Journal of Ai ML DL
Journal of Ai ML DL | Online ISSN 3070-2143
0
Citations
25.9k
Views
17
Articles
Volume 1 Number 1 2025
RESEARCH ARTICLE (Open Access)
AI-Driven Business Analytics and Organizational Growth: Sectoral Adoption, Governance Challenges, and Strategic Priorities in the U.S.
Niladry Chowdhury1*, Md Sakib Mia1, Md Iqbal Hossain2, Anik Biswas3, Ispita Jahan4
Journal of Ai ML DL 1 (1) 1-8 https://doi.org/10.25163/ai.1110382
Submitted: 20 November 2024 Revised: 07 January 2025 Published: 09 January 2025
Abstract
Background: The IT business analytics field experiences a transformation through the implementation of Artificial Intelligence (AI) and Machine Learning (ML) which enables organizations to discover advanced insights and predict market trends while optimizing their decision- making operations. The United States business environment shows growing interest in artificial intelligence and machine learning because these technologies enable companies to stay ahead in digital market competition.
Methods: The study collected information from 155 business professionals who represent various business sectors across the United States. The research methodology combined survey data analysis with interview responses to create a mixed-methods approach for obtaining quantitative and qualitative information. The study examined organizational usage of AI/ML analytics together with its advantages and operational difficulties and their subsequent effects on organizational performance. Statistical analysis was conducted using frequency distribution and percentage comparisons.
Results: Research findings demonstrate that 72% of organizations achieve better decision-making through AI-based analytics and 65% experience operational efficiency improvements. A substantial number of organizations encounter major obstacles because they lack qualified workers (59%) and must handle expensive implementation expenses (53%). Different sectors display varying adoption rates with IT services reaching 81% adoption and finance following at 68% and manufacturing at 59%. The outcomes show that top strategic priorities include investments in explainable AI together with robust governance frameworks.
Conclusion: The use of AI and ML in IT-driven business analytics leads to positive growth for organizations operating in the USA. Organizations continue to discover the strategic significance of advanced analytics although they face certain obstacles.
Keywords: Artificial Intelligence, Machine Learning, Business Analytics, Decision-Making, Organizational Growth
References
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. International Journal of Social Science Exceptional Research, 1(1), 13–25. https://doi.org/10.54660/ijsser.2022.1.1.13-25
Adekunle, B. I., Chukwuma-Eke, E. C., Balogun, E. D., & Ogunsola, K. O. (2021a). A predictive modeling approach to optimizing business operations: A case study on reducing operational inefficiencies through machine learning. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 791–799. https://doi.org/10.54660/ijmrge.2021.2.1.791-799
Adekunle, B. I., Chukwuma-Eke, E. C., Balogun, E. D., & Ogunsola, K. O. (2021b). Machine learning for automation: Developing data-driven solutions for process optimization and accuracy improvement. International Journal of Multidisciplinary Research and Growth Evaluation, 3(1), 800–808. https://doi.org/10.54660/ijmrge.2021.2.1.800-808
Ahmed, S., Alshater, M. M., Ammari, A. E., & Hammami, H. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646. https://doi.org/10.1016/j.ribaf.2022.101646
Ajami, R. A., & Karimi, H. A. (2023). Artificial intelligence: Opportunities and challenges. Journal of Asia-Pacific Business, 24(2), 73–75. https://doi.org/10.1080/10599231.2023.2210239
Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2020). Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research, 308(1–2), 7–39. https://doi.org/10.1007/s10479-020-03620-w
Alomar, M. A. (2022). Performance optimization of industrial supply chain using artificial intelligence. Computational Intelligence and Neuroscience, 2022, 9306265. https://doi.org/10.1155/2022/9306265
