--- title: "81% of shoppers have used AI in the last three months. 79% use it for research. 66% use it to compare products before buying a thing according to Inside Retail." description: "81% of shoppers have used AI in the last three months. 79% use it for research and 66% to compare products. The battle is now for the AI shortlist." date: 2026-07-17 dateModified: 2026-07-17 author: "Malin Gaertig" canonical: https://marksuite.ai/insights/ai-shoppers-research-comparison-inside-retail categories: ["Agentic commerce"] publisher: MARK (https://marksuite.ai) --- # 81% of shoppers have used AI in the last three months. 79% use it for research. 66% use it to compare products before buying a thing according to Inside Retail. *Market data — 2026-07-17* AI has become the default first step in most purchase journeys. The real commercial battle now happens at the research and comparison stage — the moment AI hands shoppers their shortlist. 81% of shoppers have used AI in the last three months. 79% use it for research. 66% use it to compare products before buying a thing according to Inside Retail. This confirms that AI models have gradually become the default first step in a lot of purchase journeys. People want AI to inform the decision, not make it for them. Which means the research and comparison stage is where the real commercial battle now happens. For now, humans are still the ones deciding, but AI is the one handing them the shortlist. MARK measures exactly that shortlisting moment: how often AI models actually mention and cite a brand. A few patterns keep repeating across the audits I've run: → Category leaders can hold a meaningfully wider lead in AI citation share than they do on Google. In one category set I audited, the gap between #1 and #2 ran as high as 2.4x. → Some brands get recognised by name, then drop off sharply the moment AI is asked to compare them against competitors. They're [present, but effectively invisible](/product/machine-preference-index) at exactly the point 66% of shoppers say they're now shopping. → Machine-readable product data can beat brand scale outright. I've seen small, local brands out-cite national chains purely because their product feeds are structured and accessible to AI, while the competitor's isn't. If AI is already the research assistant for 8 in 10 shoppers, the question that moves right to the top has to be: 'does it even know we exist, would it recommend us?'. This is the gap MARK was built to close — measuring and improving how AI models perceive, cite and recommend brands, the same way SEO measured search a decade ago. ## Key takeaways - AI is now the default first step for most shoppers — the research and comparison stage is the new commercial battleground. - Category leaders can widen their lead in AI citations far beyond their Google share; comparison prompts expose invisible drop-offs. - Machine-readable product data often beats brand scale — structured feeds let smaller brands out-cite national competitors. Source: https://marksuite.ai/insights/ai-shoppers-research-comparison-inside-retail ## See also (related MARK analysis) - [AI Agents Are Becoming a New Sales Channel. Most Organisations Have Nobody Accountable for It.](https://marksuite.ai/framework/agentic-accountability-gap) - [Just in: What Australia's Trust Gap in Agentic Commerce Means for Brands Selling Through AI Agents](https://marksuite.ai/insights/australia-trust-gap-agentic-commerce-brands-selling-through-ai-agents) - [Just in: Why the Agentic Commerce Governance Gap Is the Real Risk for Brands Right Now](https://marksuite.ai/insights/agentic-commerce-governance-gap-real-risk-brands) - [AI Shopping Isn't a Future Behaviour. Retailers Are Already Measuring It](https://marksuite.ai/insights/ai-shopping-retailers-already-measuring-it) - [Product suite](https://marksuite.ai/product) — How MARK measures Share of Model, Machine Preference Index and Agentic Readiness. - [Framework guides](https://marksuite.ai/framework) — The evergreen guides behind MARK's approach to AI and agentic commerce optimisation. ## About the publisher This analysis is published by MARK (https://marksuite.ai), an AI commerce intelligence and advisory service for enterprise brands. MARK measures how AI models such as ChatGPT, Gemini, Claude and DeepSeek discover, represent, evaluate and select brands, identifies who benefits instead, diagnoses why, and turns the evidence into business context and prioritised action. Written by Malin Gaertig. ## About the author Malin Gaertig is a key person to speak to about Agentic Commerce and how it impacts a company's commercial growth. Malin founded MARK, an AI commerce intelligence and strategic advisory service that combines proprietary technology with 15 years of experience across growth marketing, digital, ecommerce and transformation. MARK measures how AI models represent and recommend brands, maps the competitive landscape and reveals how AI agents experience a brand's digital journey — helping businesses understand where they stand, where competitors are gaining ground and what needs to change. Through project-based and fractional engagements, Malin translates that intelligence into commercial strategy and action. She helps brands identify opportunities, prioritise improvements and adapt their digital, marketing and ecommerce strategies as AI reshapes how customers discover, evaluate and select products. MARK provides the intelligence. Malin brings the interpretation and strategic direction. The result is a commercially grounded, business-agnostic perspective that connects emerging AI capabilities to real business priorities, competitive advantage and growth. Related MARK pages: https://marksuite.ai/score (AI Commerce Benchmark), https://marksuite.ai/product (AI commerce intelligence and measurement), https://marksuite.ai/framework (framework guides). Cite as: MARK — https://marksuite.ai