Authors :
Mahmoud Amjed Mohammad Alameiri; Ahmad Khamees Ibrahim Al-Betar
Volume/Issue :
Volume 10 - 2025, Issue 12 - December
Google Scholar :
https://tinyurl.com/5n995jby
Scribd :
https://tinyurl.com/mfj8thfm
DOI :
https://doi.org/10.38124/ijisrt/25dec642
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 30 to 40 days to display the article.
Abstract :
This study investigates the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML)
within digital ecosystems, focusing on their operational, strategic, and economic implications. It explores AI/ML-driven
architectures, autonomous decision-making, and predictive intelligence, highlighting how these technologies improve
efficiency, enhance decision-making, and create competitive advantage. The research identifies a dual-effect dynamic: while
AI/ML adoption enhances organizational performance and value creation, it introduces challenges such as data governance,
system complexity, and model transparency. A strategic framework is proposed for leveraging AI/ML benefits while
mitigating associated risks, enabling organizations to innovate responsibly and maintain resilience in complex digital
environments.
References :
- Russell, S., & Norvig, P. Artificial Intelligence: A Modern Approach. Pearson, 2021.
- Jordan, M. I., & Mitchell, T. M. “Machine learning: Trends, perspectives, and prospects.” Science, 2015.
- Goodfellow, I., Bengio, Y., & Courville, A. Deep Learning. MIT Press, 2016.
- Gartner, “Artificial Intelligence and Machine Learning: Strategic Trends,” Gartner Research, 2023.
- McKinsey & Company, “The State of AI in 2024,” McKinsey Report, 2024.
- ITU, “AI for Good: Global Impact Report,” International Telecommunication Union, 2023.
- IEEE, “AI Systems Design and Governance Standards,” IEEE Publications, 2022.
This study investigates the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML)
within digital ecosystems, focusing on their operational, strategic, and economic implications. It explores AI/ML-driven
architectures, autonomous decision-making, and predictive intelligence, highlighting how these technologies improve
efficiency, enhance decision-making, and create competitive advantage. The research identifies a dual-effect dynamic: while
AI/ML adoption enhances organizational performance and value creation, it introduces challenges such as data governance,
system complexity, and model transparency. A strategic framework is proposed for leveraging AI/ML benefits while
mitigating associated risks, enabling organizations to innovate responsibly and maintain resilience in complex digital
environments.