Fashion brands adopt AI agents for manufacturing and customer engagement

Conversational luxury retail

Prada has introduced a new ChatGPT plugin designed to change how luxury consumers interact with the brand. Developed in partnership with Accenture Song and DaVinci Commerce, the tool allows users in the U.S., U.K., and Italy to explore the fashion house through casual conversation. This initiative aims to move beyond traditional e-commerce by creating a more interactive digital ecosystem.

The project explores the concept of agentic commerce, which refers to using AI agents to facilitate transactions and information sharing. Prada Group digital director Laura Campanella noted that these conversational experiences represent a natural evolution of online shopping. By reimagining how audiences discover the brand, the company hopes to create new opportunities for meaningful dialogue.

Accenture Song emphasized that luxury is increasingly defined by consistency across different platforms. The goal of the Prada plugin is to meet consumers at the specific moment they intend to shop. By using trusted brand content, the company can turn emerging AI channels into consistent opportunities for customer engagement.

Connecting the factory floor

On the manufacturing side, the Korean footwear company Changshin is partnering with Palantir Technologies to modernize its production. Changshin manufactures shoes for Nike and plans to use Palantir's Artificial Intelligence Platform (AIP) and Foundry. These tools will connect information from the initial footwear development and sample planning stages directly to the factory production line.

The primary goal of this integration is to reduce friction during the handoff between different teams. By using live data, Changshin engineers can identify production issues much earlier in the cycle. This allows the company to use actual production outcomes to inform and improve future design decisions. CEO Choongil Nam stated that the partnership will help turn manufacturing expertise into unique capabilities by embedding AI into their daily work.

This move highlights a shift toward data-driven manufacturing where every decision made in the office is visible on the factory floor. By connecting these dots, companies can coordinate more effectively across different locations. The technology helps ensure that the lessons learned during one production run are carried forward into the next model.

Solving the product matching puzzle

One of the most difficult tasks in retail is identifying when two different products are actually competitors. For example, one store might list a dress as a 'sleeveless rib-knit column dress' while another calls it a 'fitted midi tank dress.' Because these descriptions differ so much, retail teams often have to perform manual research to find comparable items across large assortments.

The AI platform Profitmind has developed a product-matching model to solve this specific problem at scale. The model was trained on more than 200 million product listings collected over the last five years. It evaluates images, descriptions, and technical specifications together to identify matches without needing a shared product identifier.

Profitmind Awase identifies exact matches when they exist and finds the closest comparable products when they do not. According to Jeff Rix, head of AI at Profitmind, the model is designed to recognize subtle attributes that standard web-data models might miss. This includes factors like fabrication, pack size, and model year, which are critical for merchants making commercial decisions.

Orchestrating cross-functional agents

As companies adopt more AI tools, a new challenge has emerged regarding how these different systems talk to one another. Relex Solutions recently announced new capabilities that allow businesses to orchestrate multiple AI agents under one platform. This prevents AI from operating in silos, ensuring that a decision made in one department doesn't negatively impact another.

The Relex platform provides visibility across merchandising, supply chain, operations, and finance. By sharing context, AI agents can coordinate recommendations so that performance changes in one area trigger priority actions in others. This creates a more adaptive operation where the decisions behind every recommendation can be traced clearly across the organization.

Relex is opening its platform to external systems and partners through APIs and plugins. This allows organizations to connect their existing technology and build new capabilities without having to replace their current infrastructure. Group CEO Mikko Kärkkäinen noted that giving every function its own agent only works if those agents can work in sync to run a better business.

Expanding consumer AI assistants

Major tech platforms are also expanding the capabilities of existing AI shopping assistants for the general public. Amazon recently added over a hundred new capabilities to Alexa for Shopping for customers in the United States. These updates allow the assistant to handle more complex tasks like managing and maximizing subscriptions.

Users can now use Alexa to update delivery instructions or resolve specific payments and billing questions through the Amazon Shopping app. These features are accessible on both desktop and mobile devices, allowing customers to simply ask the assistant to take care of the task. Amazon stated that the goal is to make every part of the shopping journey as convenient as possible.

This expansion represents a move toward more personal and automated shopping experiences for the average consumer. By handling administrative tasks like billing and delivery updates, the AI reduces the time customers spend on manual management. Amazon officials noted that these updates are just the beginning of new ways to save customers time and personalize their experience.