What is an AI-driven circular economy?
AI can provide the essential infrastructure required for the transition to a circular economy. Image: Getty Images/iStockphoto
- Transition to a circular economy requires designing out waste, keeping materials in use and regenerating natural systems.
- AI can provide the essential infrastructure for this transition by providing digital connectivity and actionable data insights.
- The AI-driven circular economy is the key to overcoming data fragmentation and managing global resource cycles.
Transitioning to a circular economy is a foundational imperative for global resilience that is defined by three conditions: designing out waste, keeping materials in use and regenerating natural systems.
Artificial intelligence (AI) can provide the essential infrastructure for this transition by increasing digital connectivity and the ability to generate actionable data insights across the value chain, thereby acting as the nervous system that links product design, manufacturing and recovery.
Indeed, AI's potential to reduce emissions in key sectors outweighs its own energy footprint by a significant margin, validating its role as a net-positive tool for the transition, according to recent research.
AI as the ‘nervous system’ for a circular economy
Currently, the core components of the circular model operate in isolation. For example, packaging converters and recyclers often lack the digital data regarding material composition necessary to ensure safe, high-value reuse. Even when data is collected, the industry lacks the tools to synthesise it into insights that inform large-scale operational changes.
This is why AI makes the circular value chain smarter and more connected. AI can function as a fast-iterating feedback mechanism that can send information and links these historically isolated parts of the value chain. By turning new chemical development or waste tracking and flows into actionable data, AI enables the system to operate with the intelligence required for resilience.
Without the ability to manage material flow, product design and waste management digitally, the circular economy will be slow to scale. AI serves as the feedback mechanism needed to connect these parts of the value chain. This is particularly urgent in the plastics sector, where global production is set to reach 500 million tonnes this year, yet only about 9% is recycled globally.
How AI strengthens the plastics value chain
From materials creation to waste identification, AI is starting to transform the plastic value chain in measurable ways to improve its overall circularity.
1. Novel chemical and material synthesis
AI accelerates the research needed to create circular alternatives and chemical end-of-life solutions.
- Inverse design: University research labs and companies like CuspAI are building generative AI platforms that act as a "search engine for materials". The company's inverse design approach enables researchers to specify the needed properties (e.g., biodegradable, highly stable, easily depolymerized) and have the AI propose the optimal molecular structure.
- Exponential growth: The scale of this acceleration is unprecedented; for example, Google DeepMind’s GNOME tool recently identified more than 2 million theoretical crystal structures – 45 times the number identified by science to date – drastically shortening the timeline for materials breakthroughs.
2. Sustainable product and packaging design
AI-enabled connectivity from the manufacturer to the recycler through digital feedback will be paramount in the circular economy. Generative AI is transforming how manufacturers design products to be easily disassembled and reused.
- Optimized material discovery: In a partnership with IBM Research, Nestlé is using generative AI and chemical language models to accelerate the development of new, high-barrier packaging materials. This enables them to screen thousands of novel material compositions in days, ensuring the final design meets strict criteria for safety, cost and full recyclability.
- Lifetime tracking: The key to connecting design and recovery is a standardized data pipeline, a concept enabled by AI: the digital product passport (dpp). Originating from regulatory efforts like the EU's Ecodesign for Sustainable Products Regulation (ESPR), the DPP is a comprehensive digital record of a product’s composition and circularity attributes. This transparency ensures every chemical and material is traceable, providing the precise data the consumer and recycler need throughout the product's life.
3. Waste identification and sortation
The lack of digital data and insights at the recovery stage is the main inhibitor to high-quality recycling. AI addresses this fragmentation head-on through:
- Real-time analytics: Companies like GreyParrot use AI-powered computer vision and deep learning to analyse waste streams in real-time, identifying material types with high accuracy – tracking up to 80 items per minute. This capability effectively doubles the efficiency of traditional sorting methods.
- Purity and value: The real-time data provided by these systems overcomes blindness at the facility level. This enables operators to achieve purity levels of over 95% in recycled outputs and reduces contamination rates by as much as 85%. This is critical for safely recycling plastics, given the health risks associated with chemical additives.
- Design feedback loop: Crucially, this real-time sorting data is now being leveraged to inform brands. AI systems can identify why specific consumer packaging is incorrectly sorted or contaminated (e.g. due to a dark colour additive, complex label, or unique shape). By working with consumer brands, this data helps companies make incremental changes to their packaging –such as adjusting the size, shape, colour, or labelling of a product – to instantly increase its recyclability without requiring a full product redesign.
4. Predictive material flow and logistics
The flow of recovered material is managed efficiently through predictive intelligence, ensuring every material is routed to its highest-value destination and minimizing wasted effort.
- Emissions and cost reduction: AI-driven predictive analytics model reverse logistics networks in real-time, routing collection vehicles based on live sensor data. By avoiding inefficient trips and maximizing load capacity, these systems can realize up to 35% reduction in associated carbon emissions from transport, alongside 20% to 30% savings in operational costs.
- Material savings: Furthermore, optimizing upstream logistics through predictive modelling leads to a sharp reduction in material waste due to overproduction, obsolescence and damage. For instance, Amazon's Package Decision Engine uses AI to determine the most efficient packaging for shipments; since 2015, this model has helped avoid more than 3 million metric tons of packaging material globally.
Collaboration to accelerate the shift to circularity
AI provides the intelligence, but a cross-value chain collaborative effort is essential to scale the digital solutions into systemic action. Collaboration ensures that the data produced by AI is leveraged across the entire value chain.
Current policy frameworks are largely designed for a linear economy, failing to create the necessary market "pull" for sustainable materials. Governments and policy-makers must rectify this by establishing clear, harmonized global and regional extended producer responsibility (EPR) regulations that incentivize circularity.
Furthermore, they must define standardized key performance indicators across the value chain to track progress, ensuring that regulatory pressure moves beyond vague targets to measurable outcomes.
Meanwhile, tech companies and innovators must provide interoperable tools and avoid proprietary "black boxes”. The data generated by platforms like Google X’s Materra, CuspAI and GreyParrot must be accessible to chemical manufacturers, converters, brands and recyclers to inform design and infrastructure planning.
In addition, manufacturers and industry leaders must embrace radical transparency regarding material composition and end-of-life status. Beyond data, this requires a cultural shift toward pre-competitive knowledge sharing, where industry leaders actively disseminate successful case studies of circular adoption to accelerate the learning curve for the entire sector.
The AI-driven circular economy is the key to overcoming data fragmentation and managing global resource cycles.
By using AI as the nervous system to both enhance and connect every stage of the value chain, and by prioritizing radical collaboration, we can move beyond managing waste and towards achieving resource resilience across all material streams.
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Marie Myers
January 8, 2026