Ames, H., Glenton, C., & Lewin, S. (2019). Purposive sampling in a qualitative evidence synthesis: A worked example from a synthesis on parental perceptions of vaccination communication. BMC Medical Research Methodology, 19(1), 26. https://doi.org/10.1186/s12874-019-0665-4
Bhatore, S., Mohan, L., & Reddy, Y. R. (2020). Machine learning techniques for credit risk evaluation: A systematic literature review. Journal of Banking and Financial Technology, 4(1), 111–138. https://doi.org/10.1007/s42786-020-00020-3
Cappelli, P. (2014). Skill gaps, skill shortages and skill mismatches: Evidence for the US (Working Paper No. 20382). National Bureau of Economic Research. https://doi.org/10.3386/w20382
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39. https://doi.org/10.1080/07421222.2015.1138364
Chen, J., Lim, C. P., Tan, K. H., Govindan, K., & Kumar, A. (2021). Artificial intelligence-based human-centric decision support framework: An application to predictive maintenance in asset management under pandemic environments. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04373-w
Fang, F., Wei, W., & Huang, H. (2019). Keeping up with fast-paced industry changes: Digital media education in U.S. advertising and PR programs. Journal of Advertising Education, 23(2), 80–99. https://doi.org/10.1177/1098048219877765
Gebauer, H., Gustafsson, A., & Witell, L. (2011). Competitive advantage through service differentiation by manufacturing companies. Journal of Business Research, 64(12), 1270–1280. https://doi.org/10.1016/j.jbusres.2011.01.015
Hodgkinson, G. P., Sadler-Smith, E., Burke, L. A., Claxton, G., & Sparrow, P. R. (2009). Intuition in organizations: Implications for strategic management. Long Range Planning, 42(3), 277–297. https://doi.org/10.1016/j.lrp.2009.05.003
Irani, Z., Themistocleous, M., & Love, P. E. (2003). The impact of enterprise application integration on information system lifecycles. Information & Management, 41(2), 177–187. https://doi.org/10.1016/s0378-7206(03)00046-6
Jacobides, M. G., Brusoni, S., & Candelon, F. (2021). The evolutionary dynamics of the artificial intelligence ecosystem. Strategy Science, 6(4), 412–435. https://doi.org/10.1287/stsc.2021.0148
Kim, B., Park, J., & Suh, J. (2020). Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information. Decision Support Systems, 134, 113302. https://doi.org/10.1016/j.dss.2020.113302
Kobos, P. H., Malczynski, L. A., Walker, L. T. N., Borns, D. J., & Klise, G. T. (2018). Timing is everything: A technology transition framework for regulatory and market readiness levels. Technological Forecasting and Social Change, 137, 211–225. https://doi.org/10.1016/j.techfore.2018.07.052
Lee, S. M., Lee, D., & Kim, Y. S. (2019). The quality management ecosystem for predictive maintenance in the Industry 4.0 era. International Journal of Quality Innovation, 5(1), 4. https://doi.org/10.1186/s40887-019-0029-5
Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2017). Fair, transparent, and accountable algorithmic decision-making processes. Philosophy & Technology, 31(4), 611–627. https://doi.org/10.1007/s13347-017-0279-x
Maier, C., Thatcher, J. B., Grover, V., & Dwivedi, Y. K. (2023). Cross-sectional research: A critical perspective, use cases, and recommendations for IS research. International Journal of Information Management, 70, 102625. https://doi.org/10.1016/j.ijinfomgt.2023.102625
Majeed, A., & Hwang, S. O. (2021). Data-driven analytics leveraging artificial intelligence in the era of COVID-19: An insightful review of recent developments. Symmetry, 14(1), 16. https://doi.org/10.3390/sym14010016
Marcel, V. O. (2010, June 17). Business agility and information technology in service organizations [Doctoral dissertation, Erasmus University Rotterdam]. Erasmus University Thesis Repository. http://hdl.handle.net/1765/19805
Mikalef, P., Giannakos, M. N., Pappas, I. O., & Krogstie, J. (2018, April). The human side of big data: Understanding the skills of the data scientist in education and industry. In 2018 IEEE Global Engineering Education Conference (EDUCON) (pp. 1–8). IEEE. https://doi.org/10.1109/EDUCON.2018.8363273
