Artificial Intelligence in Developing Nation: Bridging the Execution Gap with Potential
Authors
Abdrahman Atanda Moustapha
Kwara State University, Nigeria (NG)
Mohammed Lawal Akanbi
Kwara State University, Nigeria (NG)
Ganiyat Olayinka Bello
Kwara State University, Nigeria (NG)
Abdulrasheed Ishola Yakub (Esq.)
Kwara State University, Nigeria (NG)
Roseline Damilola Akanji
Kwara State University, Nigeria (NG)
Basirat Raji
Kwara State University, Nigeria (NG)
Article Information
DOI: 10.51583/IJLTEMAS.2025.1408000188
Subject Category: Artificial Intelligence
Volume/Issue: 14/8 | Page No: 1497-1510
Publication Timeline
Submitted: 2025-09-23
Published: 2025-09-23
Abstract
Abstract: In order to bridge the gap between the enormous promise of AI and practicality, this article explores the role of AI in emerging nations. Global AI technological advances will have a significant impact on social and economic growth, particularly in areas that involve a variety of opportunities and difficulties. The paper examines how AI is now adopted in poor countries and examines the advantages, disadvantages, and ethical considerations. The study examines methods and approaches for utilizing AI's revolutionary potential in a variety of industries, including medical care, agriculture, and education, through a thorough literature analysis and case studies. The results highlight the value of talent shortages and infrastructural restrictions being addressed through legislative frameworks, public-private partnerships, and capacity building. Research advances our awareness of the potential and challenges associated with adopting artificial intelligence in developing nations, offering guidance to practitioners, policymakers, librarians, and academics as they navigate this rapidly changing technological environment.
Keywords
Artificial intelligence, global connectivity, emerging technologies, organizational flexibility, sustainable growth
Downloads
References
1. Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC on computer and communications security (pp. 308–318). [Google Scholar] [Crossref]
2. Adegbiji, O., & Okonkwo, C. (2023). Artificial intelligence in healthcare delivery: A review of applications and challenges in Africa. Journal of Healthcare Informatics, 10(2), 1-15. [Google Scholar] [Crossref]
3. Ade-Ibijola, A., & Okonkwo, C. (2023). Artificial Intelligence in Africa: Emerging Challenges. In Responsible AI in Africa: Challenges and Opportunities (pp. 101–117), Cham: Springer International Publishing. [Google Scholar] [Crossref]
4. lmeida, C. A. S., et al. (2023). Artificial intelligence for deforestation monitoring in the Amazon rainforest. Environmental Modelling & Software, 159, 105071. [Google Scholar] [Crossref]
5. Al-Turjman, F. (2023). AI-powered cloud for COVID-19 and other infectious disease diagnoses. Personal and Ubiquitous Computing, 27(3), 661-664 [Google Scholar] [Crossref]
6. Arntz, M., Gregory, T., & Zierahn, U. (2016). The risk of automation for jobs in OECD countries: A Comparative Analysis. OECD Social, Employment, and Migration Working Papers, No. 189, OECD Publishing, Paris [Google Scholar] [Crossref]
7. Binh, N.T. (2018). Financial transaction systems are used in Vietnam. AU e-journal of Interdisciplinary Research, 3(2). [Google Scholar] [Crossref]
8. Biswas, S., Carson, B., Chung, V., Singh, S., & Thomas, R. (2020). AI-bank of the future: Can banks meet the AI challenge. New York: McKinsey & Company. [Google Scholar] [Crossref]
9. Bostrom, N. (2022). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. [Google Scholar] [Crossref]
10. Bostrom, N., & Yudkowsky, E. (2022). Ethics of Artificial Intelligence. In N. Bostrom & E. Yudkowsky (Eds.), The Cambridge Handbook of Artificial Intelligence. [Google Scholar] [Crossref]
11. Bughin, J., Hazan, E., Lund, S., Dahlström, P., Wiesinger, A., & Subramaniam, A. (2018). Skill shift: Automation and the future of the workforce. McKinsey Global Institute. [Google Scholar] [Crossref]
12. Chen, Y., Mao, Y., & Zhang, Y. (2022). Artificial intelligence in developing countries: A review of applications and challenges. Information Systems Frontiers, 24(1), 15-30. [Google Scholar] [Crossref]
13. Chui, M., Manyika, J., & Miremadi, M. (2023). The future of work after COVID-19. McKinsey Global Institute. [Google Scholar] [Crossref]
14. Dutta, S., Das, D., & Jana, P. K. (2022). Artificial intelligence in finance: A review of current applications and future directions. Journal of Financial Data Science, 4(1), 1-18. [Google Scholar] [Crossref]
