Skip to Content

Creating a circular economy through AI

Pratyasha Shishodia
14 February 2023

Sustainability is a buzzword and it’s becoming action. However, the current focus is on offsetting the impact of our actions rather than tackling the root cause. A cradle-to-cradle model is the paradigm shift that the current linear economy needs. And AI is the catalyst for this. Let us look at how AI can be applied to different stages of the circular economy: right from product inception to its regeneration, thus closing the loop.

Let’s say you bought a new phone, perhaps the latest iPhone 14, as a replacement for your previous one. Have you ever thought where all the old devices go?

Unfortunately, they end up in landfills, since we follow the “take-make-dispose” a.k.a. the linear economic model, wherein we extract raw materials, manufacture, and use products, followed by disposing of the product at the end of its shelf life. Herein lies the big issue: the waste generated causes multi-dimensional ramifications on the environment and public health, best explained by designer Sophie Thomas, Director of Circular Design at Useful Simple: “Waste is a design flaw.”

Therefore, the failure of the linear economy in addressing issues like product wastage, raw material shortage, and carbon footprint has given rise to the need for an alternative model, one which is fundamentally circular, just like our nature cycles, and mitigates the harms caused by industrial waste. Here, the circular economy (CE) model seems promising in tackling problems caused by the linear economy model, since it mimics the Earth’s natural cycles by applying similar principles to our economic system. The fundamental premise of the circular economy is based on the 3Rs – Reduce, Reuse, and Recycle – that focus on waste minimization along with optimum utilization and reuse of existing products, thereby ensuring resource circularity.

Moving forward, the question of practicality arises, as in how do we shift from a linear model to a circular one in a relevant and cost-effective manner? The answer lies in artificial intelligence (AI), which will serve as the major catalyst in enabling this paradigm shift. Here are some key AI-enabled phases that can drive forward this transformation.

01: Make it last:

Design circular products using iterative machine learning and AI suggestions that will prolong the product life cycle and tackle resource scarcity. With AI, you can predict product and carbon costs right from the initial design phase to ensure optimized scenarios. For example, you can source local products and reduce the carbon footprint associated with transport or product substitution during the manufacturing phase.

Chilean brand NotCo made an egg-free mayonnaise using plant-based substitutes with the help of an AI-based ecosystem. It deploys an ML algorithm to identify new plant-based foods and food formulas by detecting patterns at a molecular level and analyzing flavor molecules. This helps in quick testing, tasting, and providing feedback to ensure that the final product tastes as good as the original one.

Amazon created sustainable packaging designs leveraging AI algorithms to identify products that can be shipped in padded mailers instead of boxes, making packages lighter. This increases the number of packages dispatched per truck, thereby reducing the amount of packaging that needs to be recycled, eventually causing a decline in the carbon footprint per item along with slashing delivery costs.

“The fundamental premise of the circular economy is based on the 3Rs – Reduce, Reuse, and Recycle – that focuses on waste minimization along with optimum utilization and reuse.”

02: Use optimally:

Use data-driven AI algorithms to develop innovative circular business strategies and frameworks for sustainable growth by combining previously recorded and real-time data from other stakeholders, including producers, manufacturers, suppliers, and consumers for process optimization and automated decision-making.

Stuffstr utilizes AI for price setting, forecasting demand, and creating trading platforms for secondary resources and products. Stuffstr buys and collects used products from consumers and sells them in secondhand markets. An AI algorithm helps Stuffstr to set competitive prices for the seller while offering Stuffstr a good margin in the secondhand market.

H&M amplifies business solutions with AI to consider the environmental impact of its raw materials. It covers the entire value chain, looking at close to 5,000 H&M stores. It uses AI to understand consumer needs to produce only the right products in the right amounts and allocate them to the right place. The framework delivered immense business value by reducing time-to-market for use case development by 50 percent (i.e. from 12 to six months).

