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AI in Quick Commerce: Technologies, Models, and Market Drivers

AI in Quick Commerce: Technologies, Models, and Market Drivers
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Author(s): Zokir Mamadiyarov (Termez University of Economics and Service, Uzbekistan), Sindor Sapaev (Urgench State University, Uzbekistan), Gularam Masharipova (Alfraganus University, Uzbekistan)and Munir Ahmad (Survey of Pakistan, Islamabad, Pakistan)
Copyright: 2026
Pages: 26
Source title: Harnessing AI for Point-of-Sale Optimization
Source Author(s)/Editor(s): Nozha Erragcha (University of Jendouba, Tunisia)and Maher Toukabri (Northern Border University, Saudi Arabia & University of Jendouba, Tunisia & Laboratory ARBRE, University of Tunis, Tunisia)
DOI: 10.4018/979-8-3373-4392-1.ch002

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Abstract

This chapter explores how AI underpins the operational backbone of Quick commerce (Q-commerce), driving innovation and redefining convenience in modern digital retail. Q-commerce represents a transformative shift in the retail and e-commerce landscape, emphasizing ultra-fast delivery within minutes. Driven by rising consumer expectations for instant gratification and seamless service, Q-commerce has gained momentum in urban areas, especially for groceries, personal care, and medicines. Central to its success is the integration of Artificial Intelligence, which enables real-time demand forecasting, dynamic route optimization, micro-inventory management, and personalized customer interactions. AI technologies such as machine learning, predictive analytics, and computer vision empower Q-commerce platforms to operate with precision, speed, and scalability.

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