Project Success Assessment based on Machine Learning

In the digital age, more people are getting connected and using digital technology than ever before. Consumers are surrounded by a variety of digital products and service offerings daily. The Information Technology (IT) industry is projected to have an annual growth rate of between 3.7% and 5.4% worldwide and has become one of the world's leading industries. The COVID-19 pandemic in 2020 has catalyzed the growth of the "digital economy" as more organizations are embracing digital transformation in their operations. This trend drives the demand for IT project practitioners. However, the high failure rate of IT projects has caused incredibly huge losses for many organizations. Existing models, tools, and techniques are incapable of predicting success, which could effectively enhance the project governance capability. This concept paper leverages the Machine Learning (ML) model to perform project success assessments. The study begins by reviewing the contributory factors to project failure, project management techniques, and various prediction models. A quantitative survey will be conducted to rank identified project risk factors. An appropriate ML model will be developed and verified for its performance outcomes in project cost and duration prediction. The proposed method is expected to enhance the prediction accuracy significantly compared to the existing project management techniques.