Behind the scenes, large organisations are feeding their internal knowledge bases into AI models and threading them into customer workflows. That might mean a tool sitting in a contact centre, a feature embedded in an app or a digital guide on a website.
As these systems learn from manuals, procedures and product libraries, they narrow the gap between limited human capacity and rising customer demand for tailored help. The pattern is similar whether the setting is a hardware warehouse or a bond-trading floor.
Retailer Bunnings is one of the clearest examples, rolling out an AI shopping assistant called Buddy to support complex DIY tasks. Buddy runs on Google’s Gemini platform and is trained on Bunnings’ extensive catalogue of how-to guides and project content.
Customers can snap a photo of a handwritten shopping list, then use Buddy to translate it into products, step-by-step instructions and cost estimates. It also walks users through different product options, helping them compare approaches that might otherwise require a long chat with an in-store expert.
Early deployments like Buddy are changing expectations in very different industries at once. Companies are treating these assistants as a way to standardise best practice and free up specialists for higher-stakes work, rather than replacing them outright.
As more institutional knowledge is digitised and fed into similar systems, the line between human and AI-delivered expertise is likely to blur further across both retail and financial services.