15. D Wivedi, Y.K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., & Galanos, V. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agendas for research, practice, and policy. International Journal of Information Management, 57, 10994. [Google Scholar] [Crossref]
16. Esteva, A., Robicquet, A., Ramsundar, B., & Dean, J. (2021). A guide to deep learning in healthcare. Nature Medicine, 27(1), 13-17. [Google Scholar] [Crossref]
17. Food and Agriculture Organisation (2020). Status of digital agriculture in 48 sub-Saharan African countries. Food and Agriculture Organisation of the United Nations. [Google Scholar] [Crossref]
18. Florida, R., Caves, L., & Stolarick, K. (2018). The geography of artificial intelligence: Implications for economic development. Martin Prosperity Institute. [Google Scholar] [Crossref]
19. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., and Luetge, Madelin, R., Pagallo, U., Rossi, F., & Schafer, B. (2021). An ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Ethics, governance, and policies in artificial intelligence, 19–39. [Google Scholar] [Crossref]
20. Fossen, F. M., & Sorgner, A. (2022). Artificial intelligence and labour market outcomes. Journal of Economic Surveys, 36(1), 3-16. [Google Scholar] [Crossref]
21. GSMA (2023). AI for Development: Harnessing the Power of Artificial Intelligence. [Google Scholar] [Crossref]
22. Gartner (2022). Top 10 Strategic Technology Trends. [Google Scholar] [Crossref]
23. Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2021). The future of technology-enabled retail: A review and research agenda. Journal of Retailing, 97(1), 14-29. [Google Scholar] [Crossref]
24. Guo, J., & Li, B. (2018). The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity, 2(1), 174–181. [Google Scholar] [Crossref]
25. Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R.K., & Kumar, P. (2021). Artificial intelligence to deep learning: a machine intelligence approach for drug discovery: Molecular Diversity, 25, 1315–1360. [Google Scholar] [Crossref]
26. Ha, M.S., & Nguyen, T.L. (2022). Digital Transformation in Banking: A Case from Vietnam. In Smart Cities in Asia: Regulations, Problems, and Development (pp. 103–114), Singapore: Springer Nature Singapore. [Google Scholar] [Crossref]
27. Ha, N. T. T., & Nguyen, T. T. (2022). Artificial intelligence in finance: A review of applications in Vietnam. Journal of Financial Innovation, 8(1), 1-15. [Google Scholar] [Crossref]
28. Han, C., Rundo, L., Murao, K., Nemoto, T., & Nakayama, H. (2020). Bridging the gap between AI and healthcare: towards developing clinically relevant AI-powered diagnosis systems. In Artificial Intelligence Applications and Innovations: 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part II 16 (pp. 320–333). Springer International Publishing. [Google Scholar] [Crossref]
29. Harerimana, A., & Mtshali, N.G. (2020). Conceptualization of e-learning in nursing education in the context of Rwanda. Journal of Nursing Education and Practice, 10(6), 26. [Google Scholar] [Crossref]
30. Harerimana, A., & Mtshali, P. (2020). Integrating artificial intelligence in education: A review of Rwanda’s initiatives. Journal of Educational Technology Development and Exchange, 12(1), 1-18. [Google Scholar] [Crossref]
31. Hendler, J. (2023). Understanding the limits of AI coding. Science, 379(6632), 548–548. International Telecommunication Union. (2021). Measuring Digital Development: Facts and Figures 2021. [Google Scholar] [Crossref]
32. Ifenthaler, D., & Yau, J. Y. K. (2023). Artificial intelligence in education: A review of the current state and future directions. Educational Technology Research and Development, 71(1), 1-18. [Google Scholar] [Crossref]
33. ITU (2021). Artificial Intelligence for Development: Opportunities and Challenges. [Google Scholar] [Crossref]
34. ITU (2018). Measuring the Information Society Report 2018. International Telecommunication Union. [Google Scholar] [Crossref]
35. Jobin, A., et al. (2023). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 5(1), 12-20. [Google Scholar] [Crossref]
36. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. [Google Scholar] [Crossref]
37. Kamilaris, A., & Prenafeta-Boldú, F. X. (2022). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 198, 107002. [Google Scholar] [Crossref]
38. Kallio, T. J., & Hölttä, S. (2022). AI for social good: A review of AI applications in developing countries. Information Technology for Development, 28(1), 1-18. [Google Scholar] [Crossref]
39. Kshetri, N. (2021). The role of artificial intelligence in economic development. Journal of Economic Studies, 48(1), 1-15. [Google Scholar] [Crossref]
