AI in circular business models: How to remove friction

Circular customer offerings often fail not because customers reject the idea, but because the experience still feels slower, more expensive or more complicated than the current, linear alternative.

Circular business models keep products, components and materials in use for longer through models such as repair, trade-in, resale, refurbishment and recovery. The challenge is not only building the model. It is making each step simple enough for customers, teams and partners to choose it over the linear alternative.

This article explores where AI can help to remove that friction across the customer journey - from marketing and sales to service and asset recovery.

The next generation of circular leaders will not win by simply asking customers to behave more sustainably. They will win by making circular choices feel easier, faster and more commercially sensible than the linear alternative.

Why use AI in circular business models?

AI matters in circular business models because it helps teams make high-value decisions faster and with less guesswork. It can identify which customer is most likely to accept a trade-in, estimate item recovery value, recommend whether an item should be repaired, refurbished, resold, harvested for parts or recycled, and help teams choose the next best partner or message. When those decisions stay slow, manual or inconsistent, circular offers become harder to scale and less appealing to customers.

Decisions like these are not abstract sustainability decisions, but commercial co-ordination decisions. If not resolved effectively, they can become significant blockers that prevent businesses from scaling circular experiences sustainably and cost-effectively.

The biggest gains do not come from one AI tool in isolation. They come from using AI across the full journey: spotting demand sooner, showing recovery value at the right moment, personalising the offer, routing items well and proving the case for scale.

Where AI creates commercial advantage in a circular model

01 - DETECT

Find the right circular opportunity

Use signals from returns, repair demand, asset age, resale potential and service costs to identify where circular demand is most commercially viable.

02 - VALUE

Make recovery value visible

Instant estimates for trade-in, refurbishment or parts value turn circularity into a clearer financial proposition for customers and frontline teams.

03 - PERSONALISE

Tailor the exchange

Different customers respond to different triggers: convenience, savings, flexibility, warranty, performance or impact. AI helps match the message to the moment.

04 - ROUTE

Send each item to its best next life

The commercial win comes from better routing decisions: repair, refurbish, resale, parts harvest or recycle.

05 - ENABLE

Help the frontline act faster

Retail, service and partner teams need prompts, scripts, thresholds and next-best actions that make circular participation easier to support.

06 - PROVE

Link performance to scale

Connect AI to conversion, return rates, time to recirculate, recovery margin, CLV and trust so leaders can see whether the model deserves more investment.

AI is most useful when it improves circular decisions across the full commercial system, not when it sits as a disconnected innovation layer.

In the electricals and electronics market, Material Focus estimates that UK households are sitting on 880 million unused electrical items, with the materials locked inside the nation’s “lost electricals” worth around £927 million.¹That is a far more relevant circular commercial signal than returns: the barrier is not whether value exists, but whether brands make repair, trade-in, resale and recovery simple and accessible enough. AI can help brands identify which users are most likely to participate, for which types of electrical product, and personalise the value proposition at the point of need. AI can then help to orchestrate the partner, pricing and service journeys needed to turn dormant assets into active demand.

This in turn creates a new commercial opportunity for businesses. If brands can use AI-generated intelligence to remove friction in repair, trade-in, resale and recovery journeys, circularity becomes a simpler and more credible alternative for customers, turning dormant value into a compelling customer proposition.

How AI can improve repair, trade-in, resale and recovery journeys

In practice, brands get value from AI when it removes effort at the points where circular participation usually breaks down. That includes valuing returned products, making the offer feel worthwhile, routing items to the right next use, and preparing operations for repair-led regulation.

Firstly, AI can make a product’s recovery value visible to the customer at the moment it matters. For example, Levi’s already gives customers a direct route from old denim to store credit, with trade-in values based on age, condition and likely resale value.² This offering translates circularity into something immediate and concrete. The next commercial opportunity would be to make those valuations quicker, more tailored, and more naturally embedded into browsing, purchase and service journeys, via their app, website or in-store.

Secondly, AI can turn circular infrastructure into a credible customer proposition. Currys has already started to translate circular infrastructure into a more tangible customer proposition via the Cash for Trash and trade-in offers at the point of purchase of a replacement item, e.g. a washing machine or fridge freezer. These give customers a simple reason to return old tech back into the system, and also fulfil a need to dispose of their old product. Separately, its Newark repair and recycling operation routes these items through repair, refurbishment, parts harvesting or responsible recycling. Last year alone, Currys claims to have collected 5.5 million pieces of e-waste across the Group, equivalent to 87,000 tonnes, while customers using its trade-in programme each received an average value of £137 in 2024/25.³ AI can deepen commercial advantage for both customers and Currys, through quicker valuation, better routing, more relevant offers and less friction across the full loop.

Thirdly, in the B2B space, AI can help brands and recovery systems work from the same evidence to generate accurate, meaningful data about circularity. London-based Greyparrot, a waste analytics platform, has launched Deepnest, an AI platform built to close the information gap between brands and waste systems by showing the role of packaging in real recovery infrastructure.⁴ This gives brands, recyclers and operators a shared evidence base for spotting where materials are lost, where design choices undermine recovery, and where better co-ordination could improve circular performance while reducing future Extended Producer Responsibility (EPR) exposure.⁵

Finally, AI can help to prepare for emerging circularity regulation, helping brands prepare for a market where repair becomes more central, not less. The EU’s Right to Repair Directive comes into effect on 31 July 2026, designed to increase consumer rights to repair and reuse options both within and outside the legal guarantee period.⁶ For UK brands selling into Europe, that is not a distant policy footnote but a real consideration for building circular offerings. They will need to consider repairability, service economics and spare-parts access - with AI providing opportunities to better predict potential faults, improve diagnostics, when to prioritise repair versus replacement, and support clearer customer communication.

The most useful way to think about AI in the circular economy, then, is not as a shiny layer on top, but the connective tissue between commercial intent, commercial execution and customer experience.

AI can help marketing and sales teams identify where circular demand is strongest and help customer experience teams reduce the effort of participation. It can help pilots scale by improving the speed and quality of routing decisions and help partner ecosystems collaborate around shared value. Finally, it can help leadership teams build a more credible business case by linking participation, recovery value and loyalty to measurable outcomes.

How circular advantage themes translate into AI-enabled execution

IDENTIFY THE OPPORTUNITY

Read market and operational signals sooner

AI helps combine demand, returns, service and product data to identify where a circular proposition has the strongest odds of commercial success.

POSITION FOR CIRCULAR ADVANTAGE

Design a better value exchange

Sharper valuation, better timing and more relevant messaging make circular offers easier to understand and easier to choose.

CO-ORDINATE THE MIX

Synchronise marketing, sales, service and partners

AI can become the decision layer that aligns customer comms, recovery flows, repair options and partner handoffs.

MEASURE AND SCALE

Turn evidence into investment confidence

Leadership can use better intelligence to see whether circular journeys are increasing participation, improving economics and justifying scale.

This keeps the argument broad and toolkit-led rather than anchored to one template or one isolated AI use case.

Questions leaders should ask to identify where AI can help their circular business models

For commercial leaders, that means asking a different set of questions when designing or scaling circular business models. Where is friction highest in our circular journey? Which decisions are still too manual? Where do customers lose confidence? Where are we destroying value because we cannot assess, route or communicate fast enough? And where would better intelligence genuinely change behaviour rather than just generate more reporting?

The brands that answer those questions well will not just run nicer pilots. They will build circular experiences that are easier to choose, easier to operate and easier to scale. That is where AI starts to create real commercial advantage.

That is exactly what our Circular Advantage Toolkit is designed to support. It helps teams identify the right circular opportunity, shape a stronger value exchange, co-ordinate adoption across the system and build the evidence needed to scale with confidence.

👉 Download The Circular Advantage Toolkit

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