From Artificial to Circular Intelligence: The Role of Generative AI

Antonio Lanotte

Antonio Lanotte (@alanotte23 on X; antonio@ vernewellgroup.com) is a chartered tax adviser and senior auditor. He is also a member of the board of advisers at Vernewell Management Consultancies, a member of the advisory council of Blockchain for Europe, a member/delegate of the CFE Tax Technology Committee, and a member of the Panel of Experts at EU Blockchain Observatory & Forum (EUBOF — European Commission).

In this article, Lanotte provides an initial exploration of the intersection between two emerging megatrends: artificial intelligence and the circular economy.

I. Introduction

In 2019 the Ellen MacArthur Foundation carried out a study¹ that investigated the role of artificial intelligence in accelerating the global transition to a circular operating model of the economy.²

According to this study, which examined two value chains (food and consumer electronics), the application of AI can offer substantial improvements in three main areas: the circular design of products, components, and choice of materials; the circular operation of business models; and infrastructure optimization. Examining the interaction between AI and the circular economy, the researchers concluded that AI represents the most powerful accelerator for realizing a new model of production and consumption in which today’s goods are tomorrow’s resources, and based on the principle of closing the life cycle loop of products, services, waste, materials, water, and energy.

II. Artificial Intelligence and Circularity

AI could accelerate the transition to a large-scale circular economy. For example, the selection and use of new materials and chemicals, with the aim of improving the circularity of products and production, require processing and evaluating large amounts of data regarding their characteristics, toxicity, biodegradability, recyclability, and available substitutes. It can therefore be useful to process a large amount of data and information quickly. And again, in circular business models, the increased variability of demand requires platforms to manage product and component inventories; ensure customer service; operate distribution and return logistics; trade in secondary raw materials,³ components, and recycled products; and connect used products with second-hand markets.

The circularity of products and material flows then require infrastructures for collection, sorting, separation, treatment, and redistribution. Optimizing the operation and management of these infrastructures requires the ability to manage a large mass of information on the products, the materials used (those that can be recovered), and the conditions of maintenance, repair, and management to make them effectively reusable. According to the European Environment Agency, the circular economy is a vital part of the green economy, which broadly deals with the human welfare, lifestyle, and consumption models for the extensive and inclusive well-being of the planet and natural capital, as well as ecosystem resilience and preservation. At the root of the growing interest in the circular economy is the inevitable need to save both renewable and nonrenewable natural resources and develop more efficient uses. Since 1900, the world’s population has quadrupled. Resource consumption has grown by a factor of 10, and it is expected to double by 2030. A circular economy refers to an industrial model that is regenerative by intention, in which products are designed to facilitate reuse, disassembling, restoration, and recycling to allow a large amount of materials to be reused instead of being produced by primary extraction. It further refers to a model in which businesses keep resources in use as long as possible to extract the maximum value from them, then to recover and regenerate their products and materials at the end of their service life. Ecodesign is a key element of the circular economy. New engineering (or reengineering) of production processes, goods, services, and value chains according to the ecodesign criteria means:

• boosting resource and energy efficiency;

• eliminating toxic and dangerous chemicals;

• reducing environmental effects in production, consumption, and end-of-life management;

• increasing product reuse, regeneration, and material recycling; and

• preventing waste production and disposal.

Businesses involved in these activities need to analyze and modify existing products and production processes. This includes doing the following:

• verifying and improving the scientific and management models, like life cycle assessment algorithms, environmental management systems, and the certification of products to make the circular economy criteria more effective;

• adopting very specific models to maximize resource efficiency toward zero waste;

• developing research and eco-innovation to enable circular economy models to reduce material and energy consumption while improving well-being, as well as focusing on the greatest renewable resource we have: the reuse, regeneration, longevity, and recyclability of products, components, and materials;

• developing production and use of renewable energy and materials, because circular economy models require businesses to move away from limited fossil fuels in favor of renewable energy sources only;

• creating zero waste, because in a circular economy model, waste is used as a resource;

• harnessing the power of inner circles, which refers to minimizing material usage by addressing the recovery of end-of-life products in the value chain close to the consumption phase, allowing a high return on the collection and treatment costs compared with disposal; and

• increasing efficiency of materials used, reducing the production costs and selling prices that create pressure on natural resources.

