven organizations," Computers in Human Behavior, vol. 117, pp. 106-120, Feb. 2021.
R. B. R. Duflo and S. D. Smith, "Data-driven culture: How organizations can foster a data-centric mindset," Business Horizons, vol. 63, no. 4, pp. 513-522, July-Aug. 2020.
M. C. K. Zhang, "Ethical considerations in AI: Bias, accountability, and transparency," AI & Society, vol. 35, no. 3, pp. 555-564, Sept. 2020.
R. Kaplan and D. Norton, "The balanced scorecard: Translating strategy into action," Harvard Business Review Press, 1996.
S. R. Quarles, "AI governance: Best practices for ethical and compliant AI systems," AI & Ethics, vol. 2, no. 2, pp. 177-186, June 2021.
J. R. Dyer, "Digital transformation strategies: Balancing innovation and tradition," Strategic Management Journal, vol. 40, no. 9, pp. 1397-1423, Sept. 2019.
A. B. D. K. Hsu, "AI and workforce dynamics: Opportunities for reskilling and upskilling," Journal of Organizational Behavior, vol. 42, no. 8, pp. 1098-1114, Dec. 2021.
L. C. A. Ferreira, "Challenges of AI integration: Barriers and strategies for overcoming resistance," Journal of Technology Management, vol. 14, no. 1, pp. 45-60, Mar. 2020.
T. P. Wu, "Future directions in AI and organizational change: Emerging trends and predictions," International Journal of Information Management, vol. 57, pp. 102-117, Oct. 2021.
D. M. Schoemaker and R. E. B. Weller, "Decision-making in AI-enhanced organizations: A new paradigm," European Management Journal, vol. 38, no. 5, pp. 786-797, Oct. 2020.
S. T. Yu and C. H. Liang, "The impact of AI on organizational culture and employee engagement," Journal of Business Research, vol. 116, pp. 314-322, July 2020.
A. R. Gupta, "AI-driven digital transformation in healthcare: Benefits and challenges," Health Information Science and Systems, vol. 8, no. 1, pp. 1-10, Dec. 2020.
V. M. G. Reyes, "Machine learning as a catalyst for digital transformation," IEEE Transactions on Engineering Management, vol. 68, no. 3, pp. 617-626, Aug. 2021.
J. L. Davis and R. L. Wright, "Organizational change management in the era of AI," Journal of Organizational Change Management, vol. 33, no. 6, pp. 913-927, Nov. 2020.
P. J. R. De Silva and J. J. Jones, "Artificial intelligence in strategic decision-making: A systematic literature review," Strategic Management Review, vol. 3, no. 1, pp. 35-52, Jan. 2022.
