Improving Market Competitiveness using the Use of Artificial Intelligence in Strategic Business Decisions

Mohammad Shoeb Abdullah^1*, Khadiza Tasnim¹, Md Zillul Karim², Reduanul Hasan¹

Artificial Intelligence (AI) driven immersive strategic decision-making enhances the competitiveness of the organization and entire industries, accurate, personalized, and innovative decision making.

Abstract

Background: The global business landscape is continuing to change quickly, and strategic decision-making is taking a turn toward the use of artificial intelligence (AI) as an agent of change. In effort to understand the barriers companies are facing in this area, and the strategic approach companies are seeking, a working paper examines the state of deployment of AI technologies in business functions and their role in contributing to competitiveness in the market. AI enables predictive analytics, customer personalization, supply chain optimization and operational efficiency. Methods: Based on global case studies, empirical data, and contemporary literature, we explore the measurable value that AI brings in terms of a 25–30% improvement in decision-making speed and accuracy, a 10–20% increase in customer engagement, and a 15% reduction in operational costs. The most important AI technologies explored in the report include machine learning, natural language processing, robotic process automation and decision intelligence systems. The research also addresses ethical issues and governance structures exposed by the responsible adoption and organization of AI. Results: The study found that organizations adopting AI, systemically and strategically, have a competitive advantage over other organizations as AI was increasingly used to influence real-time decision making through data. Conclusion: The study concluded with the finding that while AI adoption can benefit providers, to truly consider the positives of AI adoption a few factors must be addressed in terms of strategic fit, data quality, and organization readiness.

Keywords: Artificial Intelligence (AI), Strategic Decision-Making, Market Competitiveness, Machine Learning, Digital Transformation.

References

Aharoni, Y., Tihanyi, L., & Connelly, B. L. (2010). Managerial decision-making in international business: A forty-five-year retrospective. Journal of World Business, 46(2), 135–142. https://doi.org/10.1016/j.jwb.2010.05.001

Khan, M. A., Khan, H., Omer, M. F., Ullah, I., & Yasir, M. (2024). Impact of artificial intelligence on the global economy and technology advancements. In Advanced technologies and societal change (pp. 147–180). https://doi.org/10.1007/978-981-97-3222-7_7

Ramya, J., Yerraguravagari, S. S., Gaikwad, S., & Gupta, R. K. (2024). AI and Machine Learning in Predictive Analytics: Revolutionizing Business Strategies through Big Data Insights. Library of Progress-Library Science, Information Technology & Computer, 44(3).

Hadi, H. M., & Zeebaree, S. R. (2025). Data-Driven Decision Making for E-Business Success: A Review. Asian Journal of Research in Computer Science, 18(4), 209-236.

Vuohelainen, R. (2024). Artificial intelligence (AI) and robotic process automation (RPA) in the insurance business. Theseus. https://www.theseus.fi/handle/10024/876761

Krishna, P. M. S. S., Kumar, T. P., Reddy, R. V. T., & Ariwa, E. (2024). Customer-Centric AI Solutions. In Minds Unveiled (pp. 58-69). Productivity Press.

Potla, R. T., & Pottla, V. K. (2024). AI-powered personalization in Salesforce: Enhancing customer engagement through machine learning models. Valley International Journal Digital Library, 1388-1420.

Nicolaidou, A. I. (2024). AI in audit and dealmaking?: a guide tailored for PwC Cyprus. Theseus. https://www.theseus.fi/handle/10024/866534

Shen, Q. (2024). AI-driven financial risk management systems: Enhancing predictive capabilities and operational efficiency. Applied and Computational Engineering, 69(1), 134–139. https://doi.org/10.54254/2755-2721/69/20241494

Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019). Artificial intelligence and the implementation challenge. Journal of Medical Internet Research, 21(7), e13659. https://doi.org/10.2196/13659

Md Habibur Rahman, Tanjila Islam, Mohammad Hamid Hasan Amjad, Md Shihab Sadik Shovon, Md. Estehad Chowdhury, Md Rahatul Ashakin, Bayazid Hossain, Proshanta Kumar Bhowmik, Md Nurullah, Atiqur Rahman Sunny (2024). "Impact of Internet of Things (IoT) on Healthcare in Transforming Patient Care and Overcoming Operational Challenges", Journal of Angiotherapy, 8(11),1-8,10041. https://doi.org/10.25163/angiotherapy.81110041

Achumie, G. O., Oyegbade, I. K., Igwe, A. N., Ofodile, O. C., & Azubuike, C. (2022). AI-driven predictive analytics model for strategic business development and market growth in competitive industries. J Bus Innov Technol Res.

Fuchs, C., & Schreier, M. (2010). Customer empowerment in new product development*. Journal of Product Innovation Management, 28(1), 17–32. https://doi.org/10.1111/j.1540-5885.2010.00778.x

Rahman, M. H., Biswash, M. A. R., Debnath, A., Siddique, M. A. B., Rahman, M. M., Rabbi, M. M. H., Mou, M. A. (2025). "The Future of AI in Laboratory Medicine: Advancing Diagnostics, Personalization, and Healthcare Innovation", Journal of Primeasia, 6(1),1-6,10151. https://doi.org/10.25163/primeasia.6110151

Inavolu, S. M. (2024). Exploring AI-driven customer service: Evolution, architectures, opportunities, challenges and future directions. International Journal of Engineering and Advanced Technology, 13(3), 156-163.

Rane, N., Choudhary, S., & Rane, J. (2024). Artificial Intelligence and Machine Learning in Business Intelligence, Finance, and E-commerce: a Review. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4843988

Shil, S. K., Islam, M. R., & Pant, L. (2024). Optimizing US supply chains with AI: reducing costs and improving efficiency. International Journal of Advanced Engineering Technologies and Innovations, 2(1), 223-247.

