Is AI ready to clean up in waste management?

The global production, collection, recycling and overall management of waste still leaves a lot to be desired. AI continues to be seen as a promising candidate for enabling rapid breakthroughs in developing the kind of circular economy systems that will turn trash into treasure. 

But how quickly can it deliver on that promise, and how will the waste management industry respond to such systemic change?


Mountains to Move – Waste is growing, not shrinking

Despite technological advances in recycling centres, Waste-to-Energy plants, collection truck fleets and in the nerve centres of waste management operations, the world’s production of waste continues to outstrip our collective capability to deal with it.

The World Bank has predicted that municipal solid waste (MSW) generation will grow from 2.3 billion tonnes in 2023 to 3.8 billion tonnes by 2050. In 2022, only 19% of all global MSW was recycled; without a rapid expansion of recycling capabilities or drastic reduction of global consumption rates, waste will continue to be a mounting problem with not even the smallest overall dent being made.

Assessing AI’s Impact – Top uses in Waste Management 

Recycling efficiency: While the technology exists to effectively take apart and recycle even complex multipart products, scalability has always been a critical weakness of recycling plants. Automation is boosting sorting rates, but there is still a long way to go before they can keep up with demand. AI solutions can build vast databases of rubbish items, coordinating the sorting process with human and robotic workers, upping efficiency and safety while cutting down on mistakes that can contaminate whole loads. UK-based Greyparrot is an example of what cutting-edge analytical systems can do, as its AI analyses waste in real time, and scans around 32 billion items of waste a year.

Collection route optimisation: Millions of rubbish trucks across the world drive their routes every day, collecting rubbish and transporting it to landfill or recycling facilities. By understanding exactly where the waste is at a given moment, these fleets are in a better position to plan their routes efficiently, cutting out needless collections, saving time, fuel and CO2 emissions in the process. Recent research suggests that AI could reduce overall transportation distances by up to 36.8%, enabling time savings of up to 28.22%, and delivering operational cost savings of 13.35%. In time, this approach may lead to fully automated collection truck fleets, guided by AI, making no unnecessary journeys at all.

Waste-to-Energy (WtE): While burning rubbish for energy is hardly a neat solution to our global waste problem, it is better than many of the alternatives. However, WtE plants frequently endure stoppages and even safety-related incidents as irregular waste streams create blockages. AI systems can constantly scan incoming waste and raise alarms on any potential risk factors, stopping incidents before they start. This AI-empowered approach can also help optimise wider operations, leading to production improvements of 15-20% compared to traditional methods.

Education: Addressing the waste problem from the other side of the equation, AI is seeing greater use in providing knowledge and boosting awareness regarding public recycling participation and reduction of consumption rates. Through chatbots, dedicated interfaces and business advice platforms, AI can help individuals and businesses better understand what to do with their rubbish and how to play their small part in addressing the global waste problem.

Explosive potential – Slow Adoption

The wide (and widening) range of use cases for AI in waste management demonstrates one of the most frustrating truisms of wider global sustainability efforts – the technology is there (or there abouts), future success now hinges on the question of scalability.

Taking a technologically sophisticated approach, like a new AI-integrated system in a recycling plant, is beneficial in terms of demonstrating what the technology can achieve. It is also useful in sparking off more radical rethinking of whole swathes of operational processes to develop more sustainable industries overall. The optimised truck fleet is a prime example of how AI can help completely overhaul decades-old industrial practices.

However, elegant and innovative as these solutions are, they are simply not deployed widely enough at present to move the needle on the global waste generation crisis. For that to happen, AI will have to be integrated intelligently and successfully into every facet of major citywide or even nationwide waste management operations.

That day may not be far off. Though the Global AI in Waste Management Market size was only a relatively modest $1.6 billion in 2023, this is predicted to skyrocket within the coming decade, reaching $18.2 billion in 2033. This would be a CAGR of 27.5% and more than a tenfold increase.

As we’ve seen in other areas of AI adoption, success breeds success. Equally, success breeds investment. Waste management is a commercial enterprise, but it’s also highly politicised since it is tied to a range of climate and public health issues. Accordingly, success stories in AI adoption will no doubt lead to faster-than-normal rates of further integration, as both the public and private sector have a vested interest in turning the global waste crisis into a climate win and a commercial boom period.