As fashion increasingly embraces AI, its frontend and backend operations are getting transformed into a more accessible and connected ecosystem.
Brands and manufacturers are fusing technology with fashion, an AI-powered partnership that reimagines what it means to be efficient in the current era.
Prada is now on ChatGPT
If the devil wears Prada, then OpenAI isn’t far behind.
A new ChatGPT plugin allows users to explore all things Prada through casual conversations, expanding access to the luxury fashion house through one of the world’s largest AI platforms.
Available in the United States, the United Kingdom and Italy, the plugin was developed by Prada and Accenture Song, the tech-creative arm of global professional services company Accenture, to explore what agentic commerce means for luxury. This was also done with support from AI native platform DaVinci Commerce.
“Conversational experiences represent a natural evolution of traditional e-commerce,” said Laura Campanella, Prada Group digital director.
“Through this pilot initiative, we are exploring a new territory for customer engagement that enriches the brand’s digital ecosystem,” she said. “By reimagining how audiences discover and interact with the brand, we can create new opportunities for dialogue and engagement.”
Alessandro Diana, lead for Italy and Greece at Accenture Song, said luxury is increasingly defined not only by quality but also by consistency.
“With Prada, we are exploring how trusted brand content can meet consumers at the moment of intent and turn emerging channels into meaningful opportunities for engagement,” Diana said.
Changshin taps Palantir
Changshin, a Korean footwear company that manufactures shoes for Nike, is tapping Palantir Technologies to connect information and workflows from footwear development and sample planning through factory production.
Under the partnership, Changshin will apply Palantir’s Artificial Intelligence Platform (AIP) and Palantir Foundry, an enterprise operating system. This builds on a recent AgentCamp, where Changshin engineers and Palantir worked together with live Changshin data to demonstrate initial workflows.
The applications are meant to enable Changshin teams to identify issues earlier, reduce handoff friction and use production outcomes to inform future decisions.
“Decisions made in development define how we operate on the factory floor. We want to connect those decisions with production feedback so our teams can spot issues earlier, coordinate more effectively, and carry what they learn into the next model,” said Changshin CEO Choongil Nam.
“This partnership will help us turn our deep manufacturing expertise into distinctive capabilities unique to Changshin, embedding data and AI into the way we work,” he added.
Profitmind’s AI compares competition like no other
A competitive analysis seems deceptively simple. Does a competitor sell the same product? If not, what is the closest comparable item that can substitute for it?
The question gets complicated when applied to the fashion industry, given how large the assortment is, according to an AI platform called Profitmind. Take the black ribbed midi dress, for example.
Not all clothes of the same type are described the same. One retailer can list the same black ribbed midi dress as a “sleeveless rib-knit column dress” while a competitor lists it as a “fitted midi tank dress,” Profitmind said.
On the surface, they sound like two different dresses. But commercially, they’re actually competing products. To reconcile this, retail teams often have to do things the hard way—comparing product identifiers when available, exploring different keywords, or even doing manual competitive shopping if necessary.
The problem with this, Profitmind said, is that these approaches don’t work efficiently at scale, especially across large, frequently changing assortments wherein retailers describe and classify comparable products differently.
Profitmind, which describes itself an agentic AI decision intelligence platform built specifically for retail, said in a recent statement that its product-matching model, which was trained on more than 200 million product listings analyzed over the past five years, can resolve this issue.
Profitmind Awase evaluates a product’s image, description and technical specifications together and identifies the corresponding items in competing catalogs without requiring a shared identifier. Awase identifies exact matches when they exist, and the closest comparable product when they do not.
Since the model started running in production among Profitmind clients in April 2026, the company said only fewer than 1 percent of matches have been rejected.
“The hardest matches hinge on subtle attributes that models trained purely on web data miss,” said Jeff Rix, head of AI at Profitmind. “Two styles can look almost identical while differing in fabrication, pack size, model year, or another key attribute that changes the match.”
“Merchants know which details matter,” he emphasized. “We used that judgment to teach Profitmind Awase how to make those distinctions consistently across large assortments.”
Relex is orchestrating AI agents
Relex Solutions announced new agentic operating capabilities that allow companies to orchestrate AI agents and data across business operations under one platform, instead of having them operate in silos.
AI agents across functions can share context and coordinate recommendations, so decisions in one function account for their impact elsewhere.
That means visibility across merchandising, supply chain, operations, and finance, identifying priority actions when performance changes, and tracing the decisions behind each recommendation.
The platform opens Relex to external systems, agents, partners, and customer teams through APIs, plugins, and other open standards, letting organizations connect existing technology and build new capabilities without replacing what already works.
“Giving every function its own AI agent does not create a better-run business if their decisions don’t work in sync,” said Mikko Kärkkäinen, Group CEO and co-founder at Relex Solutions.
“Our experience supporting hundreds of retailers, wholesalers, and manufacturers has given us the domain expertise and AI foundation to help companies move toward more adaptive, coordinated operations,” he said.
Alexa for Shopping can now do so much more
Amazon has added over a hundred new capabilities its AI shopping assistant, Alexa for Shopping. This is only available for Amazon customers in the U.S.
Among other features, Alexa for Shopping can now maximize and manage subscriptions, update delivery instructions, and even resolve payments and billing questions.
This can be accessed by tapping the Alexa icon in the Amazon Shopping app or find Alexa for Shopping at the top of your screen on desktop. Then, customers can just ask and Alexa for Shopping takes care of the rest, Amazon said in a recent statement.
“Our vision for Alexa for Shopping is to make every part of shopping as convenient as possible,” said Rajiv Mehta, vice president of Conversational Shopping at Amazon. “And this is just the beginning. We’re continuing to invent new ways to save customers time and make shopping even more personal.”
