an A recent study published in ScienceDirect shows that AI is becoming a driver 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 circularity indicators, the promise can turn into a mirage.
We need clear metrics to monitor the life cycle of products and materials, and to ensure that AI is truly closing loops, not just optimizing the linear one. In real life, this means having the right indicators about usage, return, repurposing, attention to waste, and product life cycle, and trusting that the algorithms are providing the correct diagnosis. It's not all technological roses.
Another interesting perspective 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 life, support leasing systems that extend product lifespan, and even sophisticate 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 are 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 all this even more irresistible in a capitalist world.
And let's turn to Harvard Business Review to endorse the discussion: according to Shirley Lu and George Serafeim, the world remains stuck in a linear cycle of extract-produce-dispose, even though circularity promises trillions in value; however, it runs into barriers such as the low value of used products, high separation costs, and lack of traceability.
The way out? Accelerate with AI on three very practical fronts: extend product lifespan, use less raw material, and increase the use of recycled materials: AI can help maintain a high lifespan through updates (like in iPhones) or product-as-a-service actions, where the company remains the owner and the consumer only “rents,” prolonging the actual usage cycle. This generates revenue, fosters loyalty, increases the value of used products, and also pushes towards a more circular and profitable economy, provided 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 flows of materials and energy, 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 AI's environmental footprint (with the growing energy and water consumption in data centers), E-waste (the race for chips, servers, and super machines also generates mountains of electronic waste and pressures the mining of critical minerals), and digital inequality (developing countries may become dependent on expensive technologies, without fair access to the benefits).
The great challenge lies in balance. We need AI in the service of circularity, not the other way around. How can we ensure that Artificial Intelligence, instead of worsening the environmental crisis, becomes an effective part of the solution? We need to maintain a critical spirit. We cannot let ourselves be carried away just by the technological hype. It's time to choose: do we want an AI that deepens inequalities and environmental pressures, or an AI that empowers the transition to a circular economy?
I try to be optimistic. I believe processes tend to become increasingly efficient, with lower energy consumption and better resource utilization.
What now seems like a dilemma – more AI meaning more energy demand – may balance out in the future, provided the same creativity used to write algorithms is applied to reduce impacts and regenerate systems. We can use AI as a strategic ally for circularity, with keen eyes and solid criteria: demanding efficiency, traceability, and transparent metrics.
True intelligence is not measured only in lines of code or processing speed. In the environmental field, only circularity will ensure that this intelligence is real, and not just artificial. Ultimately, the challenge will not only be about creating and monitoring an artificial intelligence... but rather a circular intelligence.
*Isabela Bonatto is an ambassador for the Movimento Circular. Biologist and Doctor in Environmental Engineering, with 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-scientific knowledge with inclusive social practices, developing initiatives that integrate natural resource management, public policies, circular innovation, and community capacity building.
