## 4 Results
### Literature Review
#### 4.2.9 Generic Investigations  
By generic investigations we refer to those studies that deal with AI-based PM in a wider way (i.e., related to all knowledge areas or PDs in general, AI reviews and its adoption in PM, application in a sector, etc. ); this research has been mainly established during the last two years. In recent years, there has been a growing interest in exploring the potential of AI to revolutionize PM. Various scholars have conducted research to analyze the potential and limitations of AI in this field. [PERSON] et al. [136] offered an overview of AI approaches and tools that can be employed for automating tasks in business project management. In another study, [PERSON] et al. [137] presented a framework that defines the fundamental concepts for applying AI to PM, comprising both the requirements of AI application in PM as well as the requirements of PM from AI. Furthermore, [PERSON] et al. [138] carried out a systematic literature review to investigate the potentialities and limitations of AI in project management, highlighting an increased interest in the scientific community in this domain. [PERSON] [139] applied bibliometric analysis to the existing literature on AI in project management and identified emerging trends, including increased automation and data robustness in cost estimation models, intelligent project control systems based on earned value management, and optimization of input factors for effort estimation models. [PERSON] and [PERSON] [140] examined the impact of AI on the future of PM and emphasized the importance of skilled project managers in contributing value to projects through their expertise. They also underscored the significance of possessing both manpower and AI skills to achieve successful project outcomes. Additionally, they suggested that project managers should concentrate on developing skills that AI cannot achieve to remain competitive in the industry. [PERSON] et al. [141] presented survey-based research to explore the expected effect of AI on PM knowledge areas in the next 10 years. Findings reveal that project cost management, project schedule management, and project risk management are likely to be the most impacted by AI, especially in the planning phase for cost, risk, and schedule estimation. On the contrary, results indicated that knowledge areas and processes that require human skills will be the least affected by AI, highlighting the development and management of teams and stakeholder management. Additionally, the work in [142] provided a matrix method to develop purposeful AI use cases in the PM domain, where PM knowledge areas are in columns and AI functions (i.e., predicting and decision making)in rows; in this way, a specific problem related to a PM knowledge area that requires the human ability to be solved would be assigned to an AI function for being solved. Moreover, the article [143] gave insights about the existing application of AI in PM and its future prospect. That work expresses that AI-based PM, along with the use of data collected from projects, improves PM processes and is commonly subjected to ML-based PM. Indeed, it is applied to schedule and cost prediction, assist in project tracking, determine project attributes by NLP, risk and resource management, and chatbots. Furthermore, the essay [144] is about the disruptive potential of AI, together with data analytics, in PM. As stated in that discussion, an AI-assisted PM will likely reduce repetitive PM tasks (such as estimating risks) and automate the tracking of communications among stakeholders. The author highlights that complex IT PM may particularly profit from AI, which may provide task completion estimation, effective task assignment, and advanced visualization techniques for tracing/tracking project processes. Moreover, [PERSON] et al. [145] reviewed a large number of AI learning techniques aimed at PM (e.g., NN, Fuzzy, etc.).