AI Powered BI Systems Transforming Change Management and Strategic Decision Making in Enterprises
Keywords:
Artificial Intelligence (AI), Business Intelligence (BI), Change Management, Strategic Decision-Making, Predictive Analytics, Data-Driven Insights, Organizational Agility, Stakeholder Engagement, Innovation, Sustainable Growth.Abstract
The incorporation of Advanced Intelligent Business Information Systems enhances change management and strategic decision making within the current day organization. These systems employ state-of-art technologies like machine learning, deep learning, and natural language processing and capture a large amount of data in order to deliver intelligent insights in support of swift, data-oriented, and foresighted decision making. Some key benefits are, Forecasting, what if analysis, real-time data dashboards and reports, real-time strategy with changing market trends; thus reducing the probabilities of wrong choices for an organisation’s strategic direction. In change management, AI BI systems help to identify areas of low productivity, workforce problems and other externalities in advance, thus making it possible to address them before they become problematic. The above systems help in addressing stakeholder communications and facilitating change by providing automated solutions, thus clearing out workloads of upcoming human resource challenges from human resource workers. They create an environment where adaptability and innovations are expected; they prepare enterprises for complicated environments where efficiency has to be achieved. This work aims to reveal the complex ways in which AI enhanced BI systems can support strategic acumen and sustainable business advancement. This is done through the presentation of applied research and examples of their potential for change, as well as outlining potential issues regarding their ethical application, their limitations, and how they can be used to adjust enterprise dynamics and sustain competitiveness in a globalized world.
Downloads
References
Amram, M., & Kulatilaka, N. (1999). Real Options: Managing Strategic Investment in an Uncertain World.
Boardman, A., Greenberg, D., Vining, A., & Weimer, D. (2001). Cost-Benefit Analysis: Concepts and Practice.
Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies.
Carroll, A. B., & Buchholtz, A. K. (2014). Business and Society: Ethics, Sustainability, and Stakeholder Management.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). "Business Intelligence and Analytics: From Big Data to Big Impact." MIS Quarterly.
Drucker, P. F. (1967). The Effective Executive.
Freeman, R. E. (1984). Strategic Management: A Stakeholder Approach.
Glaeser, E., Kominers, S. D., Luca, M., & Naik,
N. (2018). "Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life." Economic Inquiry.
Highsmith, J. (2001). Agile Software Development Ecosystems.
Ishikawa, K. (1982). Guide to Quality Control.
Janis, I. L. (1972). Victims of Groupthink.
Jobin, A., Ienca, M., & Vayena, E. (2019). "The Global Landscape of AI Ethics Guidelines." Nature Machine Intelligence.
Kahneman, D. (2011). Thinking, Fast and Slow.
Kaplan, S., & Garrick, B. J. (1981). "On the Quantitative Definition of Risk." Risk Analysis.
Kotter, J. P. (1996). Leading Change.
Saaty, T. L. (1980). The Analytic Hierarchy Process.
Schoemaker, P. J. (1995). "Scenario Planning: A Tool for Strategic Thinking." Sloan Management Review.
Simon, H. A. (1957). Models of Man: Social and Rational.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness.
Tversky, A., & Kahneman, D. (1974). "Judgment Under Uncertainty: Heuristics and Biases." Science.
Sudeesh Goriparthi. Leveraging AIML for advanced data governance enhancing data quality and compliance monitoring. International Journal of Data Analytics (IJDA), 2(1), 2022, pp. 1-11
Ankush Reddy Sugureddy. Enhancing data governance frameworks with AI/ML: strategies for modern enterprises. International Journal of Data Analytics (IJDA), 2(1), 2022, pp. 12-22.
Sudeesh Goriparthi. Implementing robust data governance frameworks: the role of AI/ML in ensuring data integrity and compliance. International Journal of Artificial Intelligence & Machine Learning (IJAIML), 1(1), 2022, pp. 83-91
Ankush Reddy Sugureddy. AI-driven solutions for robust data governance: A focus on generative ai applications. International Journal
of Data Analytics (IJDA), 3(1), 2023, pp. 79-89
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.