AI in wealth management is entering a phase where being “smart” is no longer enough. Users have grown used to systems that can calculate, summarise and surface recommendations in seconds. Yet many still walk away with the same nagging impression: the technology is impressive, but it doesn’t really understand them. That disconnect often comes down to one thing—context.

fincite, which offers software to revolutionise investment, recently delved into why context is the backbone of great AI experiences in wealth management. 

The difference between intelligence and relevance is what makes an AI experience feel either helpful or hollow. Plenty of AI products can process data, identify patterns and produce plausible answers. But without a sense of what the user is trying to do right now, what matters most, and what constraints they are operating under, the output can feel emotionally tone-deaf, fincite explained.

One of the most common user experience pitfalls is what could be called the “blank canvas trap”. An empty chat box might look clean and simple, but for many users it creates friction rather than freedom, it said. A stronger AI UX is one that meets users where they are, offers orientation, and earns permission to go deeper.

This is where context becomes more than a feature; it becomes a design principle. In AI experiences, context is the system’s ability to understand the task at hand, the goal behind it, and the user’s rhythm—where they are in a journey and what they likely need next. The technology stops acting like a tool the user must operate and starts behaving more like a partner that can guide.

A practical framework for context-aware AI UX starts with “context first”, fincite said. Users should not have to learn how to “talk right” to an AI to get value from it. The burden should sit with the system: interpret the situation, reduce ambiguity and move the interaction forward without forcing the user to build everything from scratch.

Second is “right format”. The best response is not always a long explanation. Sometimes it is a chart, a short summary, or a single clear next step. Third is “visible reasoning”. If an AI suggests an action, it should show why. In a regulated environment, that transparency is essential for trust and accountability.