AI-Driven Decision Making in Management

Authors

DOI:

https://doi.org/10.70301/CONF.SBS-JABR.2024.1/1.1

Keywords:

Artificial Intelligence, transformation, complex management, quick business decision, timelines, increase productivity, cost reduction, business success, augmentation, accuracy, ethical and social concern, data protection, transparency, bias, Government regulations.

Abstract

Several Organizations are directing Artificial Intelligence (AI) driven expertise to assist and analyze the data-insights, gaps and transform their decision-making proficiency, especially when the pressure is high with minimal timelines. This research article investigates the impact of AI in decision making and its consequences for individuals, company, and society. Decision making is an important stage in effective accomplishment of any organization to reach the defined organization goals. Accurate data, reports and decisions can improve the prediction of business, transform the business strategies, implement and review the mid stage implementation responses on the reports, take quick decisions which increases the productivity and lead business to the roadmap of success and augmentation in future. This article shall also examine how AI transforms internal operations, starting from different departments like transport to consultation management. How it predicts demands, fluctuations, adjusts supply and data levels by prioritizing or optimizing the results, which noticeably reduces operating costs for the companies. Unlike information or data loaded solves the issue by AI software, which allows companies to make data-driven choices, improve customer targeting, and enhance development, along with assisting in customizing data and report at convenience to take speedy and precise strategic decisions. This article provides an overview of AI mechanisms like automation efficiency, complex management which includes handling multilayered problems that would overpower human policymakers, succession planning incorporation, business decision which assist in risk monitoring and assessment, compliance and scam recognition. This can be done in various departments like Human Resources, Accounts & Finance, Sales and Marketing etc. and in Industries like Healthcare, Finance, Consulting Business, Transportation, Food & Beverage, also in Government authorities’ defining and implementation processes in automation. There are several technologies and tools around the world which facilitate finding, developing, analyzing, and retaining the data, report, or topic which drives the decision-making process for the management. This article not only highlights the optimistic impact of Artificial Intelligence in decision-making process in management operations and a company’s success, but also how incorrect decision making could lead to disappointments for an organization. As AI remains to improve business decision-making, there are some challenges which must be addressed in the placement of AI in decision making which includes ethical concerns, AI algorithms, and social allegations, why and how Human-AI association is required. Data privacy protection, transparency and accountability, explainability issues in the AI driven decision-making process, these are the important factors for company’s reputation, and it is crucial to address. Companies must spotlight ethical AI methods and ensure transparency, including nonbiased fair decision-making algorithms. This article focusses on the importance of governance regulations and policies to facilitate biases and safeguard that AI systems are making decisions aligning with the organization's aims and goals along with opening and highlighting the scope of improvement in AI functionality.

References

Duan, Edwards & Dwivedi. (2019, October). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 63-71. Retrieved from ScienceDirect.

Favour Oluwadamilare Usman et al. (2024). A CRITICAL REVIEW OF AI-DRIVEN STRATEGIES FOR ENTREPRENEURIAL SUCCESS. International Journal of Management & Entrepreneurship Research, 201-202.

IBM. (2019, November 28). What is explainable AI? Retrieved from IBM: https://www.ibm.com/topics/explainable-ai#:~:text=What%20is%20explainable%20AI%3F,expected%20impact%20and%20potential%20biases.

Kanungo, A. (2023, July 18). The Green Dilemma: Can AI Fulfil Its Potential Without Harming the Environment? Retrieved from EARTH.ORG: https://earth.org/the-green-dilemma-can-ai-fulfil-its-potential-without-harming-the-environment/

Muhammad Eid BALBAA, M. S. (2024). THE IMPACT OF ARTIFICIAL INTELLIGENCE IN DECISION MAKING: A COMPREHENSIVE REVIEW. EPRA International Journal of Economics, Business and Management Studies (EBMS), 28-37.

Nauri Hicham, N. H. (2023). Strategic Framework for Leveraging Artificial Intelligence in Future Marketing Decision-Making. ResearchGate, 141-145.

Roser, M. (2022, December 6). The brief history of artificial intelligence: the world has changed fast — what might be next? Retrieved from Our World Data: https://ourworldindata.org/brief-history-of-ai#article-licence

Simon Kaggwa et al. (2023). AI in Decision Making: Transforming Business Strategies. INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION, 423-427.

Additional Files

Published

14.01.2026

Issue

Section

SBS International Research Conference 2024

How to Cite

AI-Driven Decision Making in Management. (2026). SBS Journal of Applied Business Research, 7-12. https://doi.org/10.70301/CONF.SBS-JABR.2024.1/1.1

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