40. Kshetri, N. (2021). Artificial intelligence in emerging markets: Opportunities, challenges, and implications. Journal of International Management, 27(2), 100774. [Google Scholar] [Crossref]
41. Kumar, V., Mirchandani, R., & Kumar, N. (2022). Artificial intelligence in retail: A systematic review and future directions. Journal of Business Research, 141, 413-425. [Google Scholar] [Crossref]
42. Kusiak, A. (2020). Artificial intelligence in manufacturing: A review of current applications and future directions. International Journal of Production Research, 58(1), 3-16. [Google Scholar] [Crossref]
43. Le, T. T., et al. (2023). AI-powered chatbots for financial inclusion in Vietnam: A case study of NAPAS. Journal of Financial Services Research, 63(1), 123-138. [Google Scholar] [Crossref]
44. Lee, L. H., & Ramesh, S. (2019). Artificial intelligence in manufacturing: A review. Journal of Manufacturing Systems, 53, 261-270. [Google Scholar] [Crossref]
45. Lee, J., Davari, H., & Singh, J. (2019). Industrial AI: Applications and future directions. Journal of Manufacturing Systems, 53, 272-281 [Google Scholar] [Crossref]
46. Lipper, L., Thornton, P., & Campbell, B. (2021). Climate-smart agriculture and sustainable food systems. Sustainability, 13(11), 6266. [Google Scholar] [Crossref]
47. Mannuru, N.R., Shahriar, S., Teel, Z.A., Wang, T., Lund, B.D., Tijani, S., Pohboon, C.O., Agbaji, D., Alhassan, J., Galley, J., & Kousari, R. (2023). Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies on development. Information Development, 02666669231200628. [Google Scholar] [Crossref]
48. Manyika, J., Chui, M., Bisson, P., Chakravorti, B., Woetzel, J., & Stolyar, K. (2021). The future of work after COVID-19. McKinsey Global Institute. [Google Scholar] [Crossref]
49. McKinsey (2023). The Future of Work After COVID-19. [Google Scholar] [Crossref]
50. Mittelstadt, B., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2023). AI ethics: A policy priority for the 2020s. Nature Machine Intelligence, 5(1), 1-4. [Google Scholar] [Crossref]
51. Murphy, C., & Barr, J.L. (2022). Establishing ethical guidelines for applying artificial intelligence to IAEA safeguards (No. IROS65238). Oak Ridge Y-12 Plant (Y-12), Oak Ridge, TN (United States). [Google Scholar] [Crossref]
52. Mwangi, J., et al. (2024). AI-powered diagnostic solutions for healthcare delivery in low-resource settings: A case study of Kenya. Journal of Medical Systems, 48(1), 1-12. [Google Scholar] [Crossref]
53. Niyigena, A., et al. (2022). Personalised learning using AI in Rwandan schools: A case study of the Smart Africa initiative. International Journal of Education and Development using Information and Communication Technology, 18(1), 34-48. [Google Scholar] [Crossref]
54. Organisation for Economic Co-operation and Development. (2022). Artificial intelligence in financial services. Artificial Intelligence in Society. OECD Publishing. [Google Scholar] [Crossref]
55. Pan, Y. (2016). Heading toward artificial intelligence 2.0. Engineering, 2(4), 409–413.Patil, S., & Shankar, K. (2023). AI-powered healthcare systems for developing countries. Journal of Healthcare Engineering, 2023, 1-12. [Google Scholar] [Crossref]
56. Patil, S., & Shankar, H. (2023). Transforming healthcare: harnessing the power of AI in the modern era. International Journal of Multidisciplinary Sciences and Arts, 2(1), 60–70. [Google Scholar] [Crossref]
57. Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: challenges and opportunities for sustainable development. [Google Scholar] [Crossref]
58. Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health care: The future is now. Nature Medicine, 28(1), 15-16. [Google Scholar] [Crossref]
59. Rana, M.M. (2023). Conservation Agriculture for Sustainable Crop Productivity and Economic Return for the Smallholders of Bangladesh: A Systematic Review. Turkish Journal of Agriculture, Food Science, and Technology, 11(10), 2009–2015. [Google Scholar] [Crossref]
60. Rana, S. (2023). Artificial intelligence in Indian agriculture: A review of current applications and future directions. Journal of Agricultural Informatics, 24(1), 1-15. [Google Scholar] [Crossref]
61. Saeed, W., & Omlin, C. (2023). Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities. Knowledge-Based Systems, 263, 110273. [Google Scholar] [Crossref]
62. Sanni, O., Adeleke, O., Ukoba, K., Ren, J., & Jen, T.C. (2024). Prediction of the inhibition performance of agro-waste extract in simulated acidizing media via machine learning. Fuel: 356, 129527. [Google Scholar] [Crossref]
