Penelitian ini bertujuan untuk menganalisis faktor-faktor yang mempengaruhi intensi adopsi kecerdasan buatan (AI) pada Usaha Mikro, Kecil, dan Menengah (UMKM) di Indonesia. Dengan menggunakan model Technology Acceptance Model (TAM) dan Technology Organization Environment (TOE) Framework. Metodologi yang digunakan meliputi pengumpulan data kualitatif melalui wawancara mendalam dengan pemilik/manajer UMKM. Penelitian ini dilakukan menggunakan pendekatan mixed-method dengan 11 responden wawancara daring dan 342 responden kuesioner dimana kedua jenis narasumber merupakan pelaku UMKM yang mengerti pemahaman umum mengenai AI. Penelitian ini mengidentifikasi variabel-variabel seperti Employee Capability, Financial Resources, Top Management Support, Competