The complex environment may greatly challenge the traditional project risk management (PRM) through schedule overrun, cost increase, and emergence of unpredictable risks. The paper is a qualitative research that explores the development of Artificial Intelligence (AI) and sustainability principles that aim at promoting the resilience and performance of PRM. Results indicate that AI technologies, especially ML and predictive analytics, enhance risk management many folds because they process large volumes of data to reveal complicated patterns of risk in the realms of financial, operational, schedule and quality. This allows proactive prediction, effective scenario planning, higher efficiency and data-based decisions. There are, however, hindrances to adoption and they are high, with resistance among stakeholders, measuring the effect of sustainability in terms of risk and more importantly, human factors, being a major factor. Ethical concerns exist due to strategic commitment of gaps, poor communication, inadequate training, operational failures and biases (human and algorithmic) that critically mediate success.
Mariam Al Kuwaiti (Sat,) studied this question.
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