Project success requires team commitment, which is a product of an encouraging culture of cooperation and teamwork among project team members. The research work aims to ascertain which components of team commitment affect the performance of construction projects in Nigeria. The research adopted a quantitative design where questionnaires were used for data collection. Out of 1233 questionnaires distributed, 975 were received with valid responses and used for data analysis. Data were analysed descriptively using percentage, mean score, and relative agreement index. The study showed the factors of team commitment having an effect on project performance, as rated by the respondents, to be: Normative component: “Project team members owe a great deal to this organisation”; “Members of the project team do not feel it is right to quit the project before completion”; “This organisation has a great deal of personal meaning for project team members”. Affective component: “This organisation deserves the loyalty of project team members”; “The project team considers the team’s problems as their own. Then, “One of the few negative consequences of leaving this organisation will be the scarcity of available alternatives” is for continuance. In conclusion, the emotional attachment of the team members and sense of obligation to the project team and construction organisation are the driving forces behind pushing for the successful outcome of projects within the Nigerian construction industry.

The artificial intelligence (AI)-based architect’s profile’s selection (simply iSelection) uses a polymathic mathematical model and AI-subdomains’ integration for enabling automated and optimized human resources (HR) processes and activities. HR-related processes and activities in the selection, support, problem-solving, and just-in-time evaluation of a transformation manager’s or key team members’ polymathic profile (TPProfile). Where a TPProfile can be a classical business manager, transformation manager, project manager, or an enterprise architect. iSelection-related selection processes use many types of artifacts, like critical success factors (CSF), AI-subdomain’ integration environments, and an enterprise-wide decision-making system (DMS). iSelection focuses on TPProfiles for various kinds of transformation projects, like the case of the transformation of enterprises’ HRs (EHR) processes, activities, and related fields, like enterprise resources planning (ERP) environments, financial systems, human factors (HF) evolution, and AI-subdomains. The iSelection tries to offer a well-defined (or specific) TPProfile, which includes HF’s original-authentic capabilities, education, affinities, and possible polymathical characteristics. Such a profile can also be influenced by educational or training curriculum (ETC), which also takes into account transformation projects’ acquired experiences. Knowing that selected TPProfiles are supported by an internal (or external) transformation framework (TF), which can support standard transformation activities, and solving various types of iSelection’s problems. Enterprise transformation projects (simply projects) face extremely high failure rates (XHFR) of about 95%, which makes EHR selection processes very complex.