Integrating Artificial Intelligence in Project Risk Management: Enhancing Decision-Making Across Industries

The application of artificial intelligence (AI) is highly relevant in project management, and one of the areas that may benefit from AI is risk identification. AI tools in project risk management promise to significantly enhance risk identification, evaluation, and management in different industries. Nevertheless, numerous sectors remain skeptical about implementing AI-based risk management systems despite these advantages. This study aims to identify the issues and possibilities related to the implementation of artificial intelligence in managing project risks, especially in industries where reliance on manual processes is still strong. Thereby, the purpose of the research will involve sectors that have not widely adopted AI solutions, such as construction and healthcare.

Analysis of Existing Project Management Literature

Project risk management is one of the important pillars towards the realization of project success. Conventionally, it is built based on pre-defined frameworks, including ISO 31000 and Project Management Institute’s Guide to the Project Management Body of Knowledge (PMBOK), focusing on a step-by-step process of risk identification, risk evaluation, and risk management (Acebes et al., 2024; Project Management Institute, 2021) These frameworks generally segregate risks based on deterministic or probabilistically driven models. While deterministic approaches are more about identifying the precise consequences of each risk factor, probabilistic methods consider uncertainties and the correlation between these risks (Khodabakhshian et al., 2023).

Existing literature shows that tools built and implemented using AI complement traditional risk management by applying large amounts of data to more accurately predict possible risks (Yazdi et al., 2024). For instance, AI has a decision-making capability, and it can sift through data and identify risk factors from past projects, something that cannot be done manually (Adesina et al., 2022). Such improvements are especially useful in the construction and healthcare project sectors, where factors like project uncertainties prevail. However, even now, relying on advanced technologies, many organizations continue using traditional and experience-oriented risk management approaches (Kara et al., 2020). Such a hesitation to integrate AI tools is due to various reasons like inadequate funding, insufficient knowledge and skills in IT, and organizational conservatism (Safaeian et al., 2022). Therefore, there is a research problem of increasing sector-level disparities between those applying AI and those that do not.

Research Problem Background

The slow integration of self-service tools based on artificial intelligence in industries for which manual work is typical has become an emergent issue. For instance, in construction projects, the risks are managed intuitively or indicatively, relying on factors such as the project manager’s experience (Rahimi et al., 2019). Though these methods were probably efficient in the past, they are gradually unable to address the challenges of complex projects. Similarly, risk management in the healthcare arenas of treating patients, constructing facilities and other projects, or procuring information technology for patient care is best served by a more developed, empirical process.

AI-based systems provide opportunities to address these complexities by automating risk assessment and mitigation processes. Since the risks and their impacts entail complexities, automation solutions for assessment and control are needed. They are capable of analyzing large amounts of information, estimating risks, and consulting over measures to be taken. Nevertheless, AI adoption in healthcare and construction has been low, mainly because of a high cost, skill, and organizational commitment to technology (Li et al., 2023). This research problem is significant as it tries to establish the current extent to which organizations can leverage the opportunities in enhanced project risk management.

Research Problem Statement

The research problem underpinning this study is the low uptake of AI-based project risk management tools across various sectors. As it has been established, the application of AI has the potential to improve the practice of risk management in some industries, but new methods are still absent in industries such as healthcare and construction, where the practice is still done manually. More specifically, this research will investigate the opportunities, threats, and challenges of implementing AI in these industries and their implications for project risk management; it will also examine how these impediments can be addressed to better utilize AI tools.

Purpose and Scope

This research aims to identify how AI-based project risk management tools can be best integrated into industries that currently use conventional, paper-based risk management methodologies. The healthcare and construction industries will be chosen for the study since they are highly complex and uncertain, and companies in these fields are slowly adopting AI. Thus, safeguarding risk management in industries and establishing a set of requirements for achieving effective AI is the purpose of the present study.

The research subjects refer to advanced AI technology used in risk management, existing applications of the technology in different industries, and issues and concerns that may arise from AI usage. This research will also explore other ways in which AI can work in conjunction with typical risk management strategies and produce enhanced, factual outcomes for decision-making.

Research Question

The primary research question guiding this study is: How can AI-based project risk management tools be effectively adopted in industries that rely on manual risk management processes, such as healthcare and construction?

Methods

To achieve these objectives, this research will employ both qualitative and quantitative data. The quantitative aspect will be conducted based on data on recent outcomes of projects in industries applying AI-based RM tools against those industries that have not applied such tools. This analysis will use metrics such as delay time, cost overruns, and failure rates with the theme. The qualitative aspect is to include interviews with project managers of industries as a result of this, they will develop perceptions on the various problems and prospects of utilizing AI in project risk management. The study will base its qualitative research approach on generic qualitative research to identify key aspects of how professionals in the industry view the positive and negative elements of using AI tools in risk management.

Theoretical Foundation

The research will employ decision-making theory, especially the decision-making biases affecting risk management choices. As reviewed in the research literature, applied decision-making of individuals frequently incorporates heuristics and biases, which results in less-than-optimal decision results when managing risks (Acebes et al., 2024). AI tools can greatly reduce these biases because they compute data analytically and do not contain human bias. Also, probabilistic risk assessment models will comprise a central part of the theoretical framework, highlighting the significance of factors of uncertainty in predicting risks associated with projects (Khodabakhshian et al., 2023).

Target Population

This research will target project managers, risk management specialists, and decision-makers in industries characterized by high levels of project uncertainty and project management-related complexity, such as health facility and construction projects. The participants will be sourced from organizations with and without AI tools for risk management.

