AI can generate design concepts faster than ever before. In seconds, it can produce dozens of variations, explore different styles, and help teams imagine possibilities that might have taken days to create manually. But speed alone does not solve the hardest part of design: making ideas that are both on brand and production ready.

Today’s AI-powered workflows are still a patchwork of workarounds. We feed screenshots into models, provide JSON styling instructions, or connect AI tools to design libraries. These methods can spark ideas, but they rarely produce assets that are ready to ship, reusable across projects, or anchored in a single source of truth. The result is a gap between concept and reality — one that requires significant manual work to bridge.

Where Current Workflows Struggle

When using screenshots as AI input, the model only sees a fragment of the design. Without full context, outputs often drift from the reference. The result can be visually inconsistent with the brand, requiring spot editing that slows momentum.

Providing AI with JSON or code-based styling instructions adds more structure, but sacrifices visual context. Each new project requires its own setup, and those instructions rarely carry over to future work. This creates repetitive tasks and still leaves room for inconsistencies that need to be corrected later.

Integrations like Figma Make or MagicPath can accelerate idea generation, but they also face limits. Without detailed annotations, the AI has little understanding of intent. Assets created in these environments may be hard to reintroduce into Figma or keep in sync with evolving design systems. In all cases, the result is the same: promising concepts that require rebuilding before they can be used.

The Cost of Rebuilding

When design outputs from AI are not aligned to a single source of truth, scaling becomes harder with each project. Designers and developers spend time reconciling mismatched components, layouts, and styles instead of focusing on higher-value work. Interactions get lost in translation, and elements have to be recreated from scratch.

This slows delivery and reduces the consistency that makes a product feel intentional and trustworthy. For teams working at scale, these gaps add friction to every stage of the process.

Where the Industry Might Go Next

One possible direction for closing this gap is anchoring AI workflows to a baseline design system in code. This could give AI the context it needs to generate concepts that are closer to production-ready while maintaining brand integrity. Over time, AI could learn the visual language, components, and interaction patterns of a brand — producing ideas that are easier to refine and reuse.

This approach would not eliminate the need for designers or developers. Instead, it could reduce the amount of rework between concept and production, making it easier to move quickly without losing alignment. For now, this remains an area of experimentation, but it is one that could reshape the relationship between AI-generated creativity and production design.

The Opportunity Ahead

The challenge is clear: AI excels at generating possibilities, but struggles to deliver assets that are ready to ship within an established system. Closing that gap will require tools that understand not only how something looks, but how it works, how it scales, and how it stays true to a brand over time.

At Db_studio, we are closely watching this space and exploring how these emerging capabilities could change the way teams move from vision to execution. The promise is compelling, but the path forward will require both better AI tools and thoughtful integration into real-world workflows.