Design Systems Struggle to Keep Pace With AI-Generated Interfaces

Component libraries work for human designers. Algorithmic tools may need different approaches.

Osman Gunes Cizmeci maintains design systems for products used by millions. The work involves balancing consistency with flexibility—a challenge that intensifies as AI tools generate interface elements automatically.

“Design systems assume human designers who understand context,” the New York practitioner said. “AI just sees patterns and rules.”

The tension highlights a broader question: can traditional design systems govern AI-generated interfaces?

The Control Problem

Design systems work through shared understanding. Designers know when to break rules. They recognize edge cases where standard components fail. They balance consistency with user needs.

AI lacks that judgment. Tools like Figma’s AI Component Creator generate elements that technically follow system guidelines but miss contextual nuances.

Osman Gunes Cizmeci describes reviewing AI-generated designs. The spacing is perfect. Colors match brand standards. Typography follows hierarchy. But something feels off—the composition lacks the subtle decisions that make interfaces feel considered. His analysis of AI integration examines these emerging tensions.

“The system defines what’s allowed, not what’s good,” he said.

He points to challenges around governance. Design systems rely on human gatekeepers who evaluate contributions. AI generates faster than teams can review. The volume overwhelms traditional approval processes.

The Flexibility Paradox

Strict design systems prevent AI from making obvious mistakes. But they also constrain helpful variations.

Osman Gunes Cizmeci notes that context matters. A button on a checkout page needs different visual weight than the same button in settings. Good designers adjust. AI follows rules literally. His design work demonstrates this contextual approach to interface decisions.

“You end up choosing between inconsistency or inflexibility,” he said.

Some teams respond by loosening constraints. Others add more rules to cover edge cases. Both approaches introduce problems. Loose systems produce chaotic outputs. Rigid systems stifle adaptation.

The solution, Osman Gunes Cizmeci suggests, requires rethinking design systems entirely. Instead of rule-based frameworks, systems might need to encode design principles and intent—qualities AI could interpret contextually.

Documentation Challenges

Design systems communicate through written guidelines and visual examples. These work for designers who read documentation and apply judgment.

AI needs machine-readable specifications. Semantic structure. Metadata about component purposes. APIs for programmatic access.

Osman Gunes Cizmeci watches teams struggle to maintain both human-readable documentation and machine-readable specifications. The dual requirement doubles work without clear benefit. His perspective on systems addresses these documentation challenges.

“We’re documenting the same decisions twice in different formats,” he said.

He advocates for documentation that serves both audiences—structured data that humans can parse and machines can query. The approach requires upfront investment but reduces long-term maintenance burden.

The Adaptation Path

Design systems must evolve or risk irrelevance. Osman Gunes Cizmeci sees three possible futures.

First: Systems tighten control, limiting AI to narrow use cases. This maintains quality but sacrifices speed.

Second: Systems loosen constraints, accepting inconsistency as cost of AI efficiency. This maintains pace but degrades experience.

Third: Systems become intelligent—encoding principles rather than rules, adapting to context rather than enforcing uniformity. His approach to evolution reflects this forward-thinking perspective.

“The third path is hardest,” he said. “But it’s probably inevitable.”

He notes that design systems already struggle with scale. Adding AI accelerates existing problems. Teams either solve fundamental issues or watch systems collapse under complexity.

The profession faces a choice: redesign systems for algorithmic reality or maintain frameworks built for human workflows that AI increasingly bypasses.