**Ask any business leader what the circular economy is, and you’ll probably get a well-informed response. But from a practical standpoint, most circular endeavours today remain controlled experiments rather than full-scale transformations.**
And it shows: according [to some estimates](https://www.theworldcounts.com/challenges/planet-earth/state-of-the-planet/world-waste-facts), the world dumps over two billion tons of waste every year. [One measure](https://overshoot.footprintnetwork.org/newsroom/press-release-2024-english/) finds humanity is using resources gleaned from the earth 1.7 times faster than the planet can produce them.
Businesses have had centuries to perfect the linear economy practices of resource exploitation – so what will it take to unlearn those wasteful principles and pivot to a circular mode of operation?
The answer is as old as business itself: economic opportunity. Businesses need a compelling economic reason to make a radical shift from the status quo. However, this will require devising entirely new business models for circular value recovery, which differ significantly from linear economic models. Both short- and long-term value propositions need to be carefully laid out. By defining the market opportunity and mapping the pathways to achieve it, businesses can begin their circular transformation.
### Defining the market opportunity
Today, we are in the early stages of exploring circularity as a business model, not unlike where Netflix was in the 1990s—well before streaming services grew to a $600-plus billion market. However, companies both large and small are establishing significant proof points.
To deliver circularity at scale, businesses must adopt four systems of change into their operations:
**Design for Sustainability:** The principles of Design for Manufacturability/Reliability (DFM/DFR) are traditionally driven by product economics. Design for Sustainability (DFS) complements many of these established practices but also contradicts some. Serviceability, extensibility and recyclability become important principles to retain and recapture value throughout a product’s lifecycle and beyond. AI-driven models can help simulate and optimise the lifecycle value of a product, making it more valuable to maintain or upgrade, including through software-driven lifecycle extensions.
DFS also drives changes to product structure and material supply chains to enhance environmental efficiency in transport, by reducing material consumption promoting recyclability and draw down on raw material consumption. For instance, materials like plastics and hazardous materials, such as the PFAS commonly used in textiles, should be minimised.
Automaker Volvo intends to be a fully circular business by 2040, in part through material reduction and sourcing more durable materials that can be reused and recycled into new cars. Its EX30 model, for example, is made of 17% recycled materials, and Volvo hopes to increase that to 25% recycled or bio-based materials in its new car models.
**Supply chain traceability:** Data which enables traceability in the supply chain is critical to re-engineer carbon-intensive, one-way supply chains and create more economic opportunity across communities, including the ones in which the products are used. Rather than exploiting the environment or labour for raw materials, the idea is to extract parts, components and materials and reintroduce them into the supply chain or use them in new applications.
**Manufacturing, remanufacturing and reverse logistics:** Businesses must establish strategies for the reintroduction of recovered material. The reduction of single-use and intermediate materials in the manufacturing process, such as packaging, is also important. Data models, such as Digital Product Passports and Lifecycle analysis (LCA) are critical in enabling the design of the extended supply chain, contract manufacturing arrangements and logistics processes.
For example, when GE Healthcare’s older medical equipment is not suitable for refurbishment, desirable components are redeployed as reusable parts or recycled. According to GE, 82% to 100% of a system may be recyclable.
**Lifecycle revenue models:** Finally, circularity requires a rethink of the product itself, the market in which it’s sold, the ecosystem that supports that market, and the value delivered to customers. Manufacturers of large assets, such as turbines and earthmoving equipment have had to think beyond being a manufacturer to being an owner of a fleet of assets which deliver revenue ‘by the hour’. It is also critical to consider an integrative view of lifecycle costs to maximise value beyond manufacturing. Remote asset management, digital field services and enhanced aftermarket service models become significant levers for reducing Scope III emissions and improving sustainability.
In summary, establishing a scalable, circular business model requires transforming many operating processes. Generative AI (Gen-AI), IoT and a whole spectrum of technology levers play a critical role in designing, simulating and ultimately scaling circular transformations across organisational stakeholders and supply chains.
### Getting the circle spinning – How AI and allied technologies help build momentum
Circularising operations will require a combination of incremental and holistic change. Businesses can jumpstart their circular efforts by focusing on four key principles.
**Tweak and transform:** It’s important to engineer a system of change – this will allow companies to capitalise on near-term value opportunities to prove the concept, then work toward a more fundamentally transformative opportunity. AI-driven system design also helps deliver a flexible enterprise technology framework to support this evolution
**Integrate downstream visibility:** Whether through takeback programs, subscription services or reverse logistics processes, businesses need to track what happens to their products and the materials they contain throughout their entire lifecycle. While much of this information exists within enterprise systems of record, Gen-AI and other technologies can consolidate this data to identify opportunities which lead to circularity.
**Use technology to drive collaboration and empowerment:** Gen-AI, machine learning and IoT enabled operations all help solve a crucial transformation challenge. However, there simply isn’t enough engineering expertise within many companies to effectively design changes in how various roles will transform and processes should evolve.
**Establish correlation between business and customer goals:** Circular business models must benefit both businesses and customers. Today, ESG reporting largely informs investors and industry watch groups. Re-interpreting ESG data for business performance and creating more authentic, material value propositions for customers will accelerate this transformation.
The circular economy introduces a profitable path away from exploitative business models that no longer have a place in the modern world. It’s a matter of bending the linear trajectory of doing business into a circle — a circle with global benefits for people, the environment and a profitable future.
_This piece was contributed to E+T by Manoj Mehta, president at Cognizant Europe, Middle East and Africa._