03: Recycle to close the loop:

Circular production ensures infrastructure is fully optimized and, by mathematical modeling, material flow is created for acquiring used products, assessing waste, and reprocessing.

AI is already helping in creating value for circular material flows and enhancing the selection of materials and products by sorting post-consumer mixed material streams through visual recognition techniques.

Unilever and the Alibaba Group created an Al-enabled recycling system that automatically identifies and sorts plastic packaging. It aims to speed up high-grade plastic back into the CE and move China’s companies and consumers towards a waste-free world. Using AI technology, it automatically identifies the type of plastic, sorts it and stores it, collects and returns it to recycling centers, and fast-tracks it for reuse rather than being left to degrade.

At Ikea, 15 percent of its returned items become waste. To tackle this, Ikea has adopted AI for handling returned merchandise. Ikea installed an AI platform developed by its partner Optoro in 50 locations across the US. It predicts the best possible destination for returned merchandise, whether it should be back on the floor, on the website, donated to charity, or sold to a third-party wholesaler. The algorithm determines this based on what makes the most sense for driving up Ikea’s profits.

AI in a circular economy promises boundless opportunities in the future, however, it is largely untapped. The current understanding of circular-economy principles among businesses is limited to recycling, which is just one part of the CE model. Despite increasing awareness around sustainability, most organizations are not prioritizing the remaining two stages adequately.

Creating a broader awareness and understanding of how AI can be used to support a circular economy will be essential for enabling organizations’ transition towards a truly circular economy. It will also play well with consumers who want to take responsibility for the environment.

Dr. Caroline Cassignol, Siemens Technology, explains why this transition is imperative: “We grew up in a world dominated by the linear economy, and now we need to shift to a circular economy. That requires a completely different mindset. Everything we do must be questioned.”



The failure of the linear economy in addressing environmental and health issues has given rise to the need for an alternative model which is fundamentally circular, just like our Earth’s natural cycles, and mitigates the harms caused by industrial waste.


Currently, the understanding of circular-economy principles among businesses is limited to recycling. It is important to encourage awareness around design and circular infrastructure.


An AI-enabled futuristic circular approach is the key to accomplishing the majority of the UN’s Sustainable Development Goals and generating goodwill among consumers for taking responsibility towards the environment.

Interesting read?

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 5 features 19 such articles crafted by leading Capgemini and partner experts, about looking beyond the usual surroundings and be inspired by new ways to elevate data & AI. Explore the articles on serendipity, data like poker, circular economy, or data mesh. In addition, several articles are in collaboration with key technology partners such AWS, Denodo, Databricks and DataikuFind all previous Waves here.


Pratyasha Shishodia

Director- FROG Customer Transformation and Data
Pratyasha is an Enterprise Architect with over 17+ years of experience in Customer Experience Design, Architecture and Advisory currently leading Customer transformation & Data Analytics with a team of 35+ consisting of CX and data experts. She has firsthand experience of CRM evolution and has worked in all aspects of CX transformation projects. Solving Complex CX Problems drives her to learn every day. Pratyasha is an avid reader and non-fiction is what excites her beyond work.

Faizan Shaikh

Senior Consultant- FROG Data & Analytics
Faizan is a seasoned marketing analytics consultant with over 7 years of experience in the field. With a strong background in data analysis and a passion for both AI and sustainability, Faizan has a unique perspective on how to drive business growth through data-driven insights and innovative solutions. He has honed his skills by working on a diverse range of projects for top FMCG brands. With a deep understanding of data analytics tools and techniques, Faizan, currently manages a team of data analysts and has a track record of delivering meaningful insights and recommendations to help companies make data-driven decisions.

Soumitra Upadhyay

Associate Consultant- FROG Data & Analytics
Soumitra is a consulting professional in the field of Analytics with relevant experience in Marketing Analytics , Consulting and Research using customer data driving insights about market trends and strategies for best feasible output. He is an avid sports lover and loves to travel across places with varying geographies and history.