The way we design new products and services has scaled in ambition to include our entire economic system. New tools such as artificial intelligence can ensure that the only limitations we experience are those of our imaginations and the world’s finite resources. This learning path explores what artificial intelligence is and how it can be used to accelerate the transition to a circular economy, focusing in particular on the opportunities for AI to:

  1. Inform and accelerate efforts to design out waste and pollution
  2. Increase the effectiveness of and optimise circular economy business models
  3. Streamline the infrastructure needed to keep products and materials in use
What is artificial intelligence?

The science of making inanimate objects smart.

AI is an overarching term for a collection of technologies. It deals with computer models and systems that perform human-like cognitive functions such as reasoning and learning. AI software is capable of learning from experience, differentiating it from more conventional software which is preprogrammed and deterministic in nature.

Artificial intelligence (AI) doesn’t necessarily mean giving intelligence or consciousness to machines in the same way that a person is intelligent and conscious. It simply means the machine is able to solve a particular problem or class of problems.

AI helps to solve problems through performing tasks which involve skills such as pattern recognition, prediction, optimisation, and recommendation generation, based on data from videos, images, audio, numerics, text and more.

General versus specialised

General versus specialised

What’s the difference between general AI or specialised AI?

General AI is a general-purpose system that exhibits intelligent behaviour comparable to a human’s full range of cognitive abilities. Narrow or specialised AI is good at performing specific, well-defined, tasks normally associated with human cognitive abilities, such as image and voice recognition, trend prediction, and pattern spotting.


Which of the following statements about AI is true?

  • Machine learning is another term for AI.
  • Significant manual human work is needed to develop an AI algorithm.
  • AI is always combined with robotics.


Real world data is often messy, incomplete or in a format which is not easily readable by a machine. An AI algorithm needs to be trained using ‘clean’ data so the output will be useful - this process of data engineering can involve a lot of manual work.

3 Debunking some myths about AI

Debunking some myths about AI

AI is not a new technology, nor do its origins lie in robotics.

There are numerous myths surrounding AI. One of the most common is that AI is a new technology. In fact, the first academic project investigating AI was in 1956 when a small group of mathematicians and scientists gathered for a summer research project on the campus of Dartmouth College. The reason it feels like a new field is because what we call ‘AI’ keeps changing. Clever things like automatic number plate recognition for cars (developed by UK police in the late 1970s) are now taken for granted. What we’re seeing today is simply the next step in the long-running evolution of developments to make computers better at analysing data.

Pause for thought

Now that you understand AI a little better, how do you think it could be used to enable a circular economy?

How can AI advance the circular economy?

Creating regenerative systems by introducing AI to design, business models, and infrastructure.

AI can be a hugely powerful tool. Imagine if it was being used to accelerate the transition to a circular economy and create new opportunities for large scale positive change. In the following sections we will explore how employing AI in our design, business models, and infrastructure could increase our ability to create new, regenerative systems based on the principles of circularity.

Design powered by AI

Design powered by AI

Informing designers’ choices.

The circular economy puts a strong focus on design. Indeed, the Circular Design Guide asks: what if you could redesign everything?

Designers working with AI can create products, components, and materials which are fit for the circular economy. Employing AI can account for better designs faster, due to the speed with which an AI algorithm can analyse large amounts of data and suggest initial designs or design adjustments. A designer can then review, tweak, and approve adjustments based on that data. AI gives designers a more informed insight into the most effective designs to create and test to make the best use of their time and expertise.

Three key benefits of AI in design:

Cutting through complexity

Sifting through countless designs and suggesting the ones which best fit circular design criteria.

Speeding up the design process

Algorithms can rapidly analyse large quantities of well labelled data, such as material databases and consumer preference data.

Coming up with novel designs

AI can help humans think outside the box, remove bias, and design things in new ways.

Case Studies - Design

Case Studies - Design

The following three case studies illustrate how AI is being used right now to enable better, more circular design.

Circular business models

Circular business models

Enabling new business models with AI.

Since the industrial revolution, the linear economic system has become gradually more optimised and efficient, most recently using digital technologies such as AI. Similar techniques could be applied more widely to circular business models to increase their competitiveness.

Ways in which AI could assist in creating circular business models

Dynamic pricing

Such as lowering the price of food as it approaches its expiry date to reduce food waste.

Matching algorithms

For sharing or second-hand platforms to effectively connect people with the things they want, from tools to apartments.

Predictive maintenance

And prediction of reverse logistics demands.

Case Studies - Business models

Case Studies - Business models

The case study below showcases how one company, Stuffstr, is using AI to dynamically set prices in its fashion resale marketplace.

In the video, Martin Stuchtey - co-founder and managing partner of SYSTEMIQ, paints a picture of a future mobility system in which AI plays a key role.



Streamlining the circulation of materials in the economy.

Products at the end of their life are not as uniform as they were when first manufactured, so they are harder to automatically disassemble, sort, and separate. Their condition typically needs to be manually inspected and then treated based on what damage or wear and tear they have sustained.

There are many opportunities for AI to help streamline the infrastructure needed to circulate materials in the economy - many of them focusing around the ability for AI algorithms to recognise and identify objects using cameras and other sensors.

Three ways AI can impact infrastructure:

Automated assessment

Automated condition assessment of used products, and recommendations for whether they can be reused, resold, repaired or recycled to maximise value preservation.

Automated disassembly

Automated disassembly of used products employing AI to assess and adjust the disassembly equipment settings based on the condition and position of a product.


Sorting of post-consumer mixed material streams using AI visual recognition techniques combined with robotics.

Case studies - Infrastructure

Case studies - Infrastructure

The three case studies below demonstrate how AI is already being used to improve and optimise processes such as waste sorting, recycling, and sorting of food produce.

8 Limitations


Is AI the answer to all of our questions?

While there is huge potential for AI to be a force for positive change, it also raises questions about building fairness, interpretability, privacy, and security into these systems - which are currently active areas of research and development.

AI applications need systems designed to follow best practice, alongside considerations unique to machine learning. With the potential to be fairer and more inclusive than decision-making processes based on ad hoc rules or human judgments, comes the risk that any unfairness in such AI systems could incur wide-scale impact. Thus, as AI increases across sectors and societies, it is critical to work towards systems that are fair and inclusive for all.

Ultimately, AI can be a very helpful tool to achieve circular economy ambitions, but it should be held to account by humans, driven by human values and principles, avoid creating or reinforcing unfair bias, and account for privacy and security of data.

What is the potential for AI?

What is the potential for AI?

There has never been a more exciting time to be an innovator.

Some AI systems are already in the market and just need to be used more extensively in a circular context, particularly for circular business models. This could facilitate everything, from predictive analytics (such as setting the optimal service and repair schedule for durable equipment), to dynamic pricing and matching for the effective functioning of digital marketplaces for second hand goods and by-product material streams.

As the technology develops, in a similar way to how it is being used today to improve traffic flow through cities, AI could be integral to the redesign of whole systems, which create a circular society that works in the long term. If you’d like to know even more about the potential for AI to enable the shift to a circular economy, the scoping paper, Artificial Intelligence and the Circular Economy - written in collaboration with Google, and with analytical support from McKinsey & Company - provides more detail.

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