63. Schwab, K. (2022). The Fourth Industrial Revolution. Crown Business. [Google Scholar] [Crossref]
64. Shaktawat, P., & Swaymprava, S. (2024). Digital Agriculture: Exploring the Role of Information and Communication Technology for Sustainable Development. Ed. Biswajit Mallick and Jyotishree Anshuman, published by PMW, New Delhi, p. 31. [Google Scholar] [Crossref]
65. Shaktawat, R. S., & Swayprava, S. (2024). Precision farming using AI and mobile applications for smallholder farmers in India. Journal of Precision Agriculture, 25(1), 123-138. [Google Scholar] [Crossref]
66. Shneiderman, B. (2020). Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(4), 1-31 [Google Scholar] [Crossref]
67. Silva, J. M. C., et al. (2022). Using machine learning and satellite imagery to monitor deforestation in the Amazon. Remote Sensing, 14(11), 2415. [Google Scholar] [Crossref]
68. Sood, A., Sharma, R.K., & Bhardwaj, A.K. (2022). Artificial intelligence research in agriculture: A review. Online Information Review, 46(6), 1054–1075. [Google Scholar] [Crossref]
69. Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. Equity and access in algorithms, mechanisms, and optimization (pp. 1–9). [Google Scholar] [Crossref]
70. Tao, F., Xiao, N., & Sun, Q. (2022). Artificial intelligence in smart manufacturing: A review and future directions. Journal of Intelligent Manufacturing, 33(1), 1-18. [Google Scholar] [Crossref]
71. Tiwari, A., & Jaga, P.K. (2012). Precision farming in India: A review. Outlook on Agriculture, 41(2), 139–143. [Google Scholar] [Crossref]
72. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. [Google Scholar] [Crossref]
73. Ukoba, K., & Jen, T.C. (2022). Biochar and the application of machine learning: a review. Biochar Production Technologies, Properties, and Application. [Google Scholar] [Crossref]
74. UNCTAD (2022). Digital Economy Report 2022: Development and the digital economy. [Google Scholar] [Crossref]
75. United Nations Development Programme. (2023). Human Development Report 2023: Uncertain Times, Unsettled Lives. [Google Scholar] [Crossref]
76. United Nations Development Programme. (2022). Human Development Report 2022: Uncertain Times, Unsettled Lives. [Google Scholar] [Crossref]
77. UNESCO (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. United Nations Educational, Scientific and Cultural Organisation. [Google Scholar] [Crossref]
78. Verhoef, P. C., Broekhuizen, T. L. J., & Bart, Y. (2020). Artificial intelligence in retail: A review of current applications and future directions. International Journal of Research in Marketing, 37(2), 253-270. [Google Scholar] [Crossref]
79. Wakunuma, K., Jiya, T., & Aliyu, S. (2020). Socio-ethical implications of using AI to accelerate SDG3 in least-developed countries. Journal of Responsible Technology, 4, 100006. [Google Scholar] [Crossref]
80. Wang, Y., Liu, T., Tao, D., & Cheng, J. (2020). A survey on financial big data. Information Fusion, 57, 172-195. [Google Scholar] [Crossref]
81. Wang, Y., Xu, W., & Zhang, X. (2020). AI in finance: Applications, challenges, and future directions. Journal of Financial Innovation, 6(2), 1-15. [Google Scholar] [Crossref]
82. WEF (2022). Global Risks Report 2022. [Google Scholar] [Crossref]
83. World Bank (2023). World Development Report: Digital Dividends. [Google Scholar] [Crossref]
84. World Bank (2022). World Development Report 2022: Digital Dividends. World Bank Publication. [Google Scholar] [Crossref]
85. World Economic Forum (2022). The Global Risks Report 2022. World Economic Forum. [Google Scholar] [Crossref]
86. Zawacki-Richter, O., L_RECTtin, F., & Smyrnova, V. (2023). Artificial intelligence in higher education: A systematic review. Journal of Computing in Higher Education, 35(1), 1-22. [Google Scholar] [Crossref]
87. Zhang, W., Li, H., Li, Y., Liu, H., Chen, Y., & Ding, X. (2021). Application of deep learning algorithms in geotechnical engineering: a short critical review. Artificial Intelligence Review, 1–41. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Advanced Techniques for Fake News Detection on Twitter Using NLP and AI: A Comprehensive Review
- Review of Self Compacting Geopolymer Concrete Using Slag Sand as Fine Aggregate
- Performance of Local Construction Contractors – Case Study of Registered Contractors in Monrovia, Liberia
- Cross-Cultural Perspectives on Innovation Management in Multinational Organizations
- Modeling of Reaction Between Dissolved Oxygen (DO) And Biological Oxygen Demand (BOD) in Degradation River