Ononiwu, N. M. I., Onwuzulike, N. O. C., & Shitu, N. K. (2024). The role of digital business transformation in enhancing organizational agility. World Journal of Advanced Research and Reviews, 23(3), 285–308. https://doi.org/10.30574/wjarr.2024.23.3.2670

Bergin, T. (2018). An Introduction to Data analysis?: Quantitative, Qualitative and Mixed methods. SAGE Publications Ltd - Torrossa. https://www.torrossa.com/en/resources/an/5019397

Gupta, S. (2017). Ethical Issues in Designing Internet-Based Research: Recommendations for Good practice. https://eric.ed.gov/?id=EJ1174008

Seykora, A., Coleman, C., Rosenfeld, S. J., Bierer, B. E., & Lynch, H. F. (2021). Steps toward a System of IRB Precedent: Piloting Approaches to Summarizing IRB Decisions for Future Use. Ethics & Human Research, 43(6), 2–18. https://doi.org/10.1002/eahr.500106

Gorr, A. Q. (2022). Identification of Factors Affecting Dairy Sire Semen Production and Optimization of the AI Supply Chain. The University of Wisconsin-Madison.

Naslednikov, M. (2024). The impact of artificial intelligence on customer relationship management (CRM) strategies. Theseus. https://www.theseus.fi/handle/10024/858753

Rahman, M. H., Aunni, S. A. A., Ahmed, B., Rahman, M. M., Shabuj, M. M. H., Das, D. C., Akter, M. S., Numan, A. A. (2024). "Artificial intelligence for Improved Diagnosis and Treatment of Bacterial Infections", Microbial Bioactives, 7(1),1-18,10036.. https://doi.org/10.25163/microbbioacts.7110036

Nalini, R. (2024). Transformative power of artificial Intelligence in decision-making, automation, and customer engagement. In Complex AI Dynamics and Interactions in Management (pp. 189-208). IGI Global.

George, A. S. (2024). AI-Enabled Intelligent Manufacturing: a path to increased productivity, quality, and insights. puirp.com. https://doi.org/10.5281/zenodo.13338085

Chui, M., Hall, B., Singla, A., Sukharevsky, A., & Yee, L. (2023, August 1). The state of AI in 2023: Generative AI’s breakout year. McKinsey & Company. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year

Amosu, O. R., Kumar, P., Ogunsuji, Y. M., Oni, S., & Faworaja, O. (2024). AI-driven demand forecasting: Enhancing inventory management and customer satisfaction. World Journal of Advanced Research and Reviews, 23(2), 100-110.

Lee, H., Zhou, J., & Hsu, P. (2015). The role of innovation in inventory turnover performance. Decision Support Systems, 76, 35–44. https://doi.org/10.1016/j.dss.2015.02.010

Alam, M., Islam, M. R., & Shil, S. K. (2023). AI-Based predictive maintenance for US manufacturing: reducing downtime and increasing productivity. International Journal of Advanced Engineering Technologies and Innovations, 1(01), 541-567.

Rahman, M. H., Biswash, M. A. R., Siddique, M. A. B., Rahman, M. M., Mou, M. A., Debnath, A., Fatin, M. (2025). "Significance of Artificial intelligence in clinical and genomic diagnostics", Journal of Precision Biosciences, 7(1),1-14,10149.https://doi.org/10.25163/biosciences.7110149

Machireddy, J. R. (2022). Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI. Hong Kong Journal of AI and Medicine, 2(1), 10-36.

Singh, S. K. (2024). Automating Routine Tasks to Improve. Improving Entrepreneurial Processes Through Advanced AI, 99.

Gurick, M. (2024). Learning technology professors’ experiences with National Science Foundation (NSF) external grant funding in higher education. Pepperdine University.

Rahman, M. H., Islam, T., Hossen, M. E., Chowdhury, M. E., Hayat, R., Shovon, &. M. S. S., Shabbir, H. -. A. -., Alamgir, M., Akter, S., Chowdhury, R., Sunny, A. R. (2024). "Machine Learning in Healthcare: From Diagnostics to Personalized Medicine and Predictive Analytics", Journal of Angiotherapy, 8(12),1-8,10160. https://doi.org/10.25163/angiotherapy.81210160

Bragger, J. D., Bragger, D., Hantula, D. A., & Kirnan, J. (1998). Hyteresis and uncertainty: The effect of uncertainty on delays to exit decisions. Organizational Behavior and Human Decision Processes, 74(3), 229–253.  https://doi.org/10.1006/obhd.1998.2779

Harris, R. L. L. (2022). Reduction of Employees’ Fear of Job Loss during Organizational Transformations (Doctoral dissertation, The Chicago School of Professional Psychology).

Malik, S. A. (2023). Unlocking Organizational Potential: Harnessing AI literacy for Dynamic Capabilities through sensing, seizing and reconfiguring initiatives in IT firms. Theseus. https://www.theseus.fi/handle/10024/812073

Ahmed, I. (2023, December 31). The role of big data analytics in obtaining competitive insights. https://thecrja.com/index.php/Journal/article/view/15

Musyoka, G. V. (2024, September 1). AI based Framework for Government Oversight of Personal Data Consent Compliance A Case study of Nairobi County. http://41.89.31.5:8080/handle/123456789/1820

Da Costa, R. J. L., & Gonçalves, R. a. H. (2024, December 10). Perceptions of AI adoption: Insights into productivity, organizational benefits, and future readiness from employees’ perspective. https://repositorio.iscte-iul.pt/handle/10071/33794