Enhancing Project Management Performance through Optimal Human-Artificial Intelligence Collaboration

Abstract

Abstract The emergence of collaboration between humans and artificial intelligence (AI) is a promising approach to enhancing team performance. This study bridges the gap in literature by developing a Human-Generative AI Collaboration Model for project teams in project management and determining criteria for measuring the performance of human-generative AI project teams. The study was conducted in 3 Phases. In Phase 1, the study adopted mixed methods, starting with conducting a systematic literature review to explore collaboration theories. The study then employed several quantitative techniques by developing and following a text-mining framework to identify and prioritize the main factors of team formation, solving the team formation problem for traditional teams. Moreover, the study explored generative AI capabilities that support project teams through a qualitative method and determined the main capabilities through a quantitative method by employing Machine Learning clustering algorithms. By considering how the team formation problem for traditional teams is solved and the main capabilities of generative AI, the study solves the human-generative AI team formation problem and hence builds a Human-Generative AI Project Team Formation Model. In Phase 2, by adopting mixed methods, the study first explored models for stages of team development to determine the behaviors and attitudes of humans when working in teams and studied how the main characteristics and abilities of generative AI support humans and compensate for their drawbacks in performance in each of the stages. The study builds the Stages of Human- Generative AI Project Team Development Model and compares it with the stages of the team development models for traditional teams. By quantifying the human behaviors and attitudes as well as the generative AI characteristics and abilities according to their positive or negative influence on the performance of project teams, the study confirms that the performance of teams consisting of humans and generative AI supersedes traditional human teams when working on projects. By combining the two proposed models of Phase 1 and Phase 2, the study develops the Human-Generative AI Collaboration Model for project teams to enhance the collaboration between humans and generative AI when working in project management by improving the team’s performance, and hence attaining project success. Finally, in Phase 3, qualitative data was collected to establish key performance indicators for measuring the performance of human-generative AI project teams in terms of efficiency and effectiveness. The study’s main contribution lies in the development of the Human-Generative AI Collaboration Model, which serves as an extensive framework for supporting the teamwork between humans and generative AI in projects, from team formation through the stages of team development, and the establishment of the Key Performance Indicators for assessing the performance of humans-generative AI project teams.