For a leading home furnishings company, returns management is central to operational efficiency and customer experience. As return volumes increased, existing processes relied heavily on manual decision-making and fragmented systems, leading to delays, higher operational effort and inconsistent outcomes.

To address these challenges, the retailer sought to explore how AI-led automation could improve returns decisioning, reduce manual effort and support faster, more consistent resolutions. HCLTech partnered with the client to demonstrate the potential of AI-native approaches to modernize returns workflows while aligning with business rules, policies and risk considerations.

The existing returns processing system relied heavily on manual decision-making across fragmented systems, limiting visibility and slowing resolution times. Nearly 80% of the effort was concentrated in manual reviews, creating operational bottlenecks and inconsistencies across teams.

As return volumes increased, especially during peak seasons, overall cycle time extended to more than a week, making it difficult to deliver consistent outcomes or scale efficiently. Limited automation further constrained fraud detection and risk management capabilities. This led to delayed resolutions, higher operating costs, increased exposure to compliance and policy risks, and a measurable impact on customer experience and trust.

Together, these challenges reduced operational efficiency and increased exposure to compliance and policy risks.

The client sought to reimagine returns management through AI-native capabilities that could transform the function from a manual, reactive process into a scalable, intelligence-driven operation.

The aim was not only to reduce manual effort, but to embed intelligent automation into the decision layer to enable consistent, policy-aligned outcomes at scale.

The objective was to demonstrate how AI-native capabilities could strengthen fraud detection, enhance risk governance, and create a more agile operating model that can adapt to fluctuating volumes and evolving return scenarios.

The focus was to:

To address these gaps, HCLTech supported the development of an AI-native returns management solution by implementing a streamlined Proof of Concept built on Azure AI-based Agentic AI frameworks. This was designed to automate decision-making and improve operational efficiency.

The solution introduced intelligent AI agents that interpreted return policies in real time, validated eligibility, performed automated fraud checks and recommended or executed decisions without requiring constant human intervention.

The approach focused on embedding intelligence directly into workflows to support faster, more consistent outcomes while maintaining control and compliance.

The solution brought together the following capabilities:

The AI-led solution delivered measurable improvements across delivery efficiency and execution speed while reducing manual effort.

This resulted in:

The client valued the consistency and dependability this transformation brought to their operations. By introducing structured workflows and intelligent automation, the returns function became more predictable, responsive and easier to manage at scale, strengthening confidence in the systems that support day-to-day business.

HCLTech delivered more than operational improvements. This initiative established a scalable foundation for intelligent automation—one that balances speed with oversight and enables the business to grow without adding operational complexity.

Beyond measurable outcomes, the collaboration created a resilient, future-ready returns ecosystem that enables growth without added complexity or risk.

With an AI-enabled returns processing framework, what was once a manual, fragmented function has evolved into a resilient, automated ecosystem that adapts in real time to demand, risk and customer expectations.

This partnership with HCLTech is a testament to what focused expertise, modern AI frameworks and structured execution can achieve together.

With AI-enabled workflows in place, the client is better positioned to continue exploring intelligent automation across customer-facing and operational processes, supporting more consistent experiences and informed decision-making at scale.