The AI Revolution: Transforming Business Decision-Making

Year : 2024 | Volume :15 | Issue : 02 | Page : 25-32
By

D. S. Thenmozhi,

  1. Assistant Professor (Sr), Department of Computer Science and Engineering, Government College of Engineering Erode, India

Abstract

Industries undergo a transformation thanks to artificial intelligence, which makes machines capable of activities that previously required human intelligence. This interdisciplinary field of computer science models human thought processes, impacting sectors from autonomous vehicles to creative AI tools. Integrating AI into business operations transforms decision-making and enhances corporate performance. AI-driven methodologies analyze vast datasets to provide valuable insights and facilitate decisions beyond human capability. Predictive modeling anticipates consumer behavior, market trends, and operational challenges, empowering businesses to adapt proactively. Real-time data processing enhances decision-making speed, while AI-driven optimizations streamline processes, reduce waste, and boost overall operational efficiency. This paper examines how AI drives strategic decision-making and enhances business performance across industries.

Keywords: Artificial intelligence, strategic decision-making, business performance, machine learning, big data, cognitive computing

[This article belongs to Journal of Electronic Design Technology(joedt)]

How to cite this article: D. S. Thenmozhi. The AI Revolution: Transforming Business Decision-Making. Journal of Electronic Design Technology. 2024; 15(02):25-32.
How to cite this URL: D. S. Thenmozhi. The AI Revolution: Transforming Business Decision-Making. Journal of Electronic Design Technology. 2024; 15(02):25-32. Available from: https://journals.stmjournals.com/joedt/article=2024/view=167677



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References

  1. I. Strong (2016). Applications of artificial intelligence & associated technologies. Science [ETEBMS-2016]. Mar;5(6). Available at: https://www.studocu.com/in/document/university-of-kerala/micro-economics-i/2016-applications-of-ia/61762663
  2. Mark Schwabacher and Kai Goebel (2007). A survey of artificial intelligence for prognostics. Association for the Advancement of Artificial Intelligence (AAAI) Fall Symposium – Technical Report. Available at: https://www.researchgate.net/publication/228346641_A_survey_of_artificial_intelligence_for_prognostics
  3. Yang (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), 1-29. Available at: https://www.researchgate.net/publication/351184135_Artificial_Intelligence_A_Survey_on_Evolution_and_Future_Trends
  4. Gupta , P. Mane, O.S. Rajankar, et al. (2023) Harnessing AI for Strategic Decision Making and Business Performance Optimization. International Journal of Intelligent Systems and Applications in Engineering. 11(10), 893–912. Available at: https://www.ijisae.org/index.php/IJISAE/article/view/3360
  5. Miller. (2018). AI: Augmentation, more so than automation. Asian Management Insights. 5(1), 1-20. Available at: https://ink.library.smu.edu.sg/ami/83/
  6. R. Daugherty (2018). Wilson HJ. Human+ Machine: Reimagining Work in the Age of AI: Harvard Business Press. Available at: https://store.hbr.org/product/human-machine-reimagining-work-in-the-age-of-ai/10163
  7. H. Jarrahi (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0007681318300387
  8. Agrawal, J. Gans, A. Goldfarb (2018). Prediction Machines: The simple economics of Artificial intelligence: Harvard Business Press. Available at: https://books.google.co.in/books/about/Prediction_Machines.html?id=wJY4DwAAQBAJ&redir_esc=y
  9. Ayoub, K. Payne (2016). Strategy in the age of artificial intelligence, Journal of Strategic Studies, Vol. 39 Nos. 5/6, pp. 793-819. Available at: https://doi.org/10.1080/01402390.2015.1088838
  10. Payne (2018). Artificial Intelligence: A Revolution in Strategic Affairs. Survival. 60(5) pp. 7-32. Available at: https://doi.org/10.1080/00396338.2018.1518374
  11. H. Davenport, R. Ronanki (2018). Artificial Intelligence for the Real World. Harvard Business Review, 96(1), 108-116. Available at: https://hbr.org/webinar/2018/02/artificial- intelligence-for-the-real-world
  12. Gupta , A.K. Kar , A. Baabdullah, et al. (2018) Big data with cognitive computing: A review for the future. International Journal of Information Management, 42, 78-89. Available at: https://doi.org/10.1016/j.ijinfomgt.2018.06.005
  13. M. Martínez-Rojas , M.D.C. Pardo-Ferreira, Rubio-Romero J. C. (2018) Twitter as a tool for the management and analysis of emergency situations: A systematic literature review. International Journal of Information Management, 43, 196 – 208. Available at: http://www.sciencedirect.com/science/article/pii/S0268401218303499
  14. Yeow, Adrian, Christina Soh, Rina Hansen (2018). Aligning with new Digital Strategy: A Dynamic Capabilities Approach. The Journal of Strategic Information Systems 27(1): 43–58. Available at: http://dx.doi.org/10.1016/j.jsis.2017.09.001
  15. Nishant, Rohit, Mike Kennedy, Jacqueline Corbett (2020). Artificial Intelligence for Sustainability: Challenges, Opportunities, and a Research Agenda. International Journal of Information Management, Available at: https://doi.org/10.1016/j.ijinfomgt.2020.102104
  16. Cao, Jin, Zhibin Jiang, Kangzhou Wang (2016). Customer Demand Prediction of Service-Oriented Manufacturing Incorporating Customer Satisfaction. International Journal of Production Research 54(5): 1303–1321 Available at: http://dx.doi.org/10.1080/00207543.2015.1067377
  17. Leong, Lai-Ying, Teck-SoonHew et al. (2019) A Hybrid SEM-Neural Network Analysis of Social Media Addiction. Expert Systems with Applications 133: 296–316. Available at: https://doi.org/10.1016/j.eswa.2019.05.024
  18. Abdulrahman Al-Surmi, Mahdi Bashiri, Ioannis Koliousis (2022). AI based decision making: combining strategies to improve operational performance, International Journal of Production Research, 60:14, 4464-4486 Available at: https://doi.org/10.1080/00207543.2021.1966540
  19. Sanchez, Luis & Vasile, Massimiliano & Minisci, Edmondo (2019). AI to Support Decision Making in Collision Risk Assessment. Available at: https://www.researchgate.net/publication/337032479_AI_to_Support_Decision_Making_in_Collision_Risk_Assessment
  20. Yanqing Duan, John S. Edwards, Yogesh K Dwivedi (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. Journal of Infn. Management. 48, 63-71. Available at: http://dx.doi.org/10.1016/j.ijinfomgt.2019.01.021

Regular Issue Subscription Review Article
Volume 15
Issue 02
Received July 4, 2024
Accepted July 25, 2024
Published August 17, 2024

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