The use of artificial intelligence in adapting process of UI design system for end customer requirements

This paper demonstrates an approach for developing an AI-based UI design system to improve a company white labeling (aka rebranding) process. This is the process of removing a product or service's original branding and replacing it with the branding of another company or individual. The main objectives of the research include the development of methods for optimizing rebranding, automating the delivery of designer work results, and achieving project-wise improvement in the design adaptation process for the end distributor, known as the white-labeling process. The research objective is to analyze the existing rebranding process and to analyze ready-made solutions using artificial intelligence to improve it. This research identifies innovative methods for implementing artificial intelligence in the rebranding process to facilitate and speed up tasks related to design and marketing. Research methods include analyzing existing rebranding practices, considering ready-made solutions using artificial intelligence, and conducting experiments and practical application of new methods to improve the process. The scientific novelty of this research lies in the implementation of artificial intelligence in the rebranding field and the development of effective methods for its improvement. As a result, improvements are achieved through the deployment of an AI-driven solution, meticulously engineered around the design token concept, serving as a pivotal element for standardizing and harmonizing the work of designers. This methodology involves a comprehensive adjustment of the AI model to seamlessly integrate with existing design systems, thereby facilitating the transformation of design systems and brand books into tangible design tokens. The process of integrating AI into design workflows involves extensive model training using openly accessible community data. Careful consideration is given to the selection of datasets, ensuring that they meet rigorous criteria for evaluating the quality and efficacy of artificial intelligence learning. These criteria encompass factors such as data relevance, diversity, and representativeness, as well as considerations for ethical and legal compliance. As a conclusion: by leveraging this meticulously crafted approach, organizations can effectively harness the power of AI to drive transformative change in design processes, ultimately enhancing efficiency, consistency, and innovation across their operations. By adopting various AI integration aspects, this paper provides an updated UI design process with the ability to use AI during client-centric design development.