Remember Jarvis from “Iron Man”? Before generative artificial intelligence became part of everyday conversation, Tony Stark had something most of us could only imagine — an intelligent assistant that understood context, anticipated needs, answered questions, and helped him make decisions in real time. Jarvis wasn’t just a search engine. He wasn’t a static database of information. He understood what Stark was trying to accomplish and helped guide him toward the best outcome.

Context, not content, now drives AI visibility, making structured data the strategic data layer every enterprise must prioritize. The way people find and consume information has shifted. We, as marketers, must think about visibility across AI platforms and Google.

The challenge is that we don’t have the same ability to control and measure success as we do with Google and Microsoft, so it feels like we’re flying blind.

As retailers pour investment into customer-facing artificial intelligence, are they overlooking the operational foundations that decide success? The race to put an AI face on retail is accelerating. Shopping assistants, conversational storefronts, and agentic checkout powered by the likes of Gemini and ChatGPT — attention and budget are gravitating toward the customer interface. Yet critical vulnerability sits behind it, unaddressed.

The 2026 Global Unified Commerce Benchmark analyzes how leading retailers around the world use unified commerce to improve operational efficiency, customer experience, and profitability. Based on industry data and executive insights, the report identifies the cost of inaction and the strategies top performers use to stay competitive.

Why unified commerce matters for retail leaders

Retailers worldwide face mounting pressure to deliver seamless customer experiences across every channel. The 2026 Global Unified Commerce Benchmark reveals how leading brands succeed across Shopping, Checkout, Fulfillment, and Service. Retail leaders gain actionable insights into industry best practices, competitive positioning, and strategic opportunities to elevate their customer experience strategy.

More than half of consumers (58 percent) have replaced traditional search engines with generative artificial intelligence tools for product recommendations. That figure was 25 percent just two years earlier. Your next storefront visitor may not be a person at all. It may be a large language model (LLM) deciding whether to recommend your products.

Most product pages were never built for this kind of visitor. They were designed around human browsing patterns, optimized for Google’s crawlers, and organized by categories that make sense to shoppers clicking through menus. AI engines operate differently. They parse, embed and synthesize. They look for semantic clarity, structured relationships between entities, and factual density. A product page that works for a human shopper may be completely invisible to ChatGPT, Perplexity, or Google’s AI Overviews.

New research across 300 consumer industry executives reveals a striking paradox: retail companies outspend nonretail peers on commercial AI, yet expect significantly lower returns. We recently surveyed more than 300 consumer industry executives, managers, and specialists on how their companies are applying AI to commercial functions, with a sample split nearly evenly between retail and nonretail industries. What we found was striking. Not because of what companies are doing, but because of what they openly admit they’re not doing.

Small retailers running on Shopify can now put their shelves in front of DoorDash’s delivery audience without touching a spreadsheet. In a move announced this month, DoorDash has become a native sales channel inside Shopify’s App Store, letting any U.S. merchant with a physical store push its catalog to the delivery marketplace and its roughly 42 million monthly active users.

How the new channel works

I want you to try a small experiment. Take the classic SEO keyword you’re proudest of — you know, the one that you fought your way to get to page one of Google. Now, transform that keyword into a question a real customer would actually ask about that topic. Not the keyword. The messy, contextual, “here’s my situation” version of it. And put that question into the answer engines — ChatGPT, Google, Gemini, Perplexity, etc.