Transforming E-Business with Artificial Intelligence: Insights, Impacts, and Emerging Trends
DOI:
https://doi.org/10.53748/jbms.v5i3.142Keywords:
internet of things, artificial intelligence, e-business modelAbstract
The objective of the research is to synthesize technical insights from computer science with managerial perspectives from business and social studies, thereby offering a holistic lens for both scholars and practitioners. The study employs a systematic literature review of peer‑reviewed articles, industry reports, and case studies published between 2015 and 2025, following PRISMA guidelines to ensure transparency and rigor. Findings reveal that AI enhances operational efficiency, supports real‑time decision‑making, and strengthens customer engagement, but simultaneously raises challenges of latency in edge computing, ethical concerns in data use, and regulatory compliance across jurisdictions. The framework highlights feedback loops where successful adoption generates new data that further refines AI systems, while unresolved risks can hinder organizational performance. The study contributes an interdisciplinary model that clarifies AI’s role in reshaping e‑business and sets an agenda for future empirical validation.
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References
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160.
Afzal, B., Umair, M., Shah, G. A., & Ahmed, E. (2019). Enabling IoT platforms for social IoT applications: Vision, feature mapping, and challenges. Future Generation Computer Systems, 92, 718–731. https://doi.org/10.1016/j.future.2017.12.002
Aheleroff, S., Xu, X., Lu, Y., Aristizabal, M., Velásquez, J. P., Joa, B., & Valencia, Y. (2020). IoT-enabled smart appliances under industry 4.0: A case study. Advanced Engineering Informatics, 43, 101043. https://doi.org/10.1016/j.aei.2020.101043
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., ... & Herrera, F. (2023). Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence.
Allioui, H., & Mourdi, Y. (2023). Unleashing the potential of AI: Investigating cutting-edge technologies that are transforming businesses. International Journal of Computer Engineering and Data Science (IJCEDS), 3(2), 1–12.
Areiqat, A. Y., Hamdan, A., Alheet, A. F., & Alareeni, B. (2021). Impact of artificial intelligence on E-commerce development. In The Importance of New Technologies and Entrepreneurship in Business Development: In The Context of Economic Diversity in Developing Countries: The Impact of New Technologies and Entrepreneurship on Business Development (pp. 571–578). Springer International Publishing. https://doi.org/10.1007/978-3-030-69221-6_43
Beynon‐Davies, P., Jones, P., & White, G. R. (2016). Business patterns and strategic change. Strategic Change, 25(6), 675–691. https://doi.org/10.1002/jsc.2101
Caiazzo, B., Murino, T., Petrillo, A., Piccirillo, G., & Santini, S. (2023). An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation. Journal of Manufacturing Technology Management, 34(4), 507–534. https://doi.org/10.1108/JMTM-02-2022-0092
Castillo, M. J., & Taherdoost, H. (2023). The Impact of AI Technologies on E-Business. Encyclopedia, 3(1), 107–121. https://doi.org/10.3390/encyclopedia3010009
Coelho, P., Bessa, C., Landeck, J., & Silva, C. (2023). Industry 5.0: The arising of a concept. Procedia Computer Science, 217, 1137–1144. https://doi.org/10.1016/j.procs.2022.12.312
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Dixon, R. B. L. (2023). A principled governance for emerging AI regimes: Lessons from China, the European Union, and the United States. AI and Ethics, 3(3), 793–810. https://doi.org/10.1080/14494035.2021.1928377
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Dubosson‐Torbay, M., Osterwalder, A., & Pigneur, Y. (2002). E‐business model design, classification, and measurements. Thunderbird International Business Review, 44(1), 5–23.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Galante, N., Cotroneo, R., Furci, D., Lodetti, G., & Casali, M. B. (2023). Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives. International Journal of Legal Medicine, 137(2), 445–458. https://doi.org/10.1007/s00414-022-02928-5
Gong, Y., Schroeder, A., Pan, B., Sundar, S. S., & Mowen, A. J. (2024). Does algorithmic filtering lead to filter bubbles in online tourist information searches? Information Technology & Tourism, 26(1), 183–217.
Gupta, B. B., & Quamara, M. (2020). An overview of Internet of Things (IoT): Architectural aspects, challenges, and protocols. Concurrency and Computation: Practice and Experience, 32(21), e4946. https://doi.org/10.1002/cpe.4946
Isabella, G., Almeida, M. I. S. D., & Mazzon, J. A. (2023). One-way road: The impact of artificial intelligence on the development of knowledge in management. RAUSP Management Journal, 58, 249–255. https://doi.org/10.1108/RAUSP-07-2023-273.
James, U. U., Idika, C. N., Enyejo, L. A., Abiodun, K., & Enyejo, J. O. (2024). Adversarial Attack Detection Using Explainable AI and Generative Models in Real-Time Financial Fraud Monitoring Systems. International Journal of Scientific Research and Modern Technology, 3(12), 142–157.
Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15–30. https://doi.org/10.1016/j.aac.2022.10.001
Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; Final report of the Industrie 4.0 Working Group. Forschungsunion.
Kelly, M., Mokyr, J., & Ó Gráda, C. (2023). The mechanics of the Industrial Revolution. Journal of Political Economy, 131(1), 59–94. https://doi.org/10.1086/720890
Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), 271. https://doi.org/10.3390/ijerph18010271
Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23.
