Perkembangan teknologi kecerdasan buatan (AI) telah memberikan dampak signifikan pada industri desain, salah satunya melalui penggunaan plugin AI di Figma yang meningkatkan efisiensi kerja dan hasil inovatif dalam alur kerja yang kompleks. Penelitian ini bertujuan untuk menganalisis pengaruh karakteristik tugas dan teknologi dari teori TTF serta faktor-faktor dalam teori UTAUT terhadap niat pengguna dalam menggunakan plugin AI di Figma. Dengan pendekatan mixed-method, data dikumpulkan melalui wawancara secara daring dengan 12 responden dan menyebarkan kuesioner kepada 466 responden. Melalui analisis kualitatif ditemukan tiga dimensi task characteristic (task variability, task time sensitivity, dan task creativity) serta empat dimensi technology characteristic (perceived intelligence, adaptability, responsiveness, dan perceived novelty). Pengolahan data kuantitatif dilakukan dengan metode Structural Equation Model Partial Least Square (PLS-SEM). Hasil penelitian ini menunjukkan bahwa task-technology fit, performance expectancy, effort expectancy, social influence, dan perceived risk memengaruhi niat penggunaan plugin AI di Figma, terkecuali untuk facilitating conditions yang tidak berpengaruh secara signifikan. Pendekatan ini diharapkan memberikan wawasan tentang faktor-faktor yang memengaruhi niat awal pengguna dan menjadi panduan bagi pengembang AI untuk mendorong adopsi teknologi AI dalam desain.

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The development of artificial intelligence (AI) technology has had a significant impact on the design industry, particularly through the use of AI plugin in Figma that enhance work efficiency and foster innovative outcomes in complex workflows. This research aims to analyze the influence of task and technology characteristics from the Task-Technology Fit (TTF) theory, as well as factors from the Unified Theory of Acceptance and Use of Technology (UTAUT), on users' intention to use AI plugin in Figma. Using a mixed-method approach, data was collected through online interviews with 12 respondents and a questionnaire distributed to 466 respondents. Qualitative analysis identified three dimensions of task characteristics (task variability, task time sensitivity, and task creativity) and four dimensions of technology characteristics (perceived intelligence, adaptability, responsiveness, and perceived novelty). Quantitative data analysis was conducted using the Structural Equation Model Partial Least Square (PLS-SEM) method. The results of this study reveal that task-technology fit, performance expectancy, effort expectancy, social influence, and perceived risk influence the intention to use AI plugin in Figma, except for facilitating conditions, which did not have a significant effect. This approach is expected to provide insights into the factors influencing initial user intention and serve as a guide for AI developers to promote the adoption of AI technology in design.