AI Resilience Report forRecycling Coordinators

AI is already changing how recycling programs run, but mostly by helping coordinators rather than replacing them. The clearest area of automation is at material recovery facilities (MRFs), where computer vision and robotic arms now sort paper, plastic, and metal at superhuman speeds. AI-based sorting is able to operate dozens of times faster than people with fewer mistakes and may soon be able to learn business specifics better than the people who make the machines, reducing operational and labor costs while allowing facilities to recover more materials that previously may have ended up in landfills.

WM has equipped nearly three dozen of its recycling facilities worldwide with AI sorting technology, and using AI has nearly doubled processing output at each facility to 40-45 tons per hour. The logging and reporting tasks that coordinators do (the most automatable on your list at 72%) are also being absorbed by software — CurbWaste and similar platforms ^[1] use AI to give haulers and processors real-time inventory, fuel, and maintenance data that used to require manual log-keeping. On the operations side, robotic systems now perform 60–120 picks per minute around the clock ^[2], and AI tools are helping companies identify recyclable material that would otherwise end up in landfills ^[3].

But your higher-touch tasks — training workers, supervising teams, ensuring community ordinance compliance, and investigating new collection opportunities — still rely on human judgment, relationships, and local knowledge that AI doesn't replicate.

Adoption is moving fast in the sorting and data side of the industry, and there are several reasons why. First, the technology is commercially available and proven: Tomra, AMP Robotics, Greyparrot, and others have working systems deployed in hundreds of facilities. Second, the economics work — WM is on track to spend more than $1.4 billion to build or upgrade 39 facilities by the end of 2026, adding 2.8 million tons of processing capacity.

Third, labor conditions are pushing companies toward AI: sorting jobs are notoriously hard to fill, and WM's CEO has said the company is using technology "almost by necessity" because the average heavy equipment operator is approaching 53 and replacements are hard to find ^[4]. That same plan will reduce roughly 5,000 positions by 2026 through attrition, mostly in physically demanding roles ^[4] — not coordinator jobs. Slower adoption factors include the high upfront cost of MRF retrofits, fragmented data across the industry, and the need for community trust around recycling compliance.

The encouraging takeaway: MIT Sloan researchers note that waste companies typically use AI to assist workers rather than replace coordinators ^[5], and as one WM executive put it, AI lets the company shift employees into more tech-forward roles such as operating and optimizing equipment via tablets. For young people entering this field, the skills that matter most — leading teams, building community partnerships, and interpreting data to make smarter program decisions — are exactly the ones AI makes more valuable, not less.