Artificial Intelligence (AI) and Operational Performance of Selected Manufacturing Firms in Lagos State, Nigeria

This study examined the effect of Artificial Intelligence techniques on Operational Performance of manufacturing firms in Lagos State, Nigeria. Using a Survey Research Design, data were collected from 250 respondents and analysed with SPSS using regression analysis. The findings revealed that Machine Learning significantly affects Operational Efficiency (β = 0.558, p < 0.05), Deep Learning significantly affects Product Quality and Defect Detection (β = 0.527, p < 0.05), and Computer Vision significantly affects Waste Reduction and Quality Control (β = 0.495, p < 0.05). The study concluded that Artificial Intelligence significantly enhances operational performance. It was recommended that manufacturing firms should invest in ML, DL, and CV technologies, while government should provide incentives and training to support AI adoption in the manufacturing sector.

Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Operational Performance and Manufacturing

1. Arakpogun, E. O., Elsahn, Z., Olan, F., & Elsahn, F.(2021). Artificial Intelligence in Africa: Challenges and Opportunities. The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success, 375-388. [Google Scholar] [Crossref]

2. Atalay, M., Anafarta, N. & Sarvan, F. (2013). The Relationship between Innovation and firm performance: An Empirical evidence from Turkish automotive supplier industry. Proceeding Social and Behavioural Sciences, (75), 226 – 235. [Google Scholar] [Crossref]

3. Audu & Aziwe (2025) Artificial Intelligence and the Performance of Manufacturing Firms in North-Central Nigeria: Reward System as the Moderator. International Journal Of Research And Innovation In Social Science, 9(I), 1739-1755. DOI: 10.47772. [Google Scholar] [Crossref]

4. Burian, J. (2021). The complex choreography of supply chain resilience. Retrieved fromhttps://www.industryweek.com/supplychain/article/21163467/supply-chain-resilienceis-amultilevel-challenge. On July 11, 2026. [Google Scholar] [Crossref]

5. Chan, C. M., Teoh, S. Y., Yeow, A., & Pan, G. (2019). Agility in responding to disruptive digital innovation: Case study of an SME. Information systems Journal, 29(2), 436-455. [Google Scholar] [Crossref]

6. Dawes, J. (1999). The Relationship between subjective and objective company performance Measures in market orientation research: further empirical evidence, marketing Bulletin, 10 pp. 65 – 75. https://doi.org/10.3390/su141912760. [Google Scholar] [Crossref]

7. Ebuka, Emmanuel & Idigo (2026). Artificial Intelligence as a catalyst for the Sustainability of Small and Medium Scale Business (SMEs) in Nigeria. Annals of Management and Organisation Research 5(1), 1-11. [Google Scholar] [Crossref]

8. Ekanem, G. U., Effiong, M.B., Ekanem, U. A & Udom, K. O. (2026) Artificial Intelligence and Employee Performance In Manufacturing Firms In South-South, Nigeria. International Journal Advanced Research Publications, 2(5), 1-24. [Google Scholar] [Crossref]

9. Financial Stability Board (2017). Some Studies in Machine Learning using the game of checkers. IBM Journal of Research And Development, 11(3), 170-179. [Google Scholar] [Crossref]

10. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. [Google Scholar] [Crossref]

11. Harris, L. C. (2001). Market orientation and performance: Objective and Subjective empirical evidence from UK Companies, Journal of Management Studies, 38(1), 17 – 43. [Google Scholar] [Crossref]

12. Himanshu, A., Chandrika, P. D., & Rabindra, K. S.(2022). Does Artificial Intelligence Influence the Operational Performance of Companies? A Study Atlantis Highlights in Social Sciences, Education and Humanities, 2, 59-69. [Google Scholar] [Crossref]

13. Jabłońska, M. R., & Pólkowski, Z. (2017). Artificial Intelligence-Based Processes In Smes. Studies & Proceedings of Polish Association for Knowledge Management (86). [Google Scholar] [Crossref]

14. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. [Google Scholar] [Crossref]

15. McKinsey. (2026). Fintech industry trends: AI, digital assets, and more. Retrieved from www [Google Scholar] [Crossref]

16. Reuters. (2026). Wall Street banks ramp up digital assistants in bid to win productivity race. [Google Scholar] [Crossref]

17. Rai, A., Constantinides, P., & Sarker, S. (2019). Next generation digital platforms: toward human-AI hybrids. MIS quarterly, 43(1), iii-ix. [Google Scholar] [Crossref]

18. Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson. [Google Scholar] [Crossref]

19. Szeliski, R. (2022). Computer Vision: Algorithms and Applications. Springer. [Google Scholar] [Crossref]

20. Udeogu, A.C; Okoye, I.E (2024). Artificial Intelligence and Competitive Advantage of Micro, Small & Medium Enterprises (MSMEs) in Anambra State. Cross Current InternationalJournal of Economics, Management and Media Studies,6 (1) 1-9. [Google Scholar] [Crossref]

21. Udeogu, A.C; Okoye, I.E (2024). Artificial Intelligence and Competitive Advantage of Micro, Small & Medium Enterprises (MSMEs) in Anambra State. Cross Current InternationalJournal of Economics, Management and Media Studies,6 (1) 1-9. [Google Scholar] [Crossref]

22. Ulas, D. (2019). Digital Transformation Process and SMEs. Procedia computer science, 158, 662-671. doi:https://doi.org/10.1016/j.procs.2019.09.101. [Google Scholar] [Crossref]

23. Ulrich, P., Frank, V., & Kratt, M. (2021). Adoption of Artificial Intelligence Technologies in German SMEs-Results from an Empirical Study. Paper presented at the PACIS. [Google Scholar] [Crossref]

24. Wechie & Opigo (2020) Artificial Intelligence and Organisational Performance of Manufacturing Firms in PortHarcourt, Nigerian. Journal of management Science. Retrieved from www. rsisinternational.org on 10 June, 2026. [Google Scholar] [Crossref]

25. Yulia, S., & Wamba, S. F. (2022). Artificial Intelligence, Firm Resilience to Supply Chain Disruptions, and Firm Performance. Proceedings of the 55th Hawaii International Conference on System Sciences. URI: https://hdl.handle.net/10125/80059. [Google Scholar] [Crossref]

26. Zhao, X., & Wang, Y. (2024). Computer Vision and Machine Learning Approaches for Defect Detection in 3D-Printed Cementitious Materials. MDPI. Retrieved from www.Reseaercgate.net on June10, 2026. [Google Scholar] [Crossref]

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