AI Use Cases That Can (and Will) Fix the Christmas Returns Supply Chain Problem

Christmas doesn’t end at the tree.
For retailers and supply-chain teams, it ends weeks later — at the return counter.

Every January, the retail industry enters “Returnuary”: a surge of unwanted gifts flowing back through stores, warehouses, and shipping networks. What starts as holiday goodwill quickly becomes one of retail’s largest operational and financial drains.

Returns are no longer a fringe issue. They are a core supply-chain problem — and AI is becoming one of the most effective tools to address it.

The Scale of the Return Problem

In 2025, 20%–25% of all retail sales are expected to be returned, including nearly 1 in 5 online purchases — representing close to $1 trillion in merchandise based on industry estimates.

The most commonly returned items are predictable:

• Clothing and shoes (fit and size issues)
• Accessories (easy gifts, easy misses)
• Electronics and gadgets (duplicates, defects, or unmet expectations)

The deeper issue is what happens next.

Many returned items never make it back onto store shelves because brands lack the infrastructure to process them cost-effectively.

Returns create reverse logistics flows that are slower, more manual, and more expensive than outbound fulfillment — stressing labor, transportation, inventory visibility, and margins.

Where AI Makes a Real Difference

AI doesn’t eliminate returns — but it reduces volume, speeds processing, and improves recovery value across the supply chain.

Below are five practical AI use cases that are already delivering results, along with one leading startup in each category and why they matter.

1. AI-Driven Fit, Sizing, and Product Recommendations

Reducing returns before they happen

The number-one reason for returns remains unchanged: customers buy the wrong size or fit.

AI models trained on body data, purchase behavior, and product attributes can dramatically reduce this problem by guiding shoppers to better decisions at checkout.

Leading Startup: Bold Metrics

‍Why it helps:
Bold Metrics uses AI to predict accurate body measurements and recommend the correct size with minimal customer input. By reducing size-related errors — the single largest return driver — retailers see fewer unnecessary shipments, inspections, and restocking costs.

Supply-chain impact:
Fewer wrong purchases → fewer reverse shipments → lower return volumes during peak periods.

2. AI-Powered Demand and Return Forecasting

Preparing supply chains for Returnuary

Returns are seasonal, but they aren’t random.

AI can analyze historical sales, promotions, SKU-level return behavior, geography, and external signals to forecast where and when returns will spike.

Leading Startup: o9 Solutions

Why it helps:
o9’s AI-driven planning platform helps retailers forecast both forward demand and reverse logistics flows. This allows teams to proactively plan labor, warehouse capacity, and transportation before returns overwhelm the network.

Supply-chain impact:
Better forecasting → smoother operations → fewer bottlenecks and delays.

3. Automated Return Triage and Routing

Deciding what happens to a return — instantly

Not every return should follow the same path. Some should be restocked, some refurbished, others redirected to resale channels.

AI can automate these decisions at scale.

Leading Startup: Loop Returns

Why it helps:
Loop uses AI to analyze return reasons, customer behavior, and product data to automate return routing — from instant exchanges to store credit or resale workflows. This reduces manual handling and speeds resolution.

Supply-chain impact:
Faster triage → lower labor costs → improved inventory velocity.

4. Computer Vision for Inspection and Grading

Making return processing faster and more accurate

Manual inspection is one of the most expensive steps in return handling.

Computer vision models can visually inspect returned items and determine condition, defects, or resale eligibility.

Leading Startup: Instrumental

Why it helps:
Instrumental applies computer vision to identify defects and anomalies during inspection. Originally built for manufacturing, this technology translates directly to return grading, helping teams classify items faster and more consistently.

Supply-chain impact:
Automated inspection → faster throughput → reduced processing backlogs.

5. AI-Enabled Secondary Market Optimization

Recovering value from returned goods

Many returned products are perfectly usable — just not suitable for resale through primary channels.

AI can help match returned inventory to the right secondary market, channel, and price.

Leading Startup: Recurate

Why it helps:
Recurate enables brands to resell returned and excess inventory through branded resale experiences and secondary marketplaces. AI helps optimize pricing, channel selection, and recovery value.

Supply-chain impact:
Returns shift from pure cost center → partial revenue recovery engine.

The Bigger Shift: Returns as a Strategic Data Problem

What’s changing isn’t just tooling — it’s mindset.

Leading retailers are moving from:

• Treating returns as unavoidable waste
• To treating returns as a data-rich optimization opportunity

AI enables smarter decisions before purchase, at return intake, and after processing — improving margins, sustainability, and supply-chain resilience.

And with 73% of consumers saying AI will make them less likely to return products, adoption is accelerating on both sides of the transaction.

Final Takeaway

Christmas returns expose one of retail’s most painful operational challenges — but they also highlight where AI delivers immediate, measurable value.

From fit prediction and forecasting to inspection and resale, AI is becoming a core capability for managing post-holiday supply-chain stress.

Returnuary may never disappear.
But with the right AI use cases in place, it doesn’t have to break the system.

Scouting For AI Supply Chain Technology in 2026

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