The numbers are in: the 2025 holiday shopping season broke records, according to Adobe. But higher holiday spending comes with a catch, more retail returns.

Returns are no longer just a back-office concern, they’re reshaping unified retail planning, inventory and pricing strategies. As consumer preferences shift faster than ever and supply chain constraints force decisions with incomplete information, retailers must treat returns as a strategic advantage, not just an operational challenge.

Where returns pressure shows up first in 2026

Return pressure starts in the backroom, it starts at the register and in chat support. Retailers tighten return policies to control leakage, only to see the customer experience suffer when rules feel inconsistent across store, app and support. Confusion drives customer complaints and weakens customer satisfaction.

Most policy tension in 2026 comes from three levers customers notice immediately:

• Shortened return windows in high-volume categories.
• Return fees for repeat patterns.
• Instant refunds for low-risk loyalty tiers.

Retail teams that operationalize returns forecasting based on returns expectations and vice versa can anticipate category spikes, schedule store labor and support coverage before friction shows up.

Operational strain raises the true cost of returns

Returns volume drives operational costs in areas retailers monitor daily: store labor, contact center workload and refund exposure in weekly financial reviews. Costs increase when managers approve exceptions and teams process the same return multiple ways, creating more escalations. In this environment, workflow discipline matters more than new policy language. Retailers that adopt AI-enabled returns management reduce manual exceptions and keep associates focused on serving customers instead of debating return rules.

Returns become a merchandising and availability problem

Returns shape what customers can buy next.

When returned units don’t get back into sellable inventory fast enough, customers see missing sizes online, stores lose replenishment for proven sellers and pricing teams react with heavier markdowns. This mismatch compounds profitability pressures.

Peak weeks expose these gaps, and teams that prepare using AI to navigate the holiday returns rush can keep store execution and customer communication consistent during the surge.

Return fraud grows when visibility breaks

Growing returns volume also expands the surface area for return fraud, especially when store history and digital history don’t connect cleanly. Aggressive restrictions often punish loyal customers, while repeat abusers adapt. Better outcomes come from segmentation that protects good customer paths while tightening controls on clear risk signals.

Retailers that pair fraud controls with operational discipline often achieve sustainable returns management, because consistent handling choices reduce waste, unnecessary movement and the gray area where fraud can hide.

Return rates turn into a SKU-level planning signal

In 2026, return rates expose what customers reject after purchase, not how they browse. That signal highlights quality gaps, fit issues, confusing product content and vendor inconsistency, and those drivers track closely to consumer behavior and shifting consumer expectations. Returns data supports practical merchandising actions that will help retailers to:

• Reduce chronic high-return items.
• Rebalance size curves and fit profiles.
• Improve product displays.
• Adjust vendor buys and packaging specs.
• Localize assortments based on return risk.

Apparel teams that want a structured approach often lean on modern returns management practices to connect returns management back to buying and assortment decisions.

Channel alignment decides the returns experience

Returns friction rises when store execution, ecommerce confirmations and support scripts don’t match. That mismatch undermines omnichannel strategies and fragments return experiences, especially when stores absorb buy-online-return-in-store volume without a returns-ready service model. Retailers that plan the post-holiday period as a season often use driving sales during the holiday return season to keep service levels stable while protecting store throughput during heavy return weeks.

Artificial intelligence reshapes returns decisioning

Artificial intelligence supports probability scoring for likely returns, recommended resolutions for service teams and decisions that keep sellable inventory available for the next customer.

That depends on technology solutions that connect transactions, loyalty signals and item history into one view.

Retailers that operationalize AI-enabled returns management reduce manual exceptions and keep rules consistent across store and digital.

Boost retail returns value with invent.ai

In 2026, returns serve as a customer promise, a store workload driver and a merchandising signal all at once. Teams that treat returns as a side process pay twice: once in operational costs and again through lost availability and weaker customer loyalty.

This framing reflects how retail teams describe day-to-day pain: manual processes, fragmented visibility and slow decision cycles that force reactive work. Invent.ai offers an AI-Decisioning Platform that simplifies returns management across all locations and operational needs. Connect with a retail AI expert to get started today.

Linda Marley is VP of Strategic Accounts at invent.ai.