Beyond the Five-Year Threshold: AI as a Construction Enterprise Survival Catalyst in the Developing Economy
Abstract
Purpose – The purpose of this paper is to provide insights into why construction companies fail or cannot move past the five years since their establishment by examining failures at both the individual and organizational levels and using AI as a tool to achieve productivity.
Design/methodology/approach (SLR) – A systematic search has yielded 70 articles between 2019 and 2026 that met the inclusion criteria concerning construction failure, small business failure in general, and AI in construction literature; altogether, 15 of them, which were relevant from a theoretical perspective and had a high number of citations were analyzed in the context of organizational life cycle and resource-based view theories.
Theoretical implications – The theories of organizational life cycle and resource-based view have been expanded through this research.
Findings – The cash flow problem, employee turnover, and rigidity of management structures are the causes of a high failure rate after the first step; companies surviving past year five tend to survive with a high success rate, and AI will help with these issues.
Originality/value – Proposes a Post-Startup Productivity Sustainability (PPS) model. This is the first framework linking AI capability maturity to construction enterprise life-cycle stage.
Further research – There is a need for longitudinal, firm-level studies that test the model across developing and developed construction markets.
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References
Adebayo, Y., Udoh, P., Kamudyariwa, X. B., & Osobajo, O. A. (2025). Artificial intelligence in construction project management: A structured literature review of its evolution in application and future trends. Digital, 5(3), 26. https://doi.org/10.3390/digital5030026
Adebowale, O., & Agumba, J. (2023). Artificial intelligence technology applications in building construction productivity: A systematic literature review. Acta Structilia, 30(2), 1–29.
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, 101827.
Ayodele, O. A., Chang-Richards, A., & González, V. (2019). Factors affecting workforce turnover in the construction sector: A systematic review. Journal of Construction Engineering and Management, 146(2), 03119010. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001725
Azeem, M., & Khanna, A. (2024). A systematic literature review of startup survival and future research agenda. Journal of Research in Marketing and Entrepreneurship, 26, 111–139. https://doi.org/10.1108/JRME-11-2022-0140
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Bushe, B. (2019). The causes and impact of business failure among small to micro and medium enterprises in South Africa. Africa’s public service delivery and performance review, 7(1), 1-26.
Chen, X., Xu, Y., & Li, H. (2023). Artificial intelligence-enabled predictive analytics for construction project success. Engineering, Construction and Architectural Management.
Cheraghali, H., & Molnár, P. (2023). SME default prediction: A systematic methodology-focused review. Journal of Small Business Management. https://doi.org/10.1080/00472778.2023.2277426
Churchill, N. C., & Lewis, V. L. (1983). The five stages of small business growth. Harvard Business Review, 61(3), 30–50.
Darko, A., Chan, A. P. C., Owusu-Manu, D. G., & Edwards, D. J. (2020). Drivers for implementing smart technologies in construction. Journal of Construction Engineering and Management, 146(7).
Egwim, C. N., Alaka, H., Demir, E., Balogun, H., Olu-Ajayi, R., Sulaimon, I., Wusu, G., Yusuf, W., & Muideen, A. A. (2024). Artificial intelligence in the construction industry: A systematic review of the entire construction value chain lifecycle. Energies, 17(1), 182. https://doi.org/10.3390/en17010182
eSUB. (2025). Construction management software. https://esub.com/. Retrieved July 24, 2026, from https://esub.com/
Gaskins, T. J. (2019). Strategies for small business survival for longer than 5 years. Walden
Geng, L., Ma, M., Osei-Kyei, R., Jin, X., & Shrestha, S. (2025). A review of employability skills for graduates in the construction sector. Higher Education, Skills and Work-based Learning, 15(7), 153–170. https://doi.org/10.1108/HESWBL-08-2024-0242
Greiner, L. E. (1972). Evolution and revolution as organizations grow. Harvard Business Review, 50(4), 37–46.
Hanaysha, J. R., Al-Shaikh, M. E., Joghee, S., & Alzoubi, H. M. (2022). Impact of innovation capabilities on business sustainability in small and medium enterprises. FIIB Business Review, 11(1), 67-78.University.
Hosseini, M. R., Chileshe, N., Udeaja, C., & Baroudi, B. (2021). Artificial intelligence adoption in construction: Opportunities and challenges. Engineering, Construction and Architectural Management.
Jayasekara, B. E. A., Fernando, P. N. D., & Ranjani, R. P. C. (2020). A systematic literature review on business failure of small and medium enterprises (SME). Journal of Management, 15(1), 1–15.
Lu, Q., Xue, F., Xu, X., & Zhao, L. (2021). Intelligent construction and digital transformation: A review. Automation in Construction, 132, 103960.
Maalek, R. (2024). Integrating generative artificial intelligence and problem-based learning into the digitization in construction curriculum. Buildings, 14(11), 3642. https://doi.org/10.3390/buildings14113642
Mohammed, A., Ibrahim, Y., & Othman, I. (2024). Artificial intelligence adoption and organisational resilience in construction SMEs. Buildings.
Nozari, H., Movahed, A. B., & Szmelter-Jarosz, A. (2026). Review of the effects of digital transformation on the construction industry. Journal of the Knowledge Economy.
Obi, L. I., Osuizugbo, I. C., & Awuzie, B. O. (2025). Closing the artificial intelligence skills gap in construction: Competency insights from a systematic review. Results in Engineering, 27, 106406. https://doi.org/10.1016/j.rineng.2025.106406
Olatunde, N. A., Anugwo, I. C., Awodele, I. A., Ramabodu, M. S., & Adegoke, B. F. (2026). Unpacking trends in artificial intelligence research in the construction industry: A bibliographic review. Frontiers in Built Environment.
Pan, Y., & Zhang, L. (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122, 103517. https://doi.org/10.1016/j.autcon.2020.103517
Qadir, S., Alawag, A. M., Baarimah, A. O., Zia, N., Alajmi, M., Almujibah, H. R., & Alyami, H. (2025). The role of digital technologies in enhancing construction project management. Scientific Reports.
Taiwo, R., Bello, I. T., Abdulai, S. F., Yussif, A.-M., Salami, B. A., Saka, A., & Zayed, T. (2024). Generative AI in the construction industry: A state-of-the-art analysis. arXiv.
Tambunan, T. (2019). Recent evidence of the development of micro, small and medium enterprises in Indonesia. Journal of Global Entrepreneurship Research, 9(1), 18.
Tjebane, M. M., Musonda, I., & Okoro, C. (2022). Organisational factors of artificial intelligence adoption in the South African construction industry. Journal of Construction Project Management and Innovation.
Wang, Z., Barak, R., Sacks, R., Yevu, S. K., Bentur, A., & Hadjidemetriou, G. M. (2026). Construction productivity and digital technologies. Automation in Construction, 183, 106768.
Zhang, J., Teizer, J., Lee, J.-K., & Eastman, C. (2022). Artificial intelligence applications for construction safety management: A review. Automation in Construction.
Zhao, Z. (2024). Applications of artificial intelligence in the AEC industry: A review and future outlook. Journal of Asian Architecture and Building Engineering, 23(5), 1672–1688. https://doi.org/10.1080/13467581.2024.2343800
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