## 5 Discussion  
This section gives observations on the contemporary state of AI-assisted PM based on the aforementioned literature review, followed by literature gap identification and recommendations for future investigation in such field. Bibliometric results show a notable increasing number of high-impact publications related to the AI-PM topic during the last decade. According to the findings, construction is the most impacted sector by AI, which is due to the complexity of the megaproject nature of its projects. Selected studies propose different AI methods to assist different PM processes. The huge potential of AI is remarkably reflected in planning and measurement PDs, where a substantial amount of work has been dedicated to AI-enabled project time forecasting and software effort prediction. Furthermore, several investigations exist pertaining to AI-based uncertainty PD (which includes safety issues in construction), delivery PD (e.g., compliance/conformance checking automation in construction projects), and project work PD (e.g., forecasting the state of the project), while the literature encountered for the team and stakeholder PD are scarcer and diverse respect to AI functions. Moreover, the collected research displays the evolution of AI technologies during the last decade. Under the predominance of ML, while in the initial period single methods are applied (e.g., a ML algorithm), ensemble and hybrid models that combine different algorithms and/or techniques aimed at improving the performance are utilized later (i.e., ML together with heuristics and fuzzy NN). Moreover, DL has become a trend in the last years, which gives solutions to more complex problems and enhances computer vision and NLP. Furthermore, for the validation of the presented AI models project datasets of real projects are often used. Nevertheless, the literature reveals that AI application into real PM scenarios is still on an early stage. We summarize the gaps identified in the literature referring to AI-based PM below:  
**Lack of a DL-based PM:** While the literature has emphasised ML-enabled PM, DL is key for processing complex BD but it has been applied to a limited extent. Therefore, the potential of DL has not been fully considered in the digital PM. **Lack of AI-powered PM proposals in an agile environment:** Despite the fact that a couple of studies discuss AI in agile PM, it is a topic that requires deeper investigation. **Lack of evidence of AI adoption for project managers**: Although AI-enabled PM seems encouraging, its design, standardization, and implementation in project-based firms are still a challenge. Thus, AI adoption in PM is yet to be noted. **Lack of security issues of BD within the AI-PM ecosystem**: The project BD used AI algorithms to assist PM is a major concern. Companies will be affected if data security, privacy, and authentication are not protected. However, we find that data security matters for AI-based BD analytics in the PM context are missing. **Lack of sustainability-aware AI-assisted PM:** Industry 5.0, in line with the United Nations 2030 Agenda for Sustainable Development, highlights the inclusion of sustainability in emerging technology-enabled industries. Nonetheless, we have only identified two works in the AI-PM theme that take into account the sustainability criteria in project evaluation; hence, there is a hole in sustainable AI-based PM. Considering the aforementioned gaps, we provide several recommendations for forthcoming research in the AI-PM field:  
* Regarding AI as an enabler for project BD analytics, the future question is to what extent BD analytics requirements meet the promising features of cutting-edge AI, such as DL;
* Searching for comprehensive solutions to AI-powered agile PM remains a subsequent task;
* An AI-based PM approach will create an environment that will involve both project managers and IT people to work collaboratively to make disruptive AI technologies perform effectively.