Artificial intelligence and the circular economy

This paper offers a first look into the cross-section of two emerging megatrends: how AI can accelerate the transition to a circular economy. It finds that AI can enhance and enable circular economy innovation across industries in three main ways:

1. Design circular products, components, and materials. AI can enhance and accelerate the development of new products, components, and materials fit for a circular economy through iterative machine-learning-assisted design processes that allow for rapid prototyping and testing.
2. Operate circular business models. AI can magnify the competitive strength of circular economy business models, such as product-as-a-service and leasing. By combining realtime and historical data from products and users, AI can help increase product circulation and asset utilisation through pricing and demand prediction, predictive maintenance, and smart inventory management.
3. Optimise circular infrastructure. AI can help build and improve the reverse logistics infrastructure required to ‘close the loop’ on products and materials by improving the processes to sort and disassemble products, remanufacture components, and recycle materials.

To illustrate the range of applications across sectors, this paper looks at two value chains: food and agriculture; and consumer electronics. These examples, one centred on biological materials and the other on technical materials, highlight the potential of AI to increase the circularity of a broad range of economic activity.