AI-Enhanced Strategic Project Planning and Project Success in Institutionsin Mogadishu, Somalia

Abstract

This paper examines the relationship between AI-enhanced strategic project planning and project success in institutions operating in Mogadishu, Somalia. Drawing on a quantitative survey of 109 project practitioners from government agencies, non-governmental organizations (NGOs), and private sector organizations, the study uses multiple linear regression analysis to evaluate how AI tools can optimize scope, time, cost, risk, and resource management in a postconflict, resource-constrained environment. The overall regression model was statistically significant, F(3, 105) = 36.42, p < .001, with the strategic planning dimensions collectively accounting for 51.0% of the total variance in institutional project success (R2 = 0.510, adjusted R2 = 0.496). Within the simultaneous model, strategic time and cost planning emerged as the strongest independent predictor of project success (β = 0.354, t = 4.222, p < .001), followed by risk and resource planning (β = 0.226, p = .009) and scope planning (β = 0.192, p = .017). These results underscore the operational necessity of grounding AI-enabled forecasting, predictive scheduling, and real-time contingency structures in local Mogadishu realities. The paper concludes that the adoption of intelligent project management tools can bridge the institutional “strategic gap” by moving organizations from reactive crisis mitigation toward adaptive, data-driven, and context-sensitive “adaptive realism.” The findings offer evidencebased, actionable frameworks for practitioners, policymakers, and academic institutions seeking to leverage artificial intelligence for resilient project delivery in fragile and conflictaffected settings.