Target is rearchitecting its entire technology stack to operate on AI, a shift the retailer says has already cut apparel design cycles from 42 weeks to just a few. The announcement, made by executives during a Bengaluru media briefing in July 2026, signals a fundamental redesign of how the company builds products and manages data across its business.
Prat Vemana, Executive Vice President and Chief Information and Product Officer at Target, said the company was moving beyond simply layering AI onto existing systems. "We define this as moving from using AI to run on AI," Vemana said. "Run on AI requires us to fundamentally rearchitect the stack and rethink the data ecosystem."
Rearchitecting for an AI-native future
Vemana explained that traditional enterprise systems were designed for human consumption, while future AI agents would need data structured with richer context to make autonomous decisions. The retailer is redesigning its data architecture, analytics capabilities, and responsible AI framework together, rather than deploying isolated AI applications. This approach ensures AI agents can access contextual data, reason across enterprise workflows, and operate within governance guardrails.
The goal is to prepare systems for agentic AI-applications that can act independently on behalf of the business. Vemana said the transition required rebuilding the company's AI stack, data layer, and analytics layer as a unified foundation.
Product design cycles shrink from 42 weeks to weeks
The most visible outcome has been in apparel product development. Target's shift to AI-driven product development slashed design cycles from 42 weeks to weeks, a dramatic example of AI for Product Development in action. Vemana said fashion retailers once relied on designers studying runway collections over months, but social media has compressed that timeline.
"We are now in a few weeks, not 42 weeks," he said. Faster product cycles have become critical as trends emerge and spread rapidly through platforms like Instagram and TikTok, forcing retailers to respond much faster than before. Vemana noted that AI was only one part of a broader transformation that also involved process improvements and closer supplier integration.
AI embedded across merchandising and supply chain
Brad Thompson, Senior Vice President of Technology, said AI had become integral to nearly every aspect of Target's operations. Merchants now use AI to analyze consumer trends and business performance during weekly planning sessions. The retailer also uses AI to recommend localized assortments for individual stores based on neighborhood demand patterns.
Target has developed a digital twin of its supply chain that lets teams simulate operational changes before implementing them. The technology helps assess the impact of adding new distribution nodes or modifying replenishment strategies. Inside stores, AI optimizes order fulfillment by identifying the most efficient routes for employees picking online orders, reducing travel time across large-format retail locations.
AI is also generating multiple contextual versions of product imagery, enabling more relevant personalization for individual shoppers based on browsing behavior and purchase history. Thompson said the company uses AI to recommend complementary products and tailor promotional offers to encourage purchases across additional categories.
Workforce evolution and responsible AI
AI is not replacing employees, executives said, but it is changing how work gets done. Thompson said AI was allowing software developers, designers, product managers, and data scientists to perform parts of one another's work, speeding up the journey from idea to working prototype. Vemana added that every role was evolving as AI became embedded into daily work, making continuous learning and adaptability increasingly important. "I feel like we're in the beginning of the transformation," he said. "The more you adapt, the easier it gets."
Target established a Responsible AI governance body when generative AI tools first emerged. The group, which includes representatives from technology, legal, compliance, and human resources, reviews AI use cases, vendor contracts, and internal policies to ensure alignment with the company's responsible AI principles.
Why this matters for product development
Target's experience shows that AI can compress product development timelines by an order of magnitude-from months to weeks-without sacrificing quality. For product development professionals, the shift means moving beyond using AI as a point tool and toward rethinking entire workflows, data architectures, and decision-making processes. The companies that thrive will be those that prepare their teams, data, and governance structures for an AI-native operating model, rather than bolting AI onto legacy systems.
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