AI in the Returns Process: Where Artificial Intelligence Really Helps in E-Commerce

AI in returns management: high potential, still limited adoption

AI is no longer a future topic in e-commerce. In the returns process, however, it is still used only selectively — even though this is exactly where high costs, complex decisions and large data volumes come together.

For the DACH market in particular, the topic is relevant: the EHI study 2025 "Shipping and Returns Management in E-Commerce" shows that automation and AI will significantly shape returns processes in the coming years — with the goal of reducing costs, improving service quality and making complex logistics structures scalable.

AI does not replace processes. It makes them visible, comparable and steerable.

Why AI in returns management is becoming relevant

Returns do not only cause transport costs. They generate effort in inspection, classification, restocking, customer service and inventory steering.

Many of these steps rely on data and recurring patterns. This is where AI plays to its strength: it can analyse large volumes of returns data, identify correlations and prepare or automate decisions.

For merchants, this matters because returns management today is no longer just about processing. It is increasingly about understanding return causes, recognising risks earlier and steering processes more economically.

Where AI concretely helps

1. Predicting returns. The August-Wilhelm Scheer Institute published a 2025 paper on machine-learning approaches for predicting returns in fashion. Features such as material, size or product reviews can be relevant predictors (Source: idw / August-Wilhelm Scheer Institute, 2025).

Several practical applications follow from this:

• Identify orders with elevated return risk.
• Detect patterns in return reasons.
• Surface anomalies in customer behaviour.
• Prioritise inspection processes by risk and value.

2. Automating data analysis. EHI emphasises that data-driven optimisation across the entire logistics chain is gaining importance, and that automation in shipping, packaging and returns processes is a clear trend. For the returns process this means less manual triage, faster decisions and better scalability.

3. Spotting risk and fraud patterns. AI is well suited to detect anomalies in large order and returns streams that static rules miss — from recurring wardrobing to organised return fraud.

Why the market is still at the beginning

Despite the potential, AI in returns management is not yet a standard. Current market data paints a clear picture:

• Only a small share of merchants productively use AI-driven solutions for predicting or analysing returns.
• A further share is evaluating such use cases.
• The majority considers AI relevant in the future, but does not yet plan deployment.

(Source: EHI 2025 / handelsdaten.de — adoption of AI-based solutions for return prediction and analysis.)

A central reason is the data foundation. AI only works well when return reasons, order patterns, product data and process data are available in sufficient quality. Many companies still capture this information manually or inconsistently, which limits operational usability.

The bottleneck for AI in returns is rarely the model — it is the data quality upstream.

What merchants can realistically expect

AI is not a silver bullet against returns. It does not replace clean processes, good product data or solid operational standards.

Its value lies in supporting decisions with data:

• Prioritise inspection processes by risk and value.
• Predict likely returns before shipping.
• Detect patterns in return reasons and customer behaviour.
• Analyse which factors correlate most strongly with returns.

This is economically interesting because not every return should be treated the same. When AI makes differences between products, orders and customers visible, a flat returns process turns into a steerable one.

What this means in practice

Successful AI use in returns management typically follows three steps:

1. Build the data foundation. Capture return reasons cleanly and link them to order, product and customer data.
2. Start small. Begin with a clearly defined use case — e.g. risk scoring at checkout or prioritised returns inspection.
3. Measure economically. Do not assess "AI yes/no" but the effect on return costs, margin and customer experience.

New: Xpeer makes return risk scoring accessible

This is exactly where Xpeer comes in. The Swiss-founded technology start-up is building a solution that estimates the return risk at the moment of purchase, allowing online retailers to adapt conditions such as shipping, payment or return rules accordingly. The underlying basis is anonymised return data collected across e-commerce (Source: Swiss IT Magazine, 2026).

Instead of treating every order the same, the probability of a return per cart becomes visible — and therefore manageable. For categories with return rates of 40 to 60 percent, this is a direct economic lever.

Xpeer translates what AI can do in the returns process in theory into a solution that retailers can use without their own data science team.

Bottom line

AI in the returns process is not an end in itself. It works where returns data is translated into better decisions — earlier, more consistently and more economically precise.

For online merchants in the DACH region, this is a clear lever: those who start capturing and using returns data systematically today create the foundation for returns steering that is not reactive but forward-looking and differentiated.