AI-Driven Decision-Making in Project Risk Management
Oriahi, Harrison (2026)
2026
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202602042270
https://urn.fi/URN:NBN:fi:amk-202602042270
Tiivistelmä
The increasing complexity of modern projects and the demand for data-driven decision-making have positioned Artificial Intelligence (AI) as an important enabler in project risk management. Despite its growing adoption, there remains limited understanding of how AI supports decision-making processes and what challenges influence its effective use. This study aimed to examine how AI agents improve decision-making in project risk management, identify the benefits and challenges of their adoption, and compare them with other AI-driven tools.
A quantitative research design was used, involving an online survey that received 206 responses from project professionals across multiple industries. Data were analyzed using descriptive and comparative statistical techniques in Microsoft Excel to explore trends in AI adoption, benefits, and perceived challenges.
The findings show that respondents perceived AI as enhancing the speed, accuracy, and confidence of decision-making in project risk management. However, adoption varied by industry, with IT and construction sectors reporting higher engagement compared to others. Key challenges identified were poor data quality, high implementation costs, limited technical skills, and ethical concerns such as data privacy and accountability. The study concludes that AI serves as a supportive rather than substitutive tool, improving analytical capacity while requiring human oversight and governance.
A quantitative research design was used, involving an online survey that received 206 responses from project professionals across multiple industries. Data were analyzed using descriptive and comparative statistical techniques in Microsoft Excel to explore trends in AI adoption, benefits, and perceived challenges.
The findings show that respondents perceived AI as enhancing the speed, accuracy, and confidence of decision-making in project risk management. However, adoption varied by industry, with IT and construction sectors reporting higher engagement compared to others. Key challenges identified were poor data quality, high implementation costs, limited technical skills, and ethical concerns such as data privacy and accountability. The study concludes that AI serves as a supportive rather than substitutive tool, improving analytical capacity while requiring human oversight and governance.