Lee, J., Suh, T., Roy, D., & Baucus, M. (2019). Emerging technology and business model innovation: The case of artificial intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 44. https://doi.org/10.3390/joitmc5030044
Lloyd, K. (2018). Bias amplification in artificial intelligence systems. arXiv preprint arXiv:1809.07842. https://doi.org/10.48550/arXiv.1809.07842
Maghsoudi, M., Shokouhyar, S., Ataei, A., Ahmadi, S., & Shokoohyar, S. (2023). Co-authorship network analysis of AI applications in sustainable supply chains: Key players and themes. Journal of Cleaner Production, 422, 138472. https://doi.org/10.1016/j.jclepro.2023.138472
McAfee, A., & Brynjolfsson, E. (2017). Machine, platform, crowd: Harnessing our digital future. WW Norton & Company.
Montenegro-Rueda, M., Fernández-Cerero, J., Fernández-Batanero, J. M., & López-Meneses, E. (2023). Impact of the implementation of ChatGPT in education: A systematic review. Computers, 12(8), 153. https://doi.org/10.3390/computers12080153
Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022). A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0. Energies, 15(17), 6276. https://doi.org/10.3390/en15176276
OECD (2025). The Adoption of Artificial Intelligence in Firms. New Evidence for Policymaking. https://www.oecd.org/en/publications/the-adoption-of-artificial-intelligence-in-firms_f9ef33c3-en.html
Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & McKenzie, J. E. (2021). PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. thebmj, 372, n160. https://doi.org/10.1136/bmj.n160
Radanliev, P., & Santos, O. (2023). Adversarial Attacks Can Deceive AI Systems, Leading to Misclassification or Incorrect Decisions. https://doi.org/10.20944/preprints202309.2064.v1
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020, January). Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 469–481). https://doi.org/10.1145/3351095.3372828
Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems. https://doi.org/10.1016/j.iotcps.2023.04.003
Recorded Future (2025). Measuring the U.S.–China AI gap: Model performance, investment, and regulation. https://www.recordedfuture.com/research/measuring-the-us-china-ai-gap
Rojko, A. (2017). Industry 4.0 concept: Background and overview. International Journal of Interactive Mobile Technologies, 11(5).
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
Rudin, C., & Radin, J. (2019). Why are we using black box models in AI when we don’t need to? A lesson from an explainable AI competition. Harvard Data Science Review, 1(2), 1–9. 10.1162/99608f92.5a8a3a3d
Sánchez, E., Calderón, R., & Herrera, F. (2025). Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges. Applied Sciences, 15(12), 6465. https://doi.org/10.3390/app15126465
Schuetz, P. N. (2021). Fly in the Face of Bias: Algorithmic Bias in Law Enforcement’s Facial Recognition Technology and the Need for an Adaptive Legal Framework. Minnesota Journal of Law & Inequality, 39, 221. https://heinonline.org/HOL/LandingPage?handle=hein.journals/
Schwaeke, J., Peters, A., Kanbach, D. K., Kraus, S., & Jones, P. (2025). The new normal: The status quo of AI adoption in SMEs. Journal of small business management, 63(3), 1297-1331. https://doi.org/10.1080/00472778.2024.2379999
Schweichhart, K. (2016). Reference architectural model industrie 4.0 (rami 4.0). An Introduction, 40. https://img5.custompublish.com/getfile.php/3901260.2265.akzillql7uuipz/RAMEI+4.0.pdf?return=www.amnytt.no
Seizov, O., & Wulf, A. J. (2020). Artificial Intelligence and Transparency: A Blueprint for Improving the Regulation of AI Applications in the EU. European Business Law Review, 31(4). https://doi.org/10.54648/eulr2020024
Shively, R. J., Lachter, J., Brandt, S. L., Matessa, M., Battiste, V., & Johnson, W. W. (2018). Why human-autonomy teaming? In Advances in Neuroergonomics and Cognitive Engineering: Proceedings of the AHFE 2017 International Conference on Neuroergonomics and Cognitive Engineering, July 17–21, 2017, The Westin Bonaventure Hotel, Los Angeles, California, USA 8 (pp. 3–11). Springer International Publishing. https://doi.org/10.1007/978-3-319-60642-2_1
Smith, B. (2021). AI Adoption Trends 2021: Key Insights from the State of AI Report. Forbes. https://www.forbes.com/sites/forbesbusinesscouncil/2021/07/21/ai-adoption-trends-2021-key-insights-from-the-state-of-ai-report/
Taherdoost, H. (2023). E-Business Models and Strategies. In E-Business Essentials: Building a Successful Online Enterprise (pp. 25–50). Cham: Springer Nature Switzerland.
Thormundsson B., (2023). Artificial Intelligence (AI) market size worldwide in 2021 with a forecast until 2030. Statista. https://www.statista.com/statistics/1365145/artificial-intelligence-market-size/
Van Noorden, R. (2020). The ethical questions that haunt facial-recognition research. Nature, 587(7834), 354–359.
Vermesan, O., & Friess, P. (Eds.). (2022). Digitising the Industry Internet of Things Connecting the Physical, Digital and VirtualWorlds. CRC Press.
Wang, J., Xu, C., Zhang, J., & Zhong, R. (2022). Big data analytics for intelligent manufacturing systems: A review. Journal of Manufacturing Systems, 62, 738–752.
Watts, R. & Haan, K. (2023). How Businesses Are Using Artificial Intelligence in 2023. https://www.forbes.com/advisor/business/software/ai-in-business/
Webster, M., (2023). 149 AI Statistics: The Present and Future of AI at Your Fingertips. https://www.authorityhacker.com/aistatistics/#:~:text=AI%20service%20revenue%20will%20increase,CAGR%20of%2042.2%25%20from%202018
Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122–1136. 10.1109/JAS.2023.123618
Yin, J., Ngiam, K. Y., & Teo, H. H. (2021). Role of artificial intelligence applications in real-life clinical practice: Systematic review. Journal of Medical Internet Research, 23(4), e25759. doi:10.2196/25759
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