Eligibility Criteria

The study participants should have five or more years of working experience in project or risk management in their respective industries. This criterion helps to select the right participants who can provide sufficient and proper information to help understand the risk management processes in organizations. Moreover, participants need to be directly involved in any decision-making processes concerning risk management within their respective organizations.

Ethical Considerations

This research will follow ethical practices to ensure minimal violation of the participant’s rights. Participants will sign a consent form, and their answers will be encoded to ensure that the participants’ identities cannot be identified. All data collected will remain confined to the file, and only selected scholars will be allowed access to the data. Moreover, self-generated ideas or information from the participants’ organization will not be required to reveal any proprietary or confidential information.

Gaps in the Literature

Even though there is research on the role that AI has in improving project risk management, there is a lack of sufficient material on how these tools can be adopted for different industries, especially those industries that would require hand-operated measures (Modarres, 2020). Further, with regard to the adoption of AI in risk management, there is scant literature on the human factors that can determine its effects. Knowledge of such factors is pertinent in the formulation of strategies to counter AI resistance and enable organizations to harness value-enhancing risk management technologies (Safaeian et al., 2022; Li et al., 2023; Yazdi et al., 2024).

Conclusion

Project risk management can benefit from the integration of AI in project decision-making, and risk management can, therefore, be enhanced further. However, many industries, including healthcare and construction, still use manual risk management techniques, which hampers their opportunities to use AI to its potential. This research seeks to fill the existing gap between industries that have adopted AI and those that have not by establishing the challenges that hinder AI implementation and providing solutions to these challenges. This research will further the knowledge of AI application to specific areas of project management by analyzing the incorporation of different types of AI tools to support the risk management process.

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: Yazdi, M. (eds) Linguistic Methods Under Fuzzy Information in System Safety and Reliability Analysis. Studies in Fuzziness and Soft Computing, vol 414. 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

Modarres, M. (2020). Risk analysis in engineering: techniques, tools, and trends. CRC press.

Project Management Institute (2021). A guide to the project management body of knowledge (PMBOK® Guide) – Sixth Edition.

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

Yazdi, M., Zarei, E., Pirbalouti, R. G., & Li, H. (2024). A comprehensive resilience assessment framework for hydrogen energy infrastructure development. International journal of hydrogen energy, 51, 928-947. https://doi.org/10.1016/j.ijhydene.2023.06.271

ORDER A PLAGIARISM-FREE PAPER HERE

We’ll write everything from scratch

---

Week 2 instructions.

Analyze existing approaches to project risk management literature and provide context for an analysis of project risk management best practices, elements, and techniques. Then identify a research problem in project risk management. Develop a research topic that is narrow enough for a thorough investigation within the size limitations of your project. Summarize the purpose and scope of a research project, methods used, and questions addressed.

Preparation

Complete the following:

• Research existing project management literature. You will need to analyze the literature and provide context for an analysis of project management complexity and application for an organization. See the Sample Research Questions and Issues Log [DOCX] Download Sample Research Questions and Issues Log [DOCX].
• Identify a research problem in project management.
• Develop a research topic that is narrow enough for a thorough investigation within the size limitations of your project.
• Find at least 10 current (less than 5 years old) scholarly references.

Consider a topic that could one day be used for a dissertation or Capstone Project, using a project management lens.

Use the links provided in the resources to ensure your writing and research are of doctoral-level quality.

Important Note: 

To help you understand how to develop a topic statement, problem statement, purpose statement, research questions, a gap in the literature (PhD dissertation), or a gap in practice (DIT capstone), please visit the SOBTH Doctoral Networking and Support: ResourcesLinks to an external site. site where you will find a plethora of resources for generic qualitative inquiry and quantitative regression projects, webinars, PowerPoint presentations, and completed Capstone examples.

PhD learners can also review the Project Plan Guide: PhD Programs [PDF]Links to an external site. to review hypothetical examples of a topic statement, problem statement, purpose statement, and research question.

DIT Capstone learners can also review the Project Plan Guide: Professional Doctorate Programs [PDF]Links to an external site. to review hypothetical examples of a topic statement, problem statement, purpose statement, and project question.

Instructions

In a 3–5 page paper (excluding the title and reference pages), write a topic definition statement for your course project. In your topic definition statement:

• Analyze existing project risk management literature.
• Provide context for an analysis of information project risk management mechanisms for an organization.
• Identify a research problem in project risk management.
• Summarize the purpose and scope of the research project, methods used, and questions addressed.
• Identify key constructs and/or a theoretical foundation, as well as key relationships among the constructs.
• Describe the target population.
• Describe the participants’ eligibility criteria.
• Explain ethical considerations such as participants’ right to privacy, informed consent, and protection from harm.
• Identify gaps in knowledge in the literature (PhD) or gaps in practice (DIT).
• Cite any scholarly or professional resources used.

Include a minimum of ten current scholarly or professional resources (less than 5 years). Use this assignment and course to build up a reference list for your topic; 40 resources are required for your final course project.

Organize your topic definition statement into the following sections:

• Research Topic.
  • Analysis of Existing Project Management Literature.
  • Research Problem Background.
• Research Problem Statement.
• Purpose and Scope.
• Research Question.
• Methods (i.e., generic qualitative inquiry or quantitative regression).
• Theoretical Foundation.
• Target Population.
  • Eligibility Criteria.
  • Ethical Considerations.
• Gaps in the Literature (PhD) or Gaps in Practice (DIT).

Your writing should demonstrate doctoral-level critical thinking skills and a writing style in which sentences are clear, concise, and direct, and provide a well-supported analysis supported by current resources. Use current APA format throughout the paper and format all citations and references using current APA guidelines.