III. Digital and Green Transition: The Role of AI

To accelerate the transition to a circular economy, innovative product design is needed to increase product use and thus extend useful life, improve material efficiency, and expand the use of recycled materials. According to the European Commission, in fact, the environmental impact of more than 80 percent of products is determined at the design stage.⁴ Thanks to the power of data, machine learning, and the various applications of AI, it is estimated that 70 percent of CO₂ emissions can be eliminated by 2030. An opportunity to extend the useful life of products comes from the “product-as-a-service model,” in which companies retain ownership of a product while consumers pay for its use. Advances in data and traceability technology have enabled a better flow of information that facilitates this business model.

Regarding material efficiency, AI also increases the possibilities of minimizing waste in production processes. Increasing use of recycled materials is another way to reduce carbon emissions,⁵ as well as allowing companies to increase material security — a particular concern for industries that depend on rare minerals like lithium or cobalt. Increasing material recovery and separation activities is crucial to making recycling more available and cost-effective. The application of AI, particularly machine learning, in business processes can facilitate the transition to a circular economy. Algorithms can make a decisive contribution to the use of new, more durable materials that are recyclable. Above all, by analyzing product data throughout its life cycle, algorithms can suggest modifications and repairs, and make components more adaptable. One of the great contributions of AI to the circular economy, then, is process optimization: intelligent warehouse management, machine maintenance, demand forecasting, and efficiency of deliveries, routes, and times — particularly on the last mile — are just some of the applications that increasingly help logistics. But AI can also make contributions to infrastructure, recycling, material flow, and disposal. With the use of AI technology, it is possible to act directly on the energy consumption of industrial processes, making them more efficient in production and economic terms, as well as reducing waste and production time. It also can ensure better distribution and efficiency of available energy resources. AI-based algorithms present opportunities to improve risk management and decision-making processes, analyze scenarios, optimize models, and formulate climate and environmental adaptation strategies. Estimates suggest that AI could be worth €2.2 trillion by 2030 in the food and consumer electronics sectors alone.⁶ In agribusiness, AI can predict environmental conditions and thus enable growers to make real-time decisions for their crops, assess the amount of water and fertilizer by limiting waste and generally regulating water consumption, and monitor the ripening of crops.⁷

IV. The Potential Effect of Generative AI

The potential effect of generative AI on productivity is substantial, with the possibility of contributing trillions of dollars to the global economy. Research indicates that generative AI has the capacity to generate an annual value equivalent to between $2.6 trillion and $4.4 trillion across the 63 use cases examined.⁸ To put this into perspective, the entire GDP of the United Kingdom in 2021 was $3.1 trillion. Therefore, the addition of generative AI’s value could increase the overall effect of AI on the GDP by 15 to 40 percent. It’s worth noting that this estimate could roughly double if we consider the effects of integrating generative AI into software used for tasks beyond the initial use cases. This demonstrates the potential of generative AI technology to enhance productivity and economic growth on a global scale.

Approximately 75 percent of the potential value that generative AI use can provide is concentrated within four key areas: customer operations, marketing and sales, software engineering, and research and development. The analysis encompassed 16 different business functions, examining a total of 63 cases in which generative AI technology can be applied to address specific business challenges, resulting in one or more quantifiable outcomes.⁹ Examples of these applications include the capability of generative AI to enhance customer interactions, create novel content for marketing and sales purposes, and even generate computer code based on natural-language prompts. These diverse cases illustrate the versatility and transformative potential of generative AI across a wide range of business functions, ultimately driving innovation and efficiency.

