Going shopping with AI: why understanding consumer attitudes and behaviours around AI are crucial to commerce success

Text by Justin Thomas, VP Sales, EMEA North at Akeneo

AI is already an integral part of the shopping experience today, spanning chatbots, personalised product recommendations, advanced search engines and autonomous shopping agents. This is not just about making what consumers already do easier, but about reshaping the way they discover, evaluate and purchase products online. But while the technology continues to advance at pace, its true commercial potential hinges on one often-overlooked factor: consumer attitudes.

Recent research by Akeneo uncovers a nuanced but upbeat relationship between consumers and AI-driven shopping experiences. It paints a mixed picture of growing awareness, selective engagement and cautious optimism, a picture that retailers must tap into if they are to fully unlock the advantages AI offers.

AI has moved from ‘I noticed’ to ‘I got influenced’

According to the research, 75% of consumers have noticed AI-powered recommendations or chatbots while shopping online. Of these, 44% have interacted with the technology. Moreover, nearly a third (32%) of respondents have completed a purchase based on an AI recommendation, and a striking 84% of those say they were happy with the purchase.

This satisfaction rate should serve as a wake-up call to retailers, that AI is not just a novelty or a gimmick; it directly contributes to consumer satisfaction and purchase conversion.

Where AI is already delivering

Consumers report tangible improvements in the online shopping experience thanks to AI. Key areas include, product recommendations (37%), faster customer support (33%), improved search results (31%), better product information and descriptions (28%) and smarter customer review summaries (27%). These are core components of the commerce journey. Retailers that understand where consumers are already seeing value can double down on these areas to improve satisfaction and drive loyalty.

Trust is the critical barrier

However, the relationship between consumers and AI is still fragile. Only 38% of shoppers who used chatbots were satisfied with the support they received, and just 14% described themselves as “very satisfied.” Meanwhile, trust in brand transparency remains low: 43% of consumers believe brands are not transparent about how they use customer data, and 30% are unsure. This signals an urgent need for retailers to enhance both the performance of AI tools and their communication around data use. Trust and transparency are not just compliance checkboxes, they are foundational to long-term engagement with AI-powered commerce.

Data-driven experiences drive results

AI’s strength lies in its ability to turn real-world data into actionable insights. From customer reviews and search queries to support tickets and sales trends, AI can decode consumer language and intent. That means smarter product tagging, better filtering and richer descriptions that align with how customers actually shop.

For example, a sofa listed as “firm” by a retailer might be more relatable to shoppers when described as “supportive” or “good for back pain,” terms consumers often use in reviews. AI can spot these patterns and adjust descriptors accordingly, leading to more relevant search results and higher conversion rates.

Retailers are already seeing returns on this. According to McKinsey, AI-powered search and recommendation systems are contributing to average revenue increases of 10–12%, with profitability expected to rise 59% by 2035 for businesses investing in AI.

Clean data: the foundation of AI success

But these benefits depend entirely on the quality of data feeding AI systems. Inconsistent or messy product data, such as colour descriptions that vary between ‘navy,’ ‘dark blue,’ and ‘#000080,’ can lead to faulty recommendations, search mismatches and frustrated customers.

To combat this, brands need strong data governance strategies, clearly defined roles for data ownership and cross-functional collaboration between IT, marketing, commerce and merchandising teams.

The shift from transactional to intent-driven commerce

One of AI’s biggest contributions is helping retailers shift from a transactional mindset - sell everything to everyone - to one centred on relevance and intent. It’s about selling the right product to the right customer at the right time.

Consider how AI can adapt in real time. If customer feedback on a fitness tracker starts focusing on sleep tracking features, AI can highlight those attributes in product descriptions and filters, making the product more discoverable for customers seeking sleep-related wellness solutions. This kind of dynamic adaptation ensures that product content evolves with consumer needs, delivering more relevant experiences and reducing friction in the path to purchase.

The future is already here

From Perplexity’s intent-based shopping engine to Sephora’s AI-enhanced virtual try-on tools and Amazon’s conversational assistant Rufus, AI is already setting new standards for the online shopping experience. Meanwhile, OpenAI’s Operator showcases the emerging potential of autonomous AI agents that can browse, compare and even purchase on behalf of consumers.

But none of this matters if retailers ignore how consumers feel about these technologies. Investing in AI without understanding and responding to consumer behaviours, preferences and trust barriers is a missed opportunity; ultimately, this is about listening to consumer sentiment backed by transparency in the use of data and continuous improvement in the human-AI experience loop.

To discover more on how AI is transforming shopping, search, and product experiences, and why clean, structured data is the key to staying competitive in the next era of commerce, download Akeneo’s latest report, The Next Chapter of Commerce.