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AI helps manufacturers turn sustainability into bottom-line advantage. 

 

TAKEAWAYS:
● By integrating generative and agentic AI, a product’s life cycle can become far more intelligent, adaptive, and circular.
● Circular supply chains lower emissions, build resilience, and can serve as a protective strategy against global trade disruptions.
● Circularity, initially a sustainability imperative, is now a competitive one.  

 

 

Circularity and AI are converging to define the next generation of manufacturing. At the heart of this convergence is an understanding that smarter resource use is not only a sustainability strategy but also a source of economic value. By integrating AI into core operations—especially generative AI (GenAI) and agentic AI—manufacturers can unlock new efficiencies, such as extending product life cycles, reducing waste, and delivering measurable bottom-line impacts alongside environmental, social, and governance (ESG) gains. This is part of the reason NTT DATA’s recent global GenAI survey found that 94 percent of manufacturers plan to increase GenAI investments over the next two years. The goal is intelligent, adaptive, and circular ecosystems that yield both environmental and financial returns.

AI Is Making Circularity a Business Imperative

Every transformation begins with a shift in perspective, and circularity is no exception. AI is reshaping the fundamental principles of circularity in manufacturing. While traditional approaches focus on end-of-life recovery, today’s circular models are increasingly proactive and data-driven. AI transforms what were once linear and consumption-driven supply chains into adaptive resource ecosystems capable of tracking and optimizing material flows, enabling smarter strategies for reuse, repair, and repurposing throughput of a product’s entire life cycle.

The urgency for this shift is growing. Information technology now contributes nearly 4 percent of global CO₂ emissions—twice that of the aviation sector. With AI workloads and data center demands rising exponentially, that figure could surpass 10 percent within a few years. Consequently, the challenge is both immediate and significant. However, AI-enabled techniques such as predictive modeling, real-time diagnostics, and advanced asset tracking provide manufacturers with the ability to better determine when equipment truly needs replacing and when it can be refurbished or repurposed instead.

By embedding intelligence into infrastructure and operations, manufacturers are redefining what it means to be efficient. Circularity, once viewed as a constraint, is becoming a driver of resilience, cost control, and strategic advantage.

Evolving the Product Life Cycle

Once we’ve redefined the problem, the next step is to reshape the product’s life cycle from a straight line to a circle. Manufacturers have long viewed life-cycle management through the lens of maintenance schedules and product recalls. But when we integrate GenAI and agentic AI, the life cycle becomes far more intelligent and adaptive. What emerges is a circular model that learns, anticipates, and evolves new ways to reduce waste and generate value at every stage.

AI-powered modeling tools can now map carbon hotspots across products and systems, revealing opportunities to reduce environmental impact from the earliest stages of design. These insights support choices like designing for disassembly or selecting materials that are easier to reuse. Such actions can simultaneously reduce both emissions and downstream costs. One example of this is digital twins—virtual replicas of physical assets that help monitor real-world conditions to anticipate wear and failure. A strategy based on predictive maintenance enables manufacturers to reduce energy use, avoid unnecessary replacements, and prolong asset life without compromising performance or productivity.

Nevertheless, information gaps often derail circularity, particularly concerning complex remanufacturing, reverse logistics, or component reuse. This is where generative AI steps in. It can reconstruct lost documentation, automate parts mapping, and generate service manuals on the fly. These capabilities extend utility, preserve revenue, and keep materials in productive use, reclaiming value that might otherwise be written off.

Manufacturers Are Already Realizing Returns

Theory meets practice in the real world, where manufacturers apply, adapt, and validate the utility of circular principles. A compelling example of AI-enabled circularity comes from NTT DATA’s work with a global technology company seeking to decommission aging servers. AI helped identify downstream use cases as alternatives to sending servers to scrap. The client was able to repurpose some servers as thin clients, finding a second life in applications with lighter performance demands, such as ATMs and point-of-sale systems. Even components at their true end-of-life were recycled back into the OEM value chain, reducing the need for virgin materials.

The results were tangible: lower embedded carbon emissions, reduced capital outlay, and extended life-cycle utility that contributed to both sustainability goals and bottom-line performance. This is what circular intelligence looks like in practice. It’s about keeping materials out of landfills and designing systems that learn from every step, adapt to changing conditions, and deliver value long after their first use.

Predictive, Efficient, and Circular Supply Chains

Even if a particular application of circularity principles proves successful, no life cycle functions in isolation. Behind every product is a supply chain, and circularity begins well before end-of-life, at the source. This is particularly evident in the supply chain. Traditionally designed for speed and cost, most supply chains are reactive: they respond to disruptions after they happen. AI is flipping that model, enabling supply chains to become increasingly sustainable, intelligent, efficient, and economically resilient.

