Design systems grew out of real pain points: fragmented interfaces, duplicate implementation work and drifting standards between design and engineering. Teams built shared foundations—components, tokens, patterns and docs—to keep large orgs aligned. That core still matters. But the term “design system” undersells the work and misleads people into treating it as a static UI toolbox rather than an evolving set of decisions. Engineers often translate design language. As AI steps in, that translation layer vanishes and gaps in context become critical. AI doesn’t just pull in components. It needs rules about when to bend a pattern, which accessibility trade-offs to accept, how voice shifts across features, and why past exceptions exist. Those details live in Slack threads, old tickets and tribal knowledge—not in a component library. When AI generates at scale, missing context leads to product drift. Prompts yield locally sensible UIs that stray from invisible constraints, and small divergences compound into structural inconsistency. The answer isn’t to widen component libraries but to formalize “product context.” Design system teams must shift from shipping artifacts to maintaining intent: define boundaries instead of policing consistency, build machine-readable rules alongside human docs, and bake in governance, content principles and risk tolerances. AI needs clear constraints, not extra pixels. A solid design foundation remains vital, but the real work now is modeling and operationalizing the surrounding context.