AI Came to Retail But Never Made It Past the Storefront

In 2024 and 2025, retail industry forecasts from The National Retail Federation, Deloitte, and others predicted that 2025 and 2026 would mark AI's shift from pilot projects to production across consumer shopping, supply chains, factory and store operations, and personalized customer experiences.

Two years later, that transition has been uneven. Consumer-facing AI has largely met expectations, while supply chain, factory, and in-store AI have advanced more slowly. The difference reflects the environments in which retailers are deploying AI rather than the technology itself.

What Delivered

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The category that has met or exceeded predictions is the one that required the least physical integration. 

Adobe reported a 1,950% year-over-year increase in retail site traffic from AI chat interactions during 2024's Cyber Monday. AI traffic to US retail sites surged 1,324% between October 2024 and May 2026. 

Walmart, Target, and Amazon all deployed consumer-facing AI shopping tools in 2025. OpenAI launched shopping integrations with multiple retail partners including Walmart and Target, initially through an Instant Checkout feature before evolving toward dedicated in-app retail experiences as the technology matured.

Deloitte's February 2026 agentic commerce report found that 63% of global retailers now agree companies without AI agents will fall behind within two years.

The Stord State of AI in E-Commerce 2026 report found that early adopters leading in AI-driven personalization are achieving up to 40% higher revenue than non-AI counterparts, while 95% of retailers say that AI is helping decrease operating costs. Early adopters reported a 20% to 30% reduction in inventory levels through predictive models. 

Consumer-facing AI moved faster since it required no physical infrastructure change, regulatory compliance framework, or supply chain restructuring. 

It was software layered on top of existing ecommerce infrastructure, a category of AI that could be deployed, tested, and scaled without changing physical operations.

What Did Not Deliver on Schedule

The biggest expectations centered on supply chain AI: autonomous demand forecasting, shipment rerouting, inventory rebalancing, and end-to-end visibility across retail networks.

Sucharita Kodali, VP and Principal Analyst at Forrester Research, argues the comparison between consumer-facing AI and supply chain AI is misleading. 

"AI in supply chain is really machine learning, deep learning, robotics and other back office and warehouse functions. Those have been embedded and adopted by supply chain executives for years. In fact, they are so embedded, no one even calls them AI even though, technically, it is," Kodali said to AIM Media House. 

Kodali argues that if supply chain executives were given that broader definition and then asked whether they had AI, the answer would be near-universal, the adoption story is simply less visible than a consumer-facing chatbot.

Instead, retailers entered one of the most volatile supply chain environments in decades. U.S. tariffs, regional sourcing strategies, new regulations such as the EU Digital Product Passport, and rising compliance burdens forced companies to redesign supply chains at the same time AI optimization was expected to scale. Reports from Everstream Analytics, TradeBeyond, and Inspectorio all point to a shift toward resilience and regional diversification rather than efficiency alone.

The EU Digital Product Passport is creating new data traceability requirements at the product and material level. The Inspectorio State of Supply Chain Report 2026, published June 23, 2026, found that compliance budgets have plateaued even as requirements grow. 

Only 50% of respondents saw their 2026 compliance budgets increase, compared to 75% in 2025. Audit fatigue is straining supplier relationships as suppliers manage overlapping, inconsistent audit requirements from multiple customers and regulators simultaneously.

The supply chain did not reject AI. The supply chain was being restructured by forces that had nothing to do with AI readiness at the same moment AI optimization was supposed to be scaling on top of it.

Walmart illustrates the difference. The retailer invested heavily in supply chain AI, including agentic decision-making and merchant tools for inventory management, introducing Wally, an AI agent for merchants to diagnose out-of-stock and overstock issues. 

Executives say those systems help diversify imports and respond to tariff disruptions, an investment few retailers have the scale or capital to match.

Supply chain AI leaders at Dataiku noted in their February 2026 analysis that, "78% of supply chain leaders anticipate disruptions to intensify over the next two years, but only 25% feel prepared." That gap between anticipation and preparedness is an organizational and geopolitical gap.

Kodali also argues adoption surveys overstate the gap because many measure chatbot usage rather than the embedded machine learning and agentic systems already common in supply chain operations.

The Physical Integration Problem

Factory floor robotics and in-store AI were also expected to accelerate. Yet retail and manufacturing continue to lag industries such as telecommunications and financial services because physical operations are more complex and generate slower returns on AI investments.

The barriers are well documented. Harvard Business Review estimates that 80% of AI projects fail to deliver expected value, while IDC and Informatica found data quality issues affect 77% of organizations pursuing AI. A separate Bain study found 44% of executives cite a lack of in-house AI expertise as a major constraint.

Those organizational challenges are compounded by legacy infrastructure. Stord found that 31% of retail IT budgets are still consumed by maintaining legacy systems, limiting the resources available for AI deployment.

Prologis reported in early 2026 that nearly all of the top 30 North American retailers are deploying automation and that adopters have gained more than 700 basis points of market share since 2019. 

The competitive advantage is becoming clear, but adoption remains slower than many forecasts anticipated.

The Adoption Problem

The gap between prediction and reality is not AI capability but deployment conditions.

Consumer-facing AI scaled because it built on existing software infrastructure. Supply chain and operational AI depended on stable physical systems, clean data, technical expertise, and capital-constraints that proved harder to overcome.

Kodali argues tariffs did not derail supply chain AI because retailers have been redesigning supply networks for nearly a decade, making AI increasingly valuable for rerouting orders and identifying alternatives.

She pointed to the pandemic as proof of concept: "That is in fact how much of the world did manage to get masks and vaccines. The fact that within a year, vaccines were widely distributed is due to machine learning and deep learning discovering drug cocktails, and shipping things as rapidly as possible."

As supply chains become more regional and resilience-focused, AI's role is likely to shift from optimizing linear networks to managing increasingly complex sourcing decisions. By 2030, SPS Commerce forecasts companies will routinely source from three to five regions for key products, with AI determining optimal sourcing by quarter based on costs, risks, and proximity.

The forecasts accurately anticipated AI's capabilities. What they underestimated was how long it would take retailers to build the infrastructure, supply chains, and organizational readiness needed to deploy those capabilities at scale.