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Supervisor: Dr. Muhammad Mutasim Billah

 

Application of Artificial Intelligence (AI) in Project Risk Management: Investigating the dynamics of the VUCA world

 

Supervisor: Dr. Muhammad Mutasim Billah Tufail (Associate Professor)

Campus/School/Dept: Management Studies Department, Bahria Business School BUKC

Supervisory Record:     

  • PhD Produced:  (to be provided by PGP office)
  • PhD Enrolled:    03 Nos.
  • MS/MPhil Produced:  05 Nos.
  • MS/MPhil Enrolled:   Nil.

 

Introduction

Project risk management has long been a cornerstone of effective project management practices. As organizations aim to deliver projects successfully in increasingly complex, uncertain, and dynamic environments, effective management of project risks becomes ever more crucial. Project risk management involves identifying, assessing, and mitigating risks that can negatively impact project objectives such as scope, time, cost, and quality (PMI, 2017). It is well-established that an effective approach to risk management can lead to more successful projects by anticipating challenges, mitigating threats, and capitalizing on opportunities (Hillson, 2017). However, managing these risks in traditional project management settings has its limitations, especially when faced with large datasets, complex interdependencies, and the unpredictability of external environments. As such, the discipline of project risk management is evolving, and new technologies, such as Artificial Intelligence (AI), are being integrated into the process to address these challenges.

Artificial Intelligence has emerged as a transformative force across multiple sectors, including project management. AI, encompassing techniques such as machine learning (ML), natural language processing (NLP), and predictive analytics, offers the potential to process large datasets quickly, identify patterns, and generate actionable insights that support decision-making (Moynihan & Pandey, 2021). In the context of project risk management, AI-driven technologies can automate the identification and assessment of risks in real time, enabling project teams to react proactively to evolving risks and uncertainties (Jadhav et al., 2021). The ability of AI to process data at scale and with speed significantly reduces human limitations, such as cognitive biases and the difficulty of managing complex data sets, and enhances the risk management process by offering a level of sophistication that traditional methods cannot match.

AI’s potential in project risk management is particularly valuable in today’s volatile, uncertain, complex, and ambiguous (VUCA) environment. The VUCA world, characterized by unpredictable change, volatility, and complexity, requires organizations to adapt and be agile in their risk management approaches continuously (Bennett & Lemoine, 2014). In such an environment, the pace of change and the unpredictability of external and internal factors create new, often unforeseen risks that traditional risk management practices are ill-equipped to address. AI can help mitigate these challenges by providing predictive models and real-time risk analysis, thereby enabling organizations to remain resilient and adaptive (Brady et al., 2020). However, despite the promising potential of AI in this domain, there is limited empirical research on its practical application in project risk management, and many organizations remain hesitant to fully integrate AI-based risk management tools into their operations. This research seeks to explore how AI can improve project risk management practices by helping identify, assess, and mitigate risks in real-time.

 

Research Objectives

This research aims to explore the potential applications of AI in project risk management and evaluate its effectiveness in identifying, assessing, and mitigating risks in real-time project environments. The specific objectives are:

  1. To examine the various AI-driven tools and technologies that can support risk identification in projects, such as machine learning, predictive analytics, and natural language processing (NLP).
  2. To assess how AI can enhance the risk assessment process by offering more accurate and data-driven insights into potential risks.
  3. To explore how AI can facilitate real-time risk mitigation strategies, enabling project teams to respond proactively to emerging risks.
  4. To identify the challenges, limitations, and barriers to the adoption of AI in project risk management, especially in the context of a VUCA environment.
  5. To provide recommendations for project managers and organizations on how to effectively integrate AI into their risk management practices.

 

Research Questions

To achieve the above objectives, this research will address the following key questions:

  1. What are the potential applications of AI in identifying, assessing, and mitigating project risks?
  2. How can AI tools improve the accuracy and efficiency of risk identification, particularly in complex, data-driven projects?
  3. In what ways can AI-driven tools assist in quantifying and assessing risks in dynamic project environments?
  4. How can AI enhance decision-making by offering real-time insights into emerging risks and suggesting mitigation strategies?
  5. What are the barriers to AI adoption in project risk management, and how can these be overcome to maximize the benefits of AI in a VUCA environment?

The significance of this study lies in its potential to bridge the gap between AI technologies and their application in project risk management. By exploring how AI can improve decision-making, reduce uncertainty, and enhance risk mitigation practices, this research will provide valuable insights for both academics and practitioners in the field of project management. In a VUCA world, where risks are constantly evolving and organizations must remain adaptive to survive, AI offers a powerful tool to support real-time, data-driven decision-making. This research will be critical in helping organizations understand how to leverage AI for better risk management practices, leading to more successful project outcomes and increased organizational resilience.

 

Candidate’s Eligibility Profile:

  1. The applicant must have a MS/MPhil/Equivalent degree in Project Management (thesis-based) with a CGPA > 3.5. Besides, applicants must have a strong background in mathematical and statistical optimization models and related fields.
  2. Candidate must have 1 Impact Factor or 3Y/2X HEC publication as 1st author in the domain of Project Management.
  3. Experience with programming languages such as MATLAB, Minitab, SD Modeling (iThink, Stella) or Python is advantageous. Candidates should thrive in an international environment and have excellent communication skills to actively contribute to team research efforts.
  4. Proficiency in academic writing is essential. We value independence and responsibility while promoting teamwork and collaboration among colleagues.