An AI-enhanced hybrid project management model: a comparative evaluation using the weighted sum model - Issam Talkam; Ibrahim Hamzane; Abdessamad Belangour (Institute of Advanced Engineering and Science, 2026)
Projects today face increasing complexity, uncertainty, and regulatory constraints that often limit the effectiveness of traditional and strictly Agile project management (PM) methods. Current hybrid methods remain largely theoretical and lack integrated decision-support tools that combine predictive analytics with transparent multi-criteria evaluation. To address this gap, this paper presents an artificial intelligence (AI)-enhanced hybrid project management model (AI–HPMM) that combines AI functionalities with the weighted sum model (WSM) to support adaptive, data-driven, and transparent decision-making. A systematic literature review was conducted according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines (2019–2025), identifying ten success factors aligned with project management body of knowledge (PMBOK) domains. These factors were weighted using WSM to compare Agile, Waterfall, and AI-enhanced hybrid methods. AI–HPMM achieved the highest WSM score (9.96/10), outperforming Agile (7.66/10) and Waterfall (7.90/10). The reviewed literature also reports a 15% reduction in schedule deviations, a 12–20% improvement in resource efficiency, and a 25–30% increase in decision-making speed. Overall, the proposed model improves adaptability, transparency, and responsiveness in complex and dynamic project environments.