Orphanides, A. (2001). Monetary policy rules based on real-time data. American Economic Review, 91(4), 964–985. https://doi.org/10.1257/aer.91.4.964
Posavec, A. B., & Krajnovic, S. (2016). Challenges in adopting big data strategies and plans in organizations. 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 1229–1234. https://doi.org/10.1109/MIPRO.2016.7522327
Rane, N. (2023). Enhancing customer loyalty through artificial intelligence (AI), Internet of Things (IoT), and big data technologies: Improving customer satisfaction, engagement, relationship, and experience. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4616051
Roski, J., Maier, E. J., Vigilante, K., Kane, E. A., & Matheny, M. E. (2021). Enhancing trust in AI through industry self-governance. Journal of the American Medical Informatics Association, 28(7), 1582–1590. https://doi.org/10.1093/jamia/ocab065
Sharma, A. K., Sharma, D. M., Purohit, N., Rout, S. K., & Sharma, S. A. (2021). Analytics techniques: Descriptive analytics, predictive analytics, and prescriptive analytics. In EAI/Springer Innovations in Communication and Computing (pp. 1–14). https://doi.org/10.1007/978-3-030-82763-2_1
Sharma, A., & Venkatraman, S. (2023). Towards a standard framework for organizational readiness for technology adoption. In Advances in Digital Manufacturing Systems (pp. 197–219). https://doi.org/10.1007/978-981-19-7071-9_10
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
Singireddy, J., Dodda, A., Burugulla, J. K. R., Paleti, S., & Challa, K. (2021). Innovative financial technologies: Strengthening compliance, secure transactions, and intelligent advisory systems through AI-driven automation and scalable data architectures. Universal Journal of Finance and Economics, 1(1), 123–143. https://doi.org/10.31586/ujfe.2021.1298
Svetlana, N., Anna, N., Svetlana, M., Tatiana, G., & Olga, M. (2022). Artificial intelligence as a driver of business process transformation. Procedia Computer Science, 213, 276–284. https://doi.org/10.1016/j.procs.2022.11.067
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910
Tariq, M. U., Poulin, M., & Abonamah, A. A. (2021). Achieving operational excellence through artificial intelligence: Driving forces and barriers. Frontiers in Psychology, 12, 686624. https://doi.org/10.3389/fpsyg.2021.686624
Vaughn, P., & Turner, C. (2015). Decoding via coding: Analyzing qualitative text data through thematic coding and survey methodologies. Journal of Library Administration, 56(1), 41–51. https://doi.org/10.1080/01930826.2015.1105035
Venkatesh, V., Brown, S., & Sullivan, Y. (2016). Guidelines for conducting mixed-methods research: An extension and illustration. Journal of the Association for Information Systems, 17(7), 435–494. https://doi.org/10.17705/1jais.00433
Wamba-Taguimdje, S., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411
Wang, D., Si, R., & Fahad, S. (2022). Evaluating the small and medium-sized enterprises motivating factors and influencing barriers about adoption of green practices. Environment, Development and Sustainability, 25(4), 3029–3041. https://doi.org/10.1007/s10668-022-02166-0
Weber, M., Engert, M., Schaffer, N., Weking, J., & Krcmar, H. (2022). Organizational capabilities for AI implementation—Coping with inscrutability and data dependency in AI. Information Systems Frontiers, 25(4), 1549–1569. https://doi.org/10.1007/s10796-022-10297-y
Williams, B., Bishop, D., Gallardo, P., & Chase, J. G. (2023). Demand side management in industrial, commercial, and residential sectors: A review of constraints and considerations. Energies, 16(13), 5155. https://doi.org/10.3390/en16135155
Wohlstetter, P., Datnow, A., & Park, V. (2008). Creating a system for data-driven decision-making: Applying the principal-agent framework. School Effectiveness and School Improvement, 19(3), 239–259. https://doi.org/10.1080/09243450802246376
Recommended articles
Fairness-Aware AI for Healthcare Supply Chain Equity: Integrating Social Determinants of Health, Causal Modeling, and Geospatial Optimization
Comprehensive Review of Foundation Toxicity Models Integrating In Vivo, In Vitro, and Chemical Knowledge for Unified Risk Prediction
14
Save
Save
0
Citation
Citation
1769
View
View
3
Share
Share