By leveraging real-time data with intelligent algorithms, manufacturers can anticipate material flows, track embedded emissions, and dynamically adjust procurement to favor reused or recycled inputs. AI consolidates and contextualizes key data points about products that might otherwise be siloed—such as the origins of components, manufacturing processes, and future applications. These insights lead to better sourcing on the front end, reduced waste later in the product life cycle, and smarter ongoing inventory decisions.

Agentic AI, which can autonomously act on behalf of humans, plays a pivotal role here. It can automate supplier communications, flag opportunities to repurpose aging components, and identify high-impact redesigns for circularity. This represents an evolution beyond automation for efficiency into value-driven orchestration of sustainability and supply continuity.

“Circularity, once viewed as a constraint, is becoming a driver of resilience, cost control, and strategic advantage.”

 

The result is optimized emissions tracking and decision-making that steers purchasing toward suppliers with lower-carbon or circular offerings. It involves designing disassembly pathways from the outset and optimizing logistics for both material recovery and delivery speed.

Smart disassembly and AI-guided recycling are especially critical when it comes to rare earth elements. These materials are essential to electronics but are expensive, environmentally damaging to extract, and often sourced from geopolitically sensitive regions. By recovering them from end-of-life products, manufacturers can reduce risk, lower material costs, and enhance supply security.

Circular supply chains lower emissions and build resilience. They can also serve as a protective strategy against global trade disruptions, such as those caused by tariffs, as maximizing the utility of existing equipment can mitigate the impact of higher import prices. In this way, they unlock economic benefits—from reducing exposure to price volatility to more efficient capital use. By tightening the loop between procurement and recovery, companies gain greater control over costs and create supply models that are agile by design.

Shift from Energy Use to Material Intensity

If reducing energy use was a first target, the next focus should be the materials that shape our industrial landscape. Foundational industries like steel, cement, and chemicals account for more than a sixth of global CO₂ emissions. Any serious push toward decarbonization must address how we power production, what we’re producing, and how often it’s discarded. It must also consider how efficiently we extract value from every unit of material consumed.

AI plays a crucial role in this shift. Manufacturers can use advanced modeling and simulation to design products that deliver the same performance with less material. They can track real-world usage to identify components suitable for reuse or remanufacture. They can even predict potential degradation patterns and take preemptive actions before waste occurs, thereby reducing both environmental impact and unnecessary reinvestment in materials and equipment.

“The benefits of circularity are often framed in environmental terms, but for manufacturers, the strategic upside is equally compelling.”

 

Some of the most exciting gains are happening at the intersection of different sub-verticals. Cross-industry data sharing, enabled by AI, allows manufacturers to treat waste as a raw material. A striking example comes from the Royal Mint, which recovers gold from discarded electronics—a process informed and optimized by AI. That gold, once considered unrecoverable, is now part of a revenue-generating circular flow.

In this model, circularity moves beyond compliance to encompass collaboration. With closed loops and interconnected networks, waste in one industry becomes a feedstock for another. AI acts as the matchmaker, identifying those opportunities at scale and in real time, and helping convert what was once a loss into lasting economic utility.

Circular Intelligence Is a Strategic Differentiator

What began as a sustainability imperative is rapidly becoming a competitive one. The benefits of circularity are often framed in environmental terms, but for manufacturers, the strategic upside is equally compelling. AI-enabled circular strategies improve agility, reduce dependence on volatile supply chains, and enable businesses to maintain continuity in a world of disruption.

Manufacturers that embed circular intelligence into core operations are better positioned to respond to shocks such as sudden spikes in commodity prices, new emissions regulations, or shifts in customer expectations. Circular models encourage a focus on modular design, easier traceability, and tighter control over materials—choices that support compliance and improve margins, reduce downtime, and build customer loyalty.

AI identifies patterns, flags opportunities, and supports decisions that reduce risk and unlock new value. It helps manufacturers rethink product longevity and customer relationships, resulting in a smarter, more flexible business.

Defining Efficiency by Intelligent Resource Use

When efficiency evolves into circularity, innovation transitions from being extractive to regenerative. The takeaway is that AI-enabled circularity does more than just cut waste: it creates new value from it. This represents a new kind of intelligence that learns from what was previously discarded, sees potential in overlooked resources, and responds to constraints with creativity.  M

Learn more by visiting NTT DATA Sustainability services.

 

About the authors:

 

Paul Schuster is managing director North American Sustainability, NTT DATA.