AI-Augmented HSE Excellence: Integrating Smart Technologies with the CPMAI Methodology for Proactive Safety Performance
DOI:
https://doi.org/10.54489/ijbas.v5i1.557Keywords:
AI-enabled HSE management, CPMAI framework, Predictive safety analytics, Smart industrial risk management, Socio-Technical Systems Theory, Technology Acceptance Model (TAM)Abstract
AbstractPurpose: The research explores how smart Artificial Intelligence (AI) technologies are integrated in the Cognitive Project Management for AI (CPMAI) methodology to improve Health, Safety and Environment (HSE) performance in industries with high risk. In this research, the gap between AI potential and its implementation in the structured and practical implementation for HSE has been addressed.
Methods: This research adopts a secondary data collection method. 45 Peer reviewed articles, case studies and industry reports published during 2019-2025 has been selected from relevant databases like IEEE Xplore, Scopus and ScienceDirect and data has been analysed through thematic analysis method. Key themes covered in the analysis are key applications of AI, integration framework, principles of project management and theoretical framework.
Findings: The research highlights that AI tools like Machine Learning (ML) is used in predictive analytics, Natural Language Processing (NLP) in incident analysis, Computer Vision in detecting hazards improve proactive risk management. A significant six-phase structure is offered by CPMAI framework which assumes that business objectives and AI projects are aligned, properly governed and follows ethical principles. In the safety metrics, integration of AI and CPMAI brings measurable improvements, it improves environmental compliance and minimise incident rates.
Conclusion and Implications: Combination of AI technologies and the CPMAI methodology change the reactive nature of organisational HSE framework to a proactive one. A strategic framework has been highlighted in this research to adopt such an integrated approach. Theoretically, the research strengthens application of Social-Technical System Theory (STST) and Technology Acceptance Model (TAM) to demonstrate application of AI in HSE. The research has practical implications because it offers actionable insights to the managers to properly implement AI-oriented safety projects. The research highlights the need for a data-driven project management approach to improve AI potential in the HSE to build a sustainable and safe work culture.
References
References
Adio, S. A., Ajirotutu, R. O., Olayiwola, R. K., Erinjogunola, F. L., & Sikhakhane-Nwokediegwu, Z. (2025). Engineering a Safer Future: How HSE Innovation is Transforming Infrastructure and Sustainability. International Journal of Frontline in Engineering and Industrial Revolution, 2 (2), 28-42. DOI: https://doi.org/10.54660/IJFEI.2025.2.2.28-42
Alshriefy, O. T. A. (2024). The Impact of Risk Management on Project Success in the Context of HSE. International Journal of Frontline in Engineering and Industrial Revolution, 2 (2), 28-42.
Alshriefy, O. T. A. (2024). The Impact of Risk Management on Project Success in the Context of HSE. International Journal of Frontline in Engineering and Industrial Revolution , 2(2), 28-42.
Begemann, V., Handke, L., & Lehmann-Willenbrock, N. (2024). Enabling and constraining factors of remote informal communication: a socio-technical systems perspective. Journal of Computer-Mediated Communication, 29 (5), zmae008. https://doi.org/10.1093/jcmc/zmae008 DOI: https://doi.org/10.1093/jcmc/zmae008
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. DOI: https://doi.org/10.1191/1478088706qp063oa
Huang, Y., et al. (2024). Evaluating ChatGPT-4.0’s data analytic proficiency in epidemiological studies. Journal of Global Health, 14, 04070.
Huang, Y., Wu, R., He, J., & Xiang, Y. (2024). Evaluating ChatGPT-4.0’s data analytic proficiency in epidemiological studies: A comparative analysis with SAS, SPSS, and R. Journal of Global Health, 14, 04070. https://doi.org/10.7189/jogh.14.04070 DOI: https://doi.org/10.7189/jogh.14.04070
Kemp, T., et al. (2024). Using socio-technical systems theory to study the health information management workforce in Australian acute hospitals. Social Theory & Health, 22(4), 285-300. DOI: https://doi.org/10.1057/s41285-024-00214-5
Lee, A. T. (2025). A Review of TAM Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology (UTAUT) Frameworks. PMC, 11816427. DOI: https://doi.org/10.3390/healthcare13030250
Liao, J., Huan, X., & Cooper, D. (2025). Bayesian optimal experimental design for intelligent data collection in material flow analysis. Procedia CIRP, 135, 175-180. DOI: https://doi.org/10.1016/j.procir.2025.02.128
Narayanan, M., & Schoeberl, C. (2023). A Matrix for Selecting Responsible AI Frameworks. Center for Security and Emerging Technology. DOI: https://doi.org/10.51593/20220029
Paun, C., Ivascu, C., Olteteanu, A., & Dantis, D. (2024). The main drivers of e-commerce adoption: A global panel data analysis. Journal of Theoretical and Applied Electronic Commerce Research, 19(3), 2198-2217. DOI: https://doi.org/10.3390/jtaer19030107
Silić, M., & Palačić, D. (2024). Risk management model for the application of Artificial Intelligence in occupational health and safety. In 19th International Conference Management and Safety-M&S 2024 (pp. 92-101).
Silić, M., & Palačić, D. (2024). Risk management model for the application of Artificial Intelligence in occupational health and safety. In 19th International Conference Management and Safety-M&S 2024 (pp. 92-101).
Thomas, A. (2024). Digitally transforming the organization through knowledge management: A socio-technical system (STS) perspective. European Journal of Innovation Management, 27(9), 437-460. DOI: https://doi.org/10.1108/EJIM-02-2024-0114
Victor, N. O. C. (2023). How Artificial Intelligence Influences Project Management. Research Square. https://doi.org/10.21203/rs.3.rs-2535611/v1
Victor, N. O. C. (2023). How Artificial Intelligence Influences Project Management. Research Square. https://doi.org/10.21203/rs.3.rs-2535611/v1 DOI: https://doi.org/10.21203/rs.3.rs-2535611/v1
Weidinger, L., Barnhart, J., Brennan, J., Butterfield, C., Young, S., Hawkins, W., ... & Isaac, W. (2024). Holistic safety and responsibility evaluations of advanced AI models. arXiv preprint arXiv:2404.14068.
Xu, S. (2024). A Longitudinal Study on AI Adoption Among Employees. Communication Research, 51(1), 3-31.
