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All Journal Bulletin of Electrical Engineering and Informatics
Abdessamad Belangour
Hassan II University of Casablanca
Electrical & Electronics Engineering

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An AI-enhanced hybrid project management model: a comparative evaluation using the weighted sum model Issam Talkam; Ibrahim Hamzane; Abdessamad Belangour
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10757

Abstract

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.
Co-Authors Ibrahim Hamzane Issam Talkam