Generative AI is poised to exert a significant influence across a wide spectrum of industry sectors. Among these, banking, high tech, and life sciences stand out as industries that could experience the most substantial changes to the percentage of their revenues derived from generative AI. For example, within the banking sector, the adoption of generative AI use could yield an additional value ranging from €200 billion to €340 billion if fully implemented.¹⁰ In the retail and consumer packaged goods sector, the potential effect is equally noteworthy, estimated at €400 billion to €660 billion annually. These estimates underscore the potential of generative AI technology to revolutionize various industries, from financial services to retail, by enhancing productivity, efficiency, and innovation.¹¹ Generative AI holds the potential to reshape the nature of work by enhancing the capabilities of individual workers through automation of specific tasks. Presently, the combination of generative AI and other technologies can automate work activities that consume approximately 60 to 70 percent of employees’ time.¹² This marks a significant increase compared with the previous estimation, which suggested that technology could automate only half of employees’ work time. This accelerated potential for technical automation is primarily attributed to generative AI’s improved capacity to comprehend natural language. This capability is especially valuable for work activities that constitute about 25 percent of total work time. Consequently, generative AI has a more pronounced effect on knowledge work, which is typically associated with occupations that require higher wages and greater educational qualifications compared with other types of work. This emphasizes the transformative role of generative AI in augmenting and optimizing tasks performed by human workers.

The rate of transformation in the workforce is expected to quicken, driven by the expanding possibilities of technical automation. The revised adoption scenarios, which consider factors like technology advancement, economic viability, and the pace of diffusion, result in a projection that about half of the tasks performed in the workforce could be automated between 2030 and 2060. A midpoint estimation falls around the year 2045, which is approximately a decade earlier than previous forecasts offered. This accelerated timeline underscores the growing effect and potential of automation, particularly driven by advancements in technologies like generative AI, and highlights the need for proactive planning and adaptation in the face of these changes. The era of generative AI is in its nascent stage. The enthusiasm surrounding this technology is undeniable, and the pilot projects are showing great promise. However, it’s important to recognize that fully realizing the benefits of generative AI will be a gradual process. Leaders in both the business world and society at large must confront a series of significant challenges along the way. These challenges encompass effectively managing the inherent risks associated with generative AI, determining the novel skills and capabilities that members of the workforce will require, and fundamentally reimagining core business processes, including strategies for retraining and skill development. While the potential of generative AI is immense, it will demand careful planning, adaptation, and a thoughtful approach to addressing these challenges to ensure successful integration into our evolving world.

V. Conclusion

Circularity goes far beyond recycling. It is a complete system, involving changes in business models and product design, as well as collaboration between suppliers and customers. Because technology is constantly changing, the new paradigm of global competitiveness requires the ability to innovate rapidly. This has brought the goals of environmental conservation and competitiveness together. It is important to use resources productively, whether they are natural and physical or human and capital. Both environmental conservation and global competition demand that companies innovate to increase resource productivity.

For example, the growing importance of wind and solar energy and the rise of electric vehicles are all key to the world’s growing need to reduce dependence on fossil fuels, lower carbon emissions, and mitigate climate change. Simultaneously, these burgeoning industries will soon generate tons of waste as millions of photovoltaic solar panels, wind turbines, and lithium-ion EV batteries reach the end of their respective lifecycles. Lithium-ion batteries have been in use since the early 1990s, at first powering laptops, cell phones, and other consumer electronics, and more recently used in electric vehicles and energy storage systems. Electric vehicle companies focus on recycling the valuable innards of these batteries — lithium, cobalt, nickel, copper — especially as automakers ramp up production, including by building battery “gigafactories.” Players in the circular economy are determined not to let all that go to waste, introducing the concept of circular mining.

Climate change is one of the most pressing challenges the world is facing. Extreme weather events, biodiversity loss, infrastructure degradation, and other negative effects on the environment and society will increase if humanity does not take effective action. Addressing this challenge requires a combination of mitigation measures (like reducing greenhouse gas emissions) and adaptation measures (like preparing for the already inevitable effects). It is crucial that individuals, businesses, and governments all take action to address climate change to create a more sustainable future. Blockchain, AI, and even quantum technology are disruptive innovations that could boost a green transition. Blockchain, for example, has the potential to revolutionize the energy sector by enabling the creation of decentralized, efficient, and secure systems to manage energy production, distribution, and consumption.¹³ Through smart contracts, blockchain can facilitate peer-to-peer energy-related transactions, making it easier for individuals and businesses to generate and sell renewable energy to their neighbors. This may lead to a more efficient use of renewable energy sources and reduce reliance on centralized energy providers. Also, blockchain can support the integration of emerging technologies like electric vehicles and smart batteries for energy storage, supporting more flexible and resilient energy systems.