How can artificial intelligence (AI) accelerate the transition to a circular economy by optimizing resource use, improving supply chain transparency, and enabling new sustainable business models? Through concrete examples such as intelligent waste sorting and reverse logistics, AI reduces losses, enhances traceability, and supports innovative circular practices. A survey conducted among 65 Tunisian companies confirms that AI adoption—when supported by organizational and institutional factors—enhances environmental performance and contributes to achieving the Sustainable Development Goals (SDGs).
Artificial Intelligence : A Catalyst for the Transition to a Circular Economy
The circular economy (CE) offers a systemic and innovative response to the limitations of the dominant linear economic model based on the « extract, produce, consume, discard » logic. In contrast, CE aims to keep resources circulating within the economic system for as long as possible by promoting strategies such as reuse, repair, recycling, and regeneration. It seeks to minimize waste generation and reduce reliance on virgin resources, while addressing pressing challenges such as resource depletion, climate change, and growing pressure on ecosystems. This model goes beyond waste management, calling for a deep transformation of production and consumption patterns that integrates social, economic, and environmental dimensions.
Within this transitional context, artificial intelligence emerges as a strategic technological enabler. It provides powerful tools to rethink value chains in a smarter, more agile, and more sustainable way. With its capacity to analyze vast amounts of real-time data, detect hidden patterns, automate complex processes, and optimize flows, AI can play a pivotal role in operationalizing the principles of the circular economy.
Three key areas illustrate AI’s transformative potential in the circular economy:
1. Optimizing Resource Efficiency
One of AI’s most significant contributions to the circular economy lies in its ability to maximize resource use at every stage of a product’s life cycle. Through advanced machine learning and predictive intelligence algorithms, AI can process large datasets from production, consumption, and maintenance processes in real time. This enables the rapid identification of energy losses, operational inefficiencies, or excessive consumption of raw materials—factors often imperceptible to humans or traditional tools.
For instance, AI can detect anomalies in a machine’s energy use, flag overuse of certain materials, or recommend adjustments in production parameters to reduce waste. It is also crucial for implementing predictive maintenance, anticipating equipment failures before they occur—thus extending product lifespans and minimizing costly downtime. Simultaneously, AI supports engineers in selecting more sustainable or recyclable materials using integrated life cycle assessment (LCA) tools. These applications not only result in significant cost savings but also reduce pressure on natural resources by cutting waste generation and encouraging component reuse.
2. Enhancing Transparency and Traceability in Supply Chains
In a circular economy, traceability of products, components, and materials is essential. It ensures a clear understanding of where each element originates, how it was processed, used, and eventually recovered or disposed of. AI, when combined with connected sensors (IoT) and distributed ledger technologies like blockchain, enables the creation of intelligent, transparent, and interconnected supply chains.
These technologies facilitate continuous data collection and analysis across the entire product lifecycle—from manufacturing and transportation to usage, maintenance, and end-of-life. AI can automatically detect deviations from environmental standards, recommend more responsible suppliers, or alert logistics partners in case of anomalies. This increased visibility promotes the application of eco-design principles, where products are designed from the outset to be easily disassembled, repaired, or recycled.
Moreover, the trust built through transparency fosters collaboration across supply chain stakeholders, which is critical for effective reverse logistics—the return of products or materials at end-of-life to recovery or reintegration centers. AI thus becomes a key tool in building more accountable, traceable, and circular value chains.
3. Enabling New Circular Business Models
Beyond optimizing existing systems, AI plays a vital role in the emergence of new economic models tailored to circularity. One of the most promising is the Product-as-a-Service (PaaS) model, where businesses retain ownership of a product while offering its use to customers. Enabled by AI and connected technologies, this model supports maintenance, retrieval, and even reconditioning of products, encouraging the design of longer-lasting, repairable items within a closed-loop system.
AI also helps optimize sharing or pooling platforms by analyzing usage behavior, anticipating demand, and dynamically allocating resources. These platforms are particularly valuable in sectors such as shared logistics, industrial equipment leasing, or sustainable mobility.
Finally, AI strengthens the performance of local waste management ecosystems by intelligently managing material flows. Algorithms can classify waste types, determine optimal treatment paths, or plan energy- and carbon-efficient collection routes. This transforms waste management into a value-creating activity, closely aligned with circular economy principles.
By embedding AI into circular economy practices and strategies, businesses and regions can build smarter, more resilient, and more sustainable systems. This synergy paves the way for a new generation of regenerative innovations, where technology becomes a powerful ally in reconciling economic performance with ecological responsibility and long-term value creation.
AI and Reverse Logistics : Challenges and Key Adoption Factors for Tunisian SMEs
As part of the circular transition, a study was conducted among 65 Tunisian SMEs to assess the adoption of AI in reverse logistics—a crucial area for recycling or reusing end-of-life products, and a strategic lever for companies seeking sustainable transformation. Despite challenges such as cost, skill gaps, and coordination issues, AI emerged as a highly effective solution for automating, optimizing, and adding value to return flows.
The study identified eight key factors that promote AI adoption in reverse logistics:
Perceived relative advantage : AI is viewed as providing superior benefits compared to existing methods.
Trust in technology and providers : Companies perceive AI and tech partners as reliable and competent.
Management support : Strategic commitment from leadership facilitates innovation adoption.
Compliance with environmental regulations : Regulatory pressure encourages the modernization of logistics practices.
Industry dynamics : Sector norms and competitive pressures promote sustainable initiatives.
Compatibility with existing processes : AI can be integrated smoothly without disrupting current systems.
Technological readiness of the company : Adequate infrastructure and digital skills are in place.
Government support : Public funding, incentives, or policies ease the adoption of AI.
The observed benefits of integrating AI into reverse logistics are significant: reduced carbon footprint, better material recovery, increased productivity, and stronger alignment with the Sustainable Development Goals (SDGs).
