Literature Review: Integrating Artificial Intelligence in Project Risk Management

The application of artificial intelligence (AI) in project risk management has become more crucial in most industries to support decisions, discover more risks, and better manage resources. This literature review outlines ten recent empirical studies providing insights into AI’s effects on risk management approaches, risks, and opportunities, together with minor ethical considerations on AI in construction, healthcare, and supply chain management. All the keywords used in each study, theoretical frame, the purpose of the research, results, conclusions, implications, and ethical concerns are explained, and descriptions must be included on how risk management was reasoned. These studies have shown that AI can act as an assistant during project risk management and revealed the spheres in which the problem of ethical guidelines appears.

Acebes et al. (2024) present a study titled “Beyond Probability-Impact Matrices in Project Risk Management: A Quantitative Methodology for Risk Prioritization.” The key terms of this study included risk prioritization and methods such as probability-impact matrices and quantitative risk models. The authors provide an exposure critique of probability-impact matrices, stating that these tools have shortcomings of not adequately linking risks that are high-impact and more complex. This research introduces a new quantitative model based on probability-impact data to increase the precision of risk prioritization. The research is based on the quantitative prioritization theory of risks that rationalizes allocating scarce resources to counterweighting risks with a higher probability of occurrence. These findings, therefore, suggest that this quantitative model enables the identification of risk priorities that assist in decision-making about such projects. This is an important strategy since it helps managers address high-impact risks, which is one of the key considerations in managing projects. According to the authors, implementing quantitative risk models in project management might enhance the resilience of projects and their results. As a result, they suggest that the approach should be adopted across industries using high-risk projects, particularly in the construction and healthcare industries. However, the study argues that ethical considerations are not at the forefront of the study; rather, the key message that drives ethical project management is that knowledge of the accurate nature of risks is pertinent in shaded and unshaded forms in order to allow project managers to be truthful about risk prioritization.

Adesina et al. (2022) explore the use of fuzzy-expert systems in emergency decision-making in their study “Emergency Decision Making Fuzzy-Expert Aided Disaster Management System.” Some keywords include fuzzy-expert systems, emergency decisions, and disaster management. The research depends on the fuzzy-expert theory that uses a linguistic approach to handling uncertain information, which is vital in time-constrained situations. The present investigation aims to adopt a model suitable for aiding decision-making in certain environments that do not benefit from conventional deterministic models. Thus, the paper shows that the proposed fuzzy-expert system reduces human intervention, minimizing errors, especially in a risky environment. The authors conclude that fuzzy-expert models provide a significant degree of usefulness where timely decisions are needed, including health care and emergency management. In addition, they suggest that these systems should be combined for disaster management and mitigation and that this model will tremendously benefit projects where the risk probability is unpredictable by its very nature. Ethical issues are not at the center of the study, but decreasing decision-making mistakes in emergencies also helps to manage ethical risks because it is based on safety and accuracy.

The scholarly work by Kara et al. (2020) explores data mining, supply chain risk, and artificial intelligence in project risk management. The authors use data mining theory to identify various factors that may point to various threats in the supply chain. Further, the purpose is to develop an effective risk management framework that involves an attempt to manage risks right from their emergence prior to the incidence of the project. The paper’s findings also evidence that data mining increases the effectiveness of managing supply chain disruptions, thus strengthening project stability. The authors’ experiences prove that when data mining is integrated with AI, project managers can gain insight into matters they can proactively manage, especially in industries that rely on supply chains such as manufacturing and logistics. Concerning supply chain risks, they encourage the application of the data mining frameworks across sectors where buildings’ supply-chain networks are prone to unpredictable disruptions and assert that such measures enhance predisposition in project execution. The implications of this non-ethical focus notwithstanding, the study’s conclusions point out that data transparency and accountability are critical, suggesting that ethical handling of data is important when applying artificial intelligence in risk management.

