Consumer Readiness for AI-Assisted Shopping
New research surveying 2,000 consumers in the USA and UK reveals strong demand for AI in online shopping, with 72% expecting AI assistants to enhance efficiency through features like deal alerts (59%), personalised recommendations (51%), and gift ideas (44%). However, trust remains fragile: 69% would abandon a platform after one irrelevant suggestion, 24% over data handling concerns, and 21% if AI decides without input.[8]
Early adopters, particularly 25- to 34-year-olds (59% usage rate versus 34% overall), show higher positivity, with 77% trusting brands more for offering AI assistants. Among them, 81% accept AI building full carts for occasions, and 88% value bundle suggestions, signaling progression to complex tasks.
Rising Adoption and Generational Shifts
AI usage in UK shopping has doubled year-over-year from 12% to 28%, led by Gen Z (43%) and Millennials (42%), with 18% of Gen Z and 15% of Millennials as first-time users in the past year.[2][3] Nearly half of UK shoppers (44%) are open to AI handling entire journeys, including purchases, once preferences like budget and features are set—rising to 49% for Millennials, 41% for Gen Z, and 42% for Gen X.[2]
Retailers align, with 84% open to AI completions and 49% prioritizing it; 50% plan AI expansions for customer experience.[2] Under-45 UK adults show 30% openness to AI as personal agents for recommendations, delivery checks, and buys.[7] Globally, 78% of consumers used AI tools like ChatGPT recently, hitting 93% under 35.[7]
Implications for E-Commerce Infrastructure
This demand pressures product feeds, where irrelevant outputs from poor data quality trigger 69% churn. High-fidelity feeds—rich in attributes like variants, pricing, and compatibility—become essential for AI relevance, as incomplete data amplifies errors in real-time suggestions.
Cataloguing standards must evolve to support agentic AI, emphasizing structured schemas for attributes beyond basics (e.g., occasion-fit, bundling potential). Legacy inconsistencies slow AI training, widening the readiness gap where only 27% of UK retailers deem stacks scalable for autonomous experiences.
Card quality and completeness directly impact trust: sparse descriptions or missing images undermine 51% expectation for personalised recs, while full metadata enables proactive bundles (88% early adopter appeal). Retailers forecasting 2026 online growth (80% in UK) link AI to conversions, but skills gaps and compliance hinder execution.[1][6]
Accelerating Through No-Code and AI Tools
No-code platforms bridge adoption barriers, enabling rapid feed optimization and AI integration without heavy dev costs—critical as 90% of global retailers plan AI spend for operations.[6] These tools automate catalog enrichment, speeding assortment updates to match dynamic consumer queries like price drops (59% demand).
AI-driven no-code handles guardrails from first interaction, ensuring data responsibility (24% concern) via anonymized training. This supports speed: faster indexing of updated catalogs keeps pace with 44% readiness for end-to-end automation, turning early trust (77%) into repeat engagement.[2]
Internet Retailing; Adyen Retail Report 2026.
From a NotPIM perspective, this research highlights a critical inflection point for e-commerce. Consumer expectations for AI-driven shopping experiences are rising, yet the performance of these tools hinges on the quality of product data. Retailers must prioritize high-fidelity product feeds to meet this demand and build customer trust. NotPIM provides a no-code solution to enrich and standardize product data, allowing businesses to adapt quickly and deploy AI-enhanced experiences with confidence. The quality of product data is discussed throughout this article. Managing this data is a key element of the e-commerce infrastructure. Learn how to better understand your data with our articles on product feeds at /blog/product_feed/ . Furthermore, for better data management, read more to improve your understanding of how to create sales-driving product descriptions. With efficient management of your product data, you are better equipped to handle a price list processing program .