A recent study published in ScienceDirect shows that AI is becoming an engine for circular business models. Capabilities such as predictive analytics, real-time monitoring, and intelligent automation help redesign production chains to regenerate, reuse, and repurpose, almost as if the algorithm were the circular architect. But there are risks: without good indicators of circularity, the promise may become a mirage.
We need clear metrics to monitor the lifecycle of products and materials, and to ensure that AI is truly closing loops, not just optimizing linear processes. In real life, this means having accurate indicators on usage, returns, reuse, attention to waste, and product lifecycle, and trusting that the algorithms are providing the correct diagnosis. It's not all technological rosy, though.
Another interesting finding comes from a study by the Ellen MacArthur Foundation with support from McKinsey: they show that AI can accelerate circularity on three fronts — design, new business models, and infrastructure optimization. Translating this to our daily lives: AI could help create packaging that disassembles itself at the end of its useful life, support leasing systems that extend the lifespan of products, and even refine reverse logistics to recover and recycle everything we consume.
The gains are concrete: up to US$127 billion per year in food and US$90 billion per year in electronics by 2030. We're talking about real money being saved and recycled, in a system that learns and adapts. In other words, digitized circularity also means competitiveness and profitability – which makes it all the more irresistible in a capitalist world.
And let's turn to the Harvard Business Review to support the discussion : according to Shirley Lu and George Serafeim, the world remains trapped in a linear cycle of extract-produce-discard, despite circularity promising trillions in value, but it runs into barriers such as low value of used products, high separation costs, and lack of traceability.
The solution? Accelerate with AI on three very practical fronts: extending product lifespan, using less raw materials, and increasing the use of recycled materials. AI can help maintain a long lifespan with updates (like on iPhones) or product-as-a-service initiatives, where the company retains ownership and the consumer only "rents," prolonging the actual usage cycle. This generates revenue, builds loyalty, increases the value of used products, and pushes towards a more circular and profitable economy, provided that the technology doesn't become just another expensive luxury.
This is where we need to connect the dots. The Circular Economy teaches us to rethink material and energy flows, seek efficiency, eliminate waste, and regenerate systems. But when we talk about AI, we face a paradox: it can accelerate solutions and opportunities for circularity (such as mapping flows, predicting recycling chains, optimizing reverse logistics, identifying waste hotspots, or even accelerating research into new materials), but it can also amplify environmental impacts if not used consciously.
Among some of the risks, we can highlight the environmental footprint of AI (with the increasing consumption of energy and water in data centers), e-waste (the race for chips, servers, and supercomputers also generates mountains of electronic waste and puts pressure on the mining of critical minerals), and the digital divide (developing countries may become dependent on expensive technologies without fair access to the benefits).
The great challenge lies in finding the balance. We need AI that serves circularity, not the other way around. How can we ensure that Artificial Intelligence, instead of exacerbating the environmental crisis, is an effective part of the solution? We need to maintain a critical spirit. We cannot be swayed solely by technological hype. It's time to choose: do we want AI that deepens inequalities and environmental pressures, or AI that enhances the transition to a circular economy?
I try to be optimistic. I believe that processes tend to become increasingly efficient, with lower energy consumption and better use of resources.
What seems like a dilemma today – more AI meaning more energy demand – could balance out in the future, provided the same creativity used to write algorithms is applied to reducing impacts and regenerating systems. We can use AI as a strategic ally of circularity, with watchful eyes and solid criteria: demanding efficiency, traceability, and transparent metrics.
True intelligence isn't measured solely in lines of code or processing speed. In the environmental field, only circularity will guarantee that this intelligence is real, and not merely artificial. Ultimately, the challenge won't just be about creating and monitoring artificial intelligence… but rather circular intelligence.
*Isabela Bonatto is an ambassador for the Circular Movement. A biologist with a PhD in Environmental Engineering, she has over twelve years of experience in socio-environmental management. Since 2021, she has lived in Kenya, where she works as a consultant on socio-environmental projects in partnership with UN agencies, governments, the private sector, and civil society organizations. Her career combines technical and scientific knowledge with inclusive social practices, developing initiatives that integrate natural resource management, public policies, circular innovation, and community empowerment.
