## 2 Literature Review
### Project Management and Machine Learning  
Project management is the discipline of planning, organising, and overseeing resources to achieve specific goals within constraints of time, cost, and scope [8; 9]. Key activities include scheduling, resource allocation, risk management, and performance monitoring, all of which generate structured and unstructured data amenable to ML analysis. Machine learning--a branch of artificial intelligence--enables systems to learn from data and make predictions or decisions without explicit programming [10; 11]. Standard techniques include neural networks, support vector machines, decision trees, and natural language processing. Project management and machine learning have increasingly intersected, transforming how projects are planned, executed, and controlled. Machine learning now plays a crucial, complementary role in project management, especially in handling complexity, uncertainty, and large data volumes. At the same time, demands have shifted toward more data-driven, automated, and explainable solutions. Over the past decades, project management has transformed from a predominantly manual, expertise-driven discipline to a data-centric, automated, and transparent practice by integrating machine learning techniques (see Table 1). In the pre-ML era, teams depended on expert judgment, static planning tools, and manual risk assessments, often resulting in inefficiencies and elevated failure rates owing to limited analytical capabilities [8]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Era/Trend** & **Key Demands/Changes** & **ML Contribution** \\ \hline Pre-ML & Manual planning, expert judgment & Limited \\ \hline Early ML Adoption & Predictive analytics, risk modelling & Forecasting, risk reduction \\ \hline Current (AI/Big Data) & Real-time, explainable, automated, scalable & Explainability, automation, and big data handling \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evolution of machine learning in project management over time. The advent of machine learning introduced predictive analytics and pattern recognition functionalities, which enabled more accurate forecasts of schedules and costs, automated decision support for risk modelling, and optimised resource allocation [12]. Consequently, project managers gained the ability to anticipate and mitigate risks more effectively, thereby reducing overruns and improving overall resource utilisation. More recently, the focus has shifted toward explainability and transparency, with methods such as SHAP values providing insights into the factors driving model predictions and supporting forward-looking and retrospective analyses. This explainable paradigm not only fosters trust in automated systems but also empowers managers to validate or override algorithmic recommendations when necessary [13]. In project management, ML is increasingly used to:  
* Improve Forecasting: Enhance predictions of timelines, costs, and risks, reducing overruns and delays [8; 14; 15]. * Optimise Resources: Automate routine tasks such as team assignments and workload balancing, increasing efficiency and freeing managers for strategic activities [16]. * Assess Risks: Identify and prioritise potential failures early in the project lifecycle, especially in complex or uncertain environments [8; 9]. * Support Decisions: Derive actionable insights from large, multimodal datasets--combining numerical, textual, and sensor data--to inform faster, data-driven choices [4; 10]. * Enhance Explainability: Leverage techniques like SHAP values to interpret model outputs, helping stakeholders understand driving factors behind project outcomes [13; 17; 18]. Collectively, these developments illustrate how machine learning has assumed complementary roles in data-driven decision making, risk and resource management, and the automation of routine tasks, thereby freeing managers to concentrate on strategic activities (see Table 2).