Abdullah Mohammad Khorami, Chief Business Officer, Etihad Salam Telecom Company.
The integration of AI in waste management is transforming waste segregation processes through automation and precision. Smart systems, supported by advanced telecommunications infrastructure, are reshaping how waste is collected, sorted, and processed, bringing new efficiency to an industry ready for change.
These technological advancements arrive at a crucial moment as the waste management sector embraces digital solutions to address growing environmental challenges.
The scale of Saudi Arabia’s waste management challenge demands innovative solutions aligned with Vision 2030’s sustainability goals. The country generates more than 110 million tonnes of waste annually, with nearly half concentrated in three major cities – Riyadh (21 per cent), Jeddah (14 per cent), and Dammam (8 per cent). The environmental impact is substantial, with the National Centre for Waste Management (MWAN) estimating environmental degradation costs from solid waste at $1.3bn in 2021.
As landfill sites approach capacity, the Ministry of Environment, Water and Agriculture has responded with a comprehensive strategy unveiled in early 2024. This ambitious plan aims to achieve a 95 per cent recycling rate and process 100 million tonnes of waste annually, supported by more than 65 initiatives and investments exceeding SR55bn. The strategy is expected to contribute SR120bn ($31.99bn) to the gross domestic product, marking a significant shift in how waste management operates within the Kingdom.
The implementation of AI and IoT technologies, enabled by modern telecommunications networks, stands at the forefront of this transformation. Smart bins equipped with sensors now monitor waste levels in real-time, transmitting data to central management systems that analyse fill rates and predict collection needs.
These systems optimise pickup routes, reducing unnecessary collections and lowering fuel consumption and carbon emissions from collection vehicles. In processing facilities, AI-powered sorting machines improve material recovery rates using advanced algorithms to identify different types of waste materials. This technology processes waste faster than traditional methods while maintaining higher accuracy rates and reducing contamination in recycled materials.
The integration extends to collection vehicles, where AI systems monitor and verify waste content, ensuring compliance with acceptance criteria and maintaining processing quality standards throughout the entire waste management chain.
Predictive analytics and data-driven decision-making are reshaping waste management operations at every level. By analysing historical data and current trends, AI systems forecast waste generation patterns, enabling precise resource allocation and infrastructure planning. This capability proves particularly valuable in urban areas, where waste patterns vary significantly based on population density and commercial activity.
The technology monitors waste from collection to processing, providing real-time data on volumes, types, and processing status. These insights enable facility managers to optimise operations, identify improvement areas, and make informed decisions about infrastructure development. The implementation of robotics in material recovery facilities (MRFs) adds another layer of sophistication, with AI-guided robotic sorting systems working continuously to separate materials, reducing processing time, and improving recovery rates, particularly effective for handling mixed waste streams where accurate sorting is crucial for maximising resource recovery.
The digital transformation of waste management through AI introduces unprecedented levels of transparency and efficiency to the sector. Advanced monitoring systems track waste throughout its journey, from source to final processing, creating a digital trail that ensures accountability and enables real-time reporting.
This data-driven approach provides valuable insights for policymakers and operators, facilitating strategic planning and operational improvements. AI systems analyse patterns in waste generation and processing, enabling better infrastructure planning and resource allocation.
The technology optimises recycling processes through continuous analysis of waste composition data, ensuring maximum resource recovery while minimising environmental impact. This systematic approach to waste management represents a fundamental shift in how the sector operates, moving from reactive to proactive management strategies that anticipate and address challenges before they emerge.
The potential of AI in waste management extends beyond operational efficiency – it’s about creating a cleaner, more responsible world for future generations, ushering in an era where every action in managing waste is a step towards sustainability.
- Abdullah Mohammad Khorami is the Chief Business Officer of Etihad Salam Telecom Company.
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