No one buys a dishwasher on Tuesday, then wakes up Wednesday thinking: “You know what I could really use? Another dishwasher.”

But that’s essentially how many e-commerce systems still operate. A shopper may spend weeks researching dishwashers—visiting a retailer’s website, using the mobile app, and eventually going to their local store to narrow down the options in-person—before making the big purchase.

Yet the next day, they return to the website, or open an email or text from the retailer, only to find themselves encouraged to buy…another dishwasher. It’s archaic, a little ridiculous, and often the result of a surprisingly simple problem: The systems that registered the shopper’s interest aren’t working from the same context as the one that registered the purchase.

What would be more useful is to recognize the customer’s needs have changed. Maybe now they need installation guidance, maintenance tips, detergent tabs, or something else complementary to what they just bought. But they almost certainly don’t need another dishwasher.

The first wave of GenAI showed what models can do with massive amounts of information, from answering natural-language questions (“Which dishwasher features matter most for a family of four?”) to summarizing complex materials to creating content.

But generating polished responses is different from understanding the big picture—including individuals’ actions, context, and the relationships between signals—and using that to determine what happens next.

To bridge that gap, there’s rising interest across industries in the concept of reasoning engines.

At a basic level, reasoning engines are AI systems that perform logical inference based on real-world inputs, not just questions or prompts people give them.

Source link