An Evaluation of The Current State of Risk Management Practices and The Level of Awareness on Machine Learning Among Information Technology (IT) Project Managers

Nmelichukwu Ebubechukwu Nwankwo; Benedict Amade; Ibeawuchi Ifeanyi Echeme; Gregory Chimere Enyinna

doi:10.56201/rjpst.vol.9.no9.2026.pg99.110

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An Evaluation of The Current State of Risk Management Practices and The Level of Awareness on Machine Learning Among Information Technology (IT) Project Managers

Nmelichukwu Ebubechukwu Nwankwo, Benedict Amade, Ibeawuchi Ifeanyi Echeme and, Gregory Chimere Enyinna

Abstract

This study aims to leverage machine learning (ML) for adaptive risk management in Nigerian information technology (IT) projects. The study's specific goal was to assess the present state of risk management procedures and ML awareness among IT project managers in Nigeria's Abuja, Lagos, and Rivers states. The study used snowballing and purposive selection strategies to pick 900 respondents from a finite population of 1000 practitioners, utilising the Cochran's method for sample size determination. The data collection and survey instrument included a well-structured questionnaire, performance statistics, and IT project site visits. The acquired data was presented in the form of frequency distribution tables, charts, and figures using descriptive statistical methods, specifically IBM SPSS Statistics version 26.0. Mean score ranking and the relative importance index were employed to evaluate the study's primary objectives. The study's findings indicate that risk management is still mainly immature and informal in Nigerian IT projects. The utilisation of quantitative risk analysis techniques (MIS=2.45) and specialised software tools (MIS=2.35) was extremely low, indicating that qualitative methods, manual procedures, and no analytical approaches are being used. Moderate scores for risk identification (MIS=3.15) and response planning (MIS=3.05) indicate a basic understanding of risk management concepts, whereas low scores for formal processes (MIS=2.85) and learning from previous projects (MIS=2.75) indicate a systematic lack of institutionalising risk management practices. The study also found that the majority of IT project managers are at the "Aware" or "Knowledgeable" level of ML, but only 14.7% have advanced to the "Applied" level, demonstrating a significant gap between knowledge and actual application. This report suggests that IT organisations use complete risk management frameworks, such as explicit policies, risk registers, and regular evaluations. Government and professional bodies should encourage ML adoption and training programs, which will lead to better risk management in IT projects.

Keywords

Machine learningInformation technologyRelative importance indexProjects.

References

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An Evaluation of The Current State of Risk Management Practices and The Level of Awareness on Machine Learning Among Information Technology (IT) Project Managers

Abstract

Keywords

References

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