Khodabakhshian et al. (2023) conducted a comparative analysis of deterministic and probabilistic models in their study, “Deterministic and Probabilistic Risk Management Approaches in Construction Projects.” Some key terms considered include deterministic models, probabilistic models, and construction risk management. The authors use comparative risk assessment theory, specifically considering probabilistic models for the case when decision tools involve AI. This research aims to assess the hypothesis that probabilistic models, with the help of AI, offer better risk estimation than deterministic models in construction. The study shows that probabilistic models provide more accurate information regarding risks, and their management is more effective in complex constructions. Conclusively, the authors suggest that industries where projects are complex and associated risk calibrations are challenging should adopt AI-supported probabilistic models. The scholars propose applying these models in construction and note that it is necessary to understand the AI models’ workflow, especially when it comes to discriminating factors that contribute to biased or misleading results. Social, legal, and political concerns are evident from the requirement for algorithm clarity in decision-making involving risk assessment in construction projects through artificial intelligence.

Li et al. (2023), in their research entitled “A Fuzzy Rough Copula Bayesian Network Model for Solving Complex Hospital Service Quality Assessment,” use keywords like Bayesian networks, fuzzy rough copula, and healthcare service quality. This study is based on the Bayesian network theory and involves assessing risk factors related to healthcare quality for patient satisfaction and services. The research aims to provide an example of the capability of the AI-driven Bayesian models to blend qualitative feedback with quantitative feedback. The study’s findings reveal that the Bayesian model is useful in analyzing risks associated with patient care quality and, thus, can be a useful tool in risk management in the health sector. The authors note that AI when implemented alongside Bayesian networks in the healthcare sector, could improve the quality of the service and equalize potential risks. Li et al. (2023) suggest the use of Bayesian models in more practice areas to enhance patient-centeredness in demanding settings. Ethical concerns, therefore, touch on the use of patent data in AI risk assessment and patient privacy and discretion.

A study by Rahimi et al. (2019) focuses on the hybrid construction project risk management approach. The keywords selected for this article include hybrid models, FMEA, and ISO 31000. Housed within the broad umbrella of hybrid risk management theory, the intended research applications proposed in Rahimi et al.’s (2019) paper are FMEA, ISO 31000, and AI-based evolutionary algorithms to improve risk identification in construction projects. From the evaluation of the results, it can be concluded that the hybrid model has a higher accuracy in defining possible risks than the traditional models, which is suitable for creating an elaborate risk management system. According to the authors, industries experiencing changing risks are best served by hybrid models that consider traditional and AI-informed approaches. In construction, they suggest that companies adopt hybrid approaches since they combine conventional methodologies with new AI tools for optimal risk coverage. Although the authors do not give ethical concerns a central position, they suggest that ethical project management is about accurately identifying risk to promote safety.

In their paper titled “Selecting Appropriate Risk Response Strategies Considering Utility Function and Budget Constraints,” Safaeian et al. (2022) also explore the place of utility-based risk response strategies in construction. Some terms include utility-based risk management, budget constraint, and construction risk. The authors use utility theory to analyze risk response strategies by focusing on the business’s operational costs. The research focuses on developing the theoretical framework that will facilitate the assignment of the different utility values by the project managers to arrive at the best decision within the framework of the resource constraint, in this case, the financial constraint. Analysis of the results revealed that by using AI-based utility models, managers can make efficient risk response decisions that incur less cost in terms of utilization of company resources. The authors, therefore, categorically state that in view of the above findings, utility-based models are a viable solution for projects with budget considerations like those in construction. The material indicates the ethical imperative of properly distributing resources as the focus on high utility risk response helps in effective and equitable project management.

The study “Ethics of Artificial Intelligence and Robotics in the Architecture, Engineering, and Construction Industry” by Liang et al. (2024) highlighted keywords like AI ethics, bias, and accountability regarding AEC. The research is based on ethical theory, assessing possible prejudice in AI algorithms and using the concept of the ethical obligation of integration of AI. Thus, the goal is to increase people’s concern about ethical matters, such as to reveal the problem of transparency and accountability for AI decisions. Through the study findings, the participants found that when AI is left unmonitored, prejudice may influence the results, eroding the credibility of projects. The authors revealed that ethical guidelines can help develop fair and accountable AI usage in the project management framework. It calls for setting ethical standards all over AEC to ensure that the incorporation of AI conforms with the general norms envisaged in the sector.

