Not at all. Design for AI becomes relevant the moment a product starts making decisions, predictions, or recommendations on behalf of users. That could be a complex AI system or something much simpler, like a smart workflow or an adaptive interface. What matters is not how “advanced” the AI is, but how its behaviour impacts people using the product.

It usually starts with understanding intent. We spend time unpacking what the system is meant to do, where intelligence fits in, and how much control users should have. From there, we shape flows, interactions, and decision points that make the system understandable and trustworthy. The process is collaborative and grounded in real product constraints, not abstract theory.

We work with both. Early-stage teams often need clarity before patterns harden, while mature products need recalibration as intelligence is layered in. The approach shifts depending on the stage, but the core focus remains the same: designing experiences that people can rely on, grow with, and feel confident using over time.

Traditional UX often assumes predictable systems and fixed outcomes. AI changes that. Design for AI deals with uncertainty, learning systems, and evolving behaviour. It requires thinking beyond screens and flows, and into trust, intent, feedback loops, and long-term relationships between users and systems.

Teams usually come away with clearer product direction, stronger alignment between technology and experience, and a shared language for making future decisions. More than polished interfaces, the outcome is confidence: knowing why the product behaves the way it does and how that behaviour supports real human needs.