Today's Shopper adoption of AI capabilities
Shoppers today increasingly turn to AI-powered tools to streamline their online buying journeys, making real-time decision-making faster and more confident. According to Adobe Analytics, 39% of U.S. consumers have already used generative AI for shopping, with another 53% planning to do so this year 1.
These tools aren’t limited to casual browsing; they actively compare prices, track deals, and form tailored shopping lists. Indeed, AI-assisted shoppers complete purchases 47% faster and exhibit conversion rates nearly 4× higher—12.3% versus 3.1%—when engaging with chatbots. 2 Even more compelling, returning customers who use AI-driven chat spend 25% more than those who don’t. 3
Wider adoption reflects deeper integration: pre-2025 forecasts show the global AI-enabled e-commerce market hitting $8.65 billion 4, and today 89% of companies are either using or piloting AI in retail contexts 5. Shoppers’ comfort with conversational agents is also rising: 54% would use a chatbot to inquire about a product, and 27% interact daily with such systems. 6 Notably, nearly 60% of consumers have used AI to support their shopping, and 77% say it significantly speeds decision-making—with almost half trusting AI recommendations more than friends when choosing what to wear.7
Over time, the shift has evolved from experimental use cases to mission-critical commerce tools. Platforms like Amazon’s shopping guides and AI-powered deal alerts demonstrate consumers’ growing reliance on AI for everything from price comparison to product selection. 8 Combined, these insights show AI is rapidly changing shopper behaviour—filtering information overload, increasing confidence, and making smarter, faster purchases the new normal.
A Real-World Example: From Search to Selection
Imagine this real interaction:
"Find me a party dress for an outdoor summer wedding in Portugal. I want something breathable, made from natural fibres, and under €200."
Conversational AI interprets the shopper’s natural language query and extracts key product criteria: price, fabric type, and intended use. The structured product data must contain attributes like material (e.g., linen, cotton), use case (party, wedding), and pricing. Generative AI ensures that the product descriptions are rich, localized, and optimized for SEO, making them easier for AI to index and compare. Agentic AI then filters and compares options, scores relevance based on the user’s intent, and even adds the ideal product to the basket. This seamless interaction is only possible when the underlying product content is complete, structured, and accessible to AI agents in real time.
Reference Source(s):
1 https://www.marketwatch.com/story/how-to-use-ai-to-find-the-best-amazon-prime-day-deals-6dedb8c9
2 https://www.zendesk.com/blog/ai-chatbot-trends-2024
3 https://visionx.io/how-generative-ai-chatbots-boost-customer-loyalty-and-retention
5 https://www.emarketer.com/content/5-key-stats-on-rise-of-agentic-ai-retail