Shamim (2024) examines AI’s impact on project efficiency in his study “Artificial Intelligence in Project Management: Strengthening Efficiency and Decision Making. The area concepts are AI efficiency, decision-making, and project management. The study uses efficiency theory to highlight the possibility of AI in automatically enhancing and predicting project efficiency. The purpose is to understand the existing challenges resulting in delayed work and the effectiveness of AI in managing resource allocation. As highlighted in the study, the efficiency of AI tools increases project time and reduces costs; given this situation, it is possible to conclude that AI and industries requiring fast project completion are symbiotic. The author posits that AI should be adopted hugely for efficiency gains and suggests that it should be applied in businesses where such needs are felt.

In their study, Odejide and Edunjobi (2024) explore theoretical models that enhance decision-making accuracy in project management. The identified keywords are AI models, decision-making, and risk management. The work employs the decision theory to explain how the models fashioned by AI lessen bias in risk assessments, providing an objective approach to decision-making. The findings indicate that deploying AI-based decision models enhances the objectivity and credibility of risk evaluation by substantially eliminating prejudices that may jeopardize project results. In addition, the authors note that integrating advanced decision-making tools based on artificial intelligence into the frames of conventional PM models might contribute to the steadier drive of risk management processes. They suggest that those models are also used in other industries because precise risk assessment is important for every field. The ethical considerations mentioned are the need to avoid allowing AI to take over human input in project management decisions altogether and instead come up with ways where AI can augment human input in such decisions.

Conclusion

This literature review demonstrates how AI has become an invaluable tool in handling risk management within projects across multiple industries in relation to the accuracy, efficiency, and quality of decisions made. This paper presents an overview of the implementations of AI techniques. The models covered in the reviewed papers are quantitative, fuzzy-expert systems, data mining frameworks, hybrid models, and Bayesian networks. All researchers in the reviewed articles stress that the use of AI for risk management offers project managers more accurate, detailed, and enhanced means than conventional ones for identification, evaluation, and control to enhance organizational effectiveness. Moreover, the discussion of ethical aspects by Liang et al. (2024) and Odejide and Edunjobi (2024) reveals that transparency, accountability, and ethical standards should be incorporated into AI solutions to avoid bias and guarantee fairness.

AI adoption helps allocate resources optimally in project risk management and in managing risks effectively and quickly; this is well illustrated by Acebes et al. (2024) and Shamim (2024). However, AI relevance demonstrates flexibility by addressing various project requirements, including budget options (Safaeian et al., 2022) and emergency response applications (Adesina et al., 2022). However, ethical issues must be solved to maintain the reliability of AI and apply it primarily to project management. Overall, these studies call for a proper and ethical approach to managing project risks by adopting AI in project risk management while highlighting AI’s capability to improve project outcomes.

References

Acebes, F., González-Varona, J. M., López-Paredes, A., & Pajares, J. (2024). Beyond probability-impact matrices in project risk management: A quantitative methodology for risk prioritization. Humanities and Social Sciences Communications, 11(1), 1-13. https://doi.org/10.1057/s41599-024-03180-5

Adesina, K. A., Yazdi, M., & Omidvar, M. (2022). Emergency decision making fuzzy-expert aided disaster management system. In Linguistic Methods under Fuzzy Information in System Safety and Reliability Analysis (Vol. 414, pp. 139–150). Springer, Cham. https://doi.org/10.1007/978-3-030-93352-4_6

Kara, M. E., Fırat, S. Ü. O., & Ghadge, A. (2020). A data mining-based framework for supply chain risk management. Computers & Industrial Engineering, 139, 105570. https://doi.org/10.1016/j.cie.2018.12.017

Khodabakhshian, A., Puolitaival, T., & Kestle, L. (2023). Deterministic and probabilistic risk management approaches in construction projects: A systematic literature review and comparative analysis. Buildings, 13(5), 1312. https://doi.org/10.3390/buildings13051312

Li, H., Yazdi, M., Huang, H. Z., Huang, C. G., Peng, W., Nedjati, A., & Adesina, K. A. (2023). A fuzzy rough copula Bayesian network model for solving complex hospital service quality assessment. Complex & Intelligent Systems, 9(5), 5527-5553. https://doi.org/10.1007/s40747-023-01002-w

Liang, C. J., Le, T. H., Ham, Y., Mantha, B. R., Cheng, M. H., & Lin, J. J. (2024). Ethics of artificial intelligence and robotics in the architecture, engineering, and construction industry. Automation in Construction, 162, 105369. https://doi.org/10.48550/arXiv.2310.05414

Odejide, O. A., & Edunjobi, T. E. (2024). AI in project management: Exploring theoretical models for decision-making and risk management. Engineering Science & Technology Journal, 5(3), 1072-1085. https://doi.org/10.51594/estj.v5i3.959

Rahimi, Y., Tavakkoli-Moghaddam, R., Iranmanesh, S. H., & Vaez-Alaei, M. (2019). Hybrid approach to construction project risk management with simultaneous FMEA/ISO 31000/evolutionary algorithms: Empirical optimization study. Journal of construction engineering and management, 144(6), 04018043. https://doi.org/10.1061/(asce)co.1943-7862.0001486

Safaeian, M., Fathollahi-Fard, A. M., Kabirifar, K., Yazdani, M., & Shapouri, M. (2022). Selecting appropriate risk response strategies considering utility function and budget constraints: A case study of a construction company in Iran. Buildings, 12(2), 98. https://doi.org/10.3390/buildings12020098

Shamim, M. M. I. (2024). Artificial Intelligence in Project Management: Enhancing Efficiency and Decision-Making. International Journal of Management Information Systems and Data Science, 1(1), 1-6. https://doi.org/10.62304/ijmisds.v1i1.107

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Instructions

Search the literature for scholarly or peer-reviewed articles on your research topic/problem statement. Locate 10 empirical studies (each must be under five years old) addressing your selected issue. At least two of the 10 articles should have an ethical issue described and at least two of the 10 articles should discuss risk management of a project. Describe what you learned, how it applies to your research topic/problem statement, and what applications apply to your topic. Summarize the methodology, and research results, and provide an analysis of the empirical studies. Draw conclusions for the findings in your research.

Summarize each article. You may want to capture the following information with a tool such as One Note or RefWorks for later use in your doctoral project. Use this assignment and course as a way to build up a reference list for your topic.

Write a report/review of the literature you find that provides the following:

• Key words in title and abstracts.
• Theory referenced.
• Purpose of research.
• Results.
• Conclusions.
• Implications and recommendations.
• Any ethical implications that exist as a result of the study reviewed toward project management.
• Description of how risk management processes were discussed, analyzed, or studied in the literature reviewed.

This is the second section of the final project and should be presented in paragraph format. In addition, be sure to:

• Use current APA edition formatting for all references.
  

  Literature Review: Integrating Artificial Intelligence in Project Risk Management

Your assignment will be scored on the following criteria:

1. Analyze scholarly, peer-reviewed, empirical research articles addressing a project management problem.
2. Summarize the topic, methodology, and research results of each article addressing the project management issue.
3. Draw conclusions for the findings in the research and how those findings relate to your projected study topic.
4. Provide a description of how risk management processes were discussed, analyzed, or studied in the literature reviewed.
5. Discuss the ethical implications within two of the studies reviewed.
6. Communicate in a professional manner that adheres to APA guidelines, with outstanding writing skills indicative of doctoral-level work.

Additional Requirements

• Written communication: Ensure written communication is free of errors that detract from the overall message.
• Number of resources: At least 10 current scholarly or professional resources. Formatting: Resources and citations are formatted according to current APA guidelines for style and formatting.
• Length: 7–9 double-spaced pages, excluding the title page and reference pages. Include a title page and reference page.
• Font and font size: Times New Roman, 12 point.