An AI assistant asking, “What are you looking for?” may sound intelligent. But if it responds with irrelevant products, the conversation ends quickly.
Latest research* shows that consumers are not waiting for retailers to give them a futuristic shopping robot. Or the loudest AI feature entertaining consumers. They are waiting for brands to understand what they want. Recommendation engines are filling this need much better today.
How do work recommendation engines?
A recommendation engine works differently. It can learn from browsing behavior, purchase history, preferences, price sensitivity, product relationships, and real-time context. It quietly improves discovery without demanding that the shopper explain everything.
That makes recommendations a powerful first step toward AI adoption.
When shoppers repeatedly receive useful suggestions, they begin to trust the system. That trust can then lead to more advanced behavior:
- 61% of consumers are interested in product bundles and complementary products.
- Among existing AI users, that figure rises to 88%.
- Interest in having AI create a complete shopping cart increases from 48% among all consumers to 81% among existing AI users.
Consumers become more willing to delegate once the technology has proven itself.
In other words, the shopping cart is not the starting line. Relevance is.
The Real Problem: Retailers Are Underprepared
Consumer interest is growing, but retailer investment is lagging. Only 36% of retailers have a dedicated AI budget, despite roughly two-thirds of consumers already using or showing interest in AI shopping agents.
This creates a dangerous mismatch.
Customers are becoming more accustomed to personalized digital experiences, while many retailers are still working with fragmented data, incomplete product information, and limited personalization infrastructure. Launching an AI assistant on top of that foundation will not solve the problem. It may simply expose it.
An assistant cannot recommend what the retailer’s data does not understand.
Stop Chasing the Most Advanced Use Case
Retailers do not need to begin with a fully automated shopping agent. They need to begin with useful experiences that work.
A smarter rollout might look like this:
- Improve product data and customer profiles.
- Deploy personalized product recommendations.
- Add price-drop alerts, gift inspiration, and complementary-item suggestions.
- Measure relevance, engagement, conversion, and abandonment.
- Introduce conversational and automated shopping features only after trust has been earned.
This approach may not generate as many headlines, but it is more likely to generate results.
The most recognizable AI model is not automatically the best retail solution. Capability matters more than brand recognition. Retailers should choose technology based on its ability to understand intent, connect with customer data, and consistently produce relevant recommendations.
You don’t need to launch the loudest AI feature
Retailers may be spending too much time asking, “How do we launch an AI assistant?”. The better question is: “How do we make every product recommendation more useful?”
A recommendation engine that consistently shows the right products can deliver a better experience today. It is familiar, low-friction, and easier for consumers to accept. More importantly, it creates the trust required for tomorrow’s AI assistants.
The brands that win will not be the ones that launch the loudest AI feature. They will be the ones that make shopping feel effortless – one relevant recommendation at a time.
Research source: https://www.emarketer.com/content/ai-retail-interest-high-poor-execution-driving-abandonment
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