As Abu Dhabi pushes for higher recycling and diversion rates, Tajmee'e is using AI-powered collection and visual monitoring to make its services more responsive. Ashly Alex, CEO of Tajmee'e, explains how real-time data is shaping operations, why collection is the first mile of resource recovery, and what skills the workforce will need next. 

The UAE is pushing towards higher recycling and diversion rates. How does the collection system need to evolve to get more waste separated at source and to the right recovery and recycling facilities?

Collection is the first critical link in the resource-recovery value chain. To increase recycling and diversion, we need to improve segregation at source and make sure each waste stream is directed to the right recovery and recycling pathway.

That requires closer integration between collection and downstream infrastructure, supported by better data and greater awareness of correct segregation. Ultimately, collection is the first mile of a wider system designed to preserve the value of materials and enable greater resource recovery.

Tajmee'e has expanded its AI-powered collection model to 70+ districts across northern Abu Dhabi. What have you learned about using real-time data to move from scheduled collection to a more demand-driven model?

Real-time data gives us greater visibility of collection needs and supports more informed operational decisions. Rather than relying solely on fixed schedules, we can better understand where and when services are required and adjust operations accordingly.

We use the data captured through our various data inlet points for trend analysis, to identify hotspots and adjust schedules based on demand. Our focus is on using technology and data to build a more responsive collection model while maintaining reliable, consistent service.

Tajmee'e has also piloted AI-powered visual monitoring to detect overflowing bins, waste accumulation and illegal dumping. How will this change the way collection teams respond to problems on the ground?

It gives us greater visibility of these issues and helps operations teams identify problems and prioritise their response based on conditions on the ground. The opportunity is to use technology to strengthen operational visibility and support more timely, informed decision-making.

It also helps us identify areas where these issues persist, so we can communicate with residents and encourage them to use the collection infrastructure properly and keep the city clean.

AI depends heavily on data. How are you turning the data from your fleets, bins and collection operations into better business decisions?

The value of data lies in how it informs decisions. The information generated through collection operations gives us greater visibility of service patterns, operational demand and asset performance, and used well, it supports operational planning and resource allocation. Our objective is to turn operational data into actionable insights that lead to better-informed decisions.

Could AI fundamentally change the traditional collection model, moving from fixed schedules to demand-based, predictive collection?

AI can support that evolution, but it should be seen as a progression rather than an immediate replacement of existing operating models. As the quality and availability of operational data improve, collection can become increasingly responsive to actual demand and, over time, more predictive. The opportunity is to combine operational experience with better data and technology to make collection more responsive and efficient.

As AI becomes more embedded in fleet and collection operations, what new skills will waste management companies need in their workforce?

Technology needs to be matched by investment in people. As digital tools become more integrated into collection operations, teams will need digital literacy, data interpretation and operational decision-making skills, alongside their existing technical and field expertise.