Why most AI product recommendations fail (and how to fix it)

I recently had a customer ask me about the AI capabilities of StoreConnect. I had to smirk a little when I asked how they planned on using it. They just looked back and said, “You tell us, because we haven’t had a good experience so far.”

I was not surprised. AI is easily the technology of our age, but most businesses are struggling to produce something reliable that actually impacts the bottom line without ruining the customer experience.

The biggest limitation of AI right now is control. When you ask a generic AI to give you an answer, you are not in control of what comes back. In eCommerce, a “hallucination”, where the AI makes up a product or a price, isn’t just a glitch; it’s a lost customer and a hit to your brand’s data integrity.

We are taking a different approach. We are using our advanced Liquid capabilities to provide the AI with dynamic choices.

As a customer moves through your store, the system gathers snippets of context: where they have been, how long they spent on a page and which categories they have engaged with. We then hand the AI a very specific, small set of real data and say: “Recommend the best choice from this list.”

By narrowing the field to real products and real context, we remove the risk of hallucination altogether. The result is a recommendation that is predictable, useful and most importantly, correct.

The best part? This all happens first-party and server-side. Nothing is shared with external platforms and nothing leaves the Salesforce security model.

Most people try to “save” a sale with an abandoned cart email after the customer has already left. We are using AI to provide that personalized, dynamic content in real-time, while they are still on the site. That is how you turn AI from a buzzword into a conversion engine.

Ready for AI that actually works? See how we use real-time data to power personalized experiences.