Artificial Intelligence-Enabled Transformation Across Management and Engineering Domains: An Integrative Review of Finance, Services, Supply Chains, Education, and Social Innovation
Keywords:
Artificial Intelligence, Digital Transformation, Sustainable Value, Customer Service, Supply Chain Management, Financial Analytics, Education Technology, Social InnovationAbstract
AI has ceased to be a technical and expert ability, but it is a strategic asset and is used to make decisions, provide services, predict, engage with customers, comply, and innovate in various fields. But the literature is frequently unresponsive. Finance scholarship is focused on prediction, compliance and risk, service and marketing scholarship on personalization and automation, supply chain scholarship on visibility and resilience, educational scholarship on guidance and learning support, and smart city scholarship on social innovation, inclusion and solving urban problems. This disruption prevents the creation of a consistent view of how artificial intelligence creates value in the fields of management and engineering as well as introduces new governance, trust, and sustainability issues. The paper will create an integrative review of cross-domain applications of artificial intelligence and suggest a strategic model of AI-enabled sustainable value creation. The review summarizes the recent literature along with eleven area contributions including disability peer support, urban technologies, quantum-enhanced AI, blockchain and IoT in supply chains, AI-generated educational visuals, AI agents in customer service, sustainable tourism, AI in tax compliance, stock market prediction, digital school counselling, and AI in finance. By relying on the discussion of dynamic capabilities, technology acceptance, trust and risk perspectives, and sustainable value logic, the paper contends that the key to successful AI adoption is not only based on the performance of the algorithm but on the design that is human-centered, its domain relevance, data quality, and institutional legitimacy, as well as quantifiable organizational outcomes. The paper has three contributions. First, it charts the movement of AI capabilities throughout a variety of management and engineering environments. Second, it determines repetitive processes that relate AI inputs to operational, strategic, and social outputs. Third, it provides an inclusive, governance, explainability, capacity building, and responsible innovation cross-domain research agenda. The review has concluded that AI generates long-term value when technical complexities are combined with trust, sustainability, and implementation that is contextual.
References
1. Ajzen, I. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
2. Anagnostopoulos, I. (2018). Fintech and regtech: Impact on regulators and banks. Journal of Economics and Business, 100, 7–25. https://doi.org/10.1016/j.jeconbus.2018.07.003
3. Ansarullah, S. I., Ikhlaq, S., Yousuf, T., ul Islam, M. I., Mufti, S., & Fayaz, F. A. (2026). Quantum-enhanced artificial intelligence: The next frontier in computing and decision-making. In From Bits to Qubits: The Quantum Transformation of Computing (Chapter 9). Springer. https://doi.org/10.1007/978-3-031-90727-2_9
4. Ansarullah, S. I., Islam, M. I., Ahmad, F., Danish, M., Dar, A. R., & Mufti, S. (2025). Predicting stock markets using linear regression and cloud computing: A literature review and case study approach. In Advancements in Cloud-Based Intelligent Informative Engineering (pp. 331–352). IGI Global. https://doi.org/10.4018/979-8-3373-0781-7.ch015
5. Ansarullah, S. I., Islam, M. I., Begum, G., & Bhat, N. A. (2025). Leveraging digital tools to enhance school counselling: Case studies and best practices. In Enhancing School Counseling With Technology and Case Studies (pp. 49–72). IGI Global. https://doi.org/10.4018/979-8-3693-8392-6.ch003
6. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
7. Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management: A literature review. International Journal of Production Research, 57(15–16), 4719–4742. https://doi.org/10.1080/00207543.2017.1402140
8. Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482. https://doi.org/10.25300/MISQ/2013/37:2.3
9. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
10. Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017). Quantum machine learning. Nature, 549(7671), 195–202. https://doi.org/10.1038/nature23474
11. Bocken, N. M. P., Short, S. W., Rana, P., & Evans, S. (2014). A literature and practice review to develop sustainable business model archetypes. Journal of Cleaner Production, 65, 42–56. https://doi.org/10.1016/j.jclepro.2013.11.039
12. Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-dependent algorithm aversion. Journal of Marketing Research, 56(5), 809–825. https://doi.org/10.1177/0022243719851788
13. Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S. C., Endo, S., Fujii, K., McClean, J. R., Mitarai, K., Yuan, X., Cincio, L., & Coles, P. J. (2021). Variational quantum algorithms. Nature Reviews Physics, 3(9), 625–644. https://doi.org/10.1038/s42254-021-00348-9
14. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
15. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033
16. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., ... Wright, R. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
17. Elkington, J. (1998). Partnerships from cannibals with forks: The triple bottom line of 21st-century business. Environmental Quality Management, 8(1), 37–51. https://doi.org/10.1002/tqem.3310080106
18. Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
19. Geissdoerfer, M., Savaget, P., Bocken, N. M. P., & Hultink, E. J. (2017). The circular economy: A new sustainability paradigm? Journal of Cleaner Production, 143, 757–768. https://doi.org/10.1016/j.jclepro.2016.12.048
20. Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057
21. Hart, S. L., & Milstein, M. B. (2003). Creating sustainable value. Academy of Management Executive, 17(2), 56–67. https://doi.org/10.5465/AME.2003.10025194
22. Hermann, E., Wirtz, J., Paluch, S., & Kunz, W. H. (2024). Deploying artificial intelligence in services to aid vulnerable consumers. Journal of the Academy of Marketing Science, 52(6), 1431–1451. https://doi.org/10.1007/s11747-023-00986-8
23. Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
24. Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
25. Islam, M. I. (2025). Leveraging blockchain and IoT for sustainable supply chain management in Industry 4.0. In Next-Generation Data-Driven Business 4.0 Using the Internet of Things, Blockchain, and Interconnected Devices (pp. 91–106). CRC Press. https://doi.org/10.1201/9781003568988-5
26. Islam, M. I., Ansarullah, S. I., Nisa, K. U., Mufti, S., Dar, A. R., & Salau, A. O. (2026). Social network design for disability peer support: Identifying barriers, applying standards, and implementing measurable solutions. Open Information Science, 10(1), Article 20250036. https://doi.org/10.1515/opis-2025-0036
27. Islam, M. I., Baba, M. M., Ahmad, A., & Ansarullah, S. I. (2025). Redefining travel: Sustainable tourism, experiential travel, and dark tourism. In The Emerald Handbook of International Dark Tourism (pp. 81–97). Emerald Publishing. https://doi.org/10.1108/978-1-80592-501-920251005
28. Islam, M. I., Nisa, K. U., Ikhlaq, S., Yousuf, T., Gulzar, S., & Ansarullah, S. I. (2026). Fostering social innovation through urban technologies. In Tech-Enabled Urbanism and Entrepreneurship for Inclusive Cities (pp. 103–119). Springer. https://doi.org/10.1007/978-981-95-3873-7_6
29. Islam, M. I., Nisa, K. U., Mufti, S., Ansarullah, S. I., Ikhlaq, S., & Yousuf, T. (2025). Artificial intelligence in tax compliance: Transforming taxpayer behavior and system efficiency. In Modeling and Profiling Taxpayer Behavior and Compliance (pp. 251–270). IGI Global. https://doi.org/10.4018/979-8-3373-0422-9.ch011
30. Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450
31. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
32. Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004
33. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeiffer, F., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
34. Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations & Production Management, 37(1), 10–36. https://doi.org/10.1108/IJOPM-02-2015-0078
35. Laine, T., Kauttonen, J., & Lähteenmäki, I. (2024). Ethics-based AI auditing: A systematic literature review on conceptualizations of ethical principles and knowledge contributions to stakeholders. Information & Management, 61(8), 103969. https://doi.org/10.1016/j.im.2024.103969
36. Lee, I., & Shin, Y. J. (2018). Fintech: Ecosystem, business models, investment decisions, and challenges. Business Horizons, 61(1), 35–46. https://doi.org/10.1016/j.bushor.2017.09.003
37. Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103. https://doi.org/10.1016/j.obhdp.2018.12.005
38. Ma, L., & Sun, B. (2020). Machine learning and AI in marketing: Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481–504. https://doi.org/10.1016/j.ijresmar.2020.04.005
39. Nambisan, S. (2017). Digital entrepreneurship: Toward a digital technology perspective of entrepreneurship. Entrepreneurship Theory and Practice, 41(6), 1029–1055. https://doi.org/10.1111/etap.12254
40. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275
41. Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79. https://doi.org/10.22331/q-2018-08-06-79
42. Queiroz, M. M., Fosso Wamba, S., De Bourmont, M., & Telles, R. (2021). Blockchain adoption in operations and supply chain management: Empirical evidence from an emerging economy. International Journal of Production Research, 59(20), 6087–6103. https://doi.org/10.1080/00207543.2020.1803511
43. Rawanda, M. I. I. (2025). Transforming customer service with AI agents: Enhancing efficiency and personalisation in the service sector: Balancing automation and the human touch. In Harnessing Emotion AI for Customer Support and Employee Wellbeing (pp. 241–268). IGI Global. https://doi.org/10.4018/979-8-3373-3658-9.ch010
44. Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117–2135. https://doi.org/10.1080/00207543.2018.1533261
45. Schuld, M., & Killoran, N. (2019). Quantum machine learning in feature Hilbert spaces. Physical Review Letters, 122(4), 040504. https://doi.org/10.1103/PhysRevLett.122.040504
46. Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe and trustworthy. International Journal of Human-Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118
47. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
48. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, 15. https://doi.org/10.1186/s40561-023-00237-x
49. ul Islam, M., Nisa, K. U., Ansarullah, S. I., Mufti, S., Danish, M., Ajala, O., & Salau, A. O. (2025). Leveraging AI-generated visuals for enhancing management of career orientation: A quasi-experimental study. Open Information Science, 9(1), Article 20250019. https://doi.org/10.1515/opis-2025-0019
50. ul Islam, M. I., Ahmad, F., Ansarullah, S. I., & Nisa, K. U. (2025). The future of machine learning and artificial intelligence in finance. In Utilizing AI and Machine Learning in Financial Analysis (pp. 491–508). IGI Global. https://doi.org/10.4018/979-8-3693-8507-4.ch026
51. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
52. von Schnurbein, G., Potluka, O., & Mayer, A. (2023). Creating social innovation in urban development through collaborative processes. Innovation: The European Journal of Social Science Research, 36(2), 316–332. https://doi.org/10.1080/13511610.2021.1910800
53. Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C.-E. (2020). Influence of artificial intelligence 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
54. Wirtz, J., Patterson, P. G., Kunz, W., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931. https://doi.org/10.1108/JOSM-04-2018-0119
55. Ylipulli, J., & Hämäläinen, T. (2023). Towards a practice-oriented framework for digital inequality in smart cities. In Proceedings of the 10th International Conference on Communities & Technologies (pp. 180–190). ACM. https://doi.org/10.1145/3593743.3593781
56. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0
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