Development of an Integrated Branding Platform Based on Generative AI and a Comparative Usability Study Between Designers and Non-Designers

Background : Generative artificial intelligence (GenAI) tools have expanded access to brand design, yet existing platforms remain fragmented, requiring users to manually transfer outputs across multiple independent tools for naming, color, logo, and visual identity generation. This fragmentation causes workflow discontinuity and visual inconsistency, undermining the strategic coherence essential to brand identity systems. While multimodal AIGC integration has been explored at the model level, practical platform-level solutions for design workflows remain limited. This study addresses this gap by proposing Branderia, an integrated branding platform built on a data-continuity architecture that automatically propagates outputs across modules to maintain semantic and visual coherence throughout the brand design process.<br/>Methods : The research followed a three-phase design process. First, a tool evaluation framework was established using seven metrics (accuracy, creativity, brand alignment, visual quality, consistency, efficiency, integrability) that integrate criteria from AIGC evaluation literature and brand design research. Second, an AI expert study group (n=8) evaluated candidate tools across four categories (naming, color, logo, image) through standardized protocols, selecting Gemini, Huemint, Smashinglogo, and Midjourney for integration into Branderia via a Figma-based prototype. Third, 50 participants (professional designers (n=20) and non-designers (n=30)) completed branding tasks using the platform. Usability was measured across five ISO 9241-11 dimensions (efficiency, effectiveness, ease of use, learnability, satisfaction) using 7-point Likert scales. Task completion times were recorded, and independent samples t-tests were examined between-group differences.<br/>Results : The platform achieved strong usability (M=5.61/7.0, Cronbach’s α=0.93) with an average task completion time of 23.86 minutes, substantially reducing time when compared to conventional multi-tool workflows. Both groups produced equivalent output quality (M=5.68) and brand consistency (M=5.50), validating the platform’s democratization goal. However, designers completed tasks 33.2% faster (19.9 vs. 26.5 min) and reported significantly higher ease of use (5.90 vs. 4.63, p<.001) and satisfaction (6.32 vs. 5.39, p<.001), reflecting efficiency gains through mental model alignment with the platform’s sequential workflow. Conversely, non-designers rated practicality higher (5.83 vs. 5.50, p=.032), viewing outputs as final deliverables rather than refinement starting points. These differences reveal expertise-dependent value mechanisms: the platform amplifies efficiency for experts while providing guided scaffolding for non-experts.<br/>Conclusions : This study demonstrates that data-continuity-based integration of AIGC tools resolves workflow fragmentation and maintains brand coherence while delivering differentiated value across user groups. The findings establish that output quality parity does not equate to process experience parity, leading to a design principle for integrated AIGC systems: adaptive usability is essential. Future platforms should implement dual-mode architectures (guided scaffolding for non-experts and advanced controls for experts) accessing the same generative backbone to ensure output consistency while optimizing process experience. By reframing the challenge from “what AI can generate” to “how humans orchestrate multiple AI tools coherently,” this study provides actionable guidance for designing multimodal AIGC systems that amplify human creativity.