Many AI systems fail not because models are weak, but because they prioritize capability over real-world problems. Effective design starts by defining the decision, the user, and common failures before selecting a model. This approach shifts focus from pure intelligence to practical outcomes.

Design must account for messy, interrupted workflows and human behavior, not just ideal conditions. Systems need clear boundaries for accuracy, error rates, and escalation rules. Context—historical data, user preferences, domain rules—must persist across interactions to enable learning and trust.

Testing must cover end-to-end workflows, edge cases, and user reactions, not just model metrics. Planning for predictable failure with fallbacks and clear error signals is essential. The goal is building trustworthy systems that fit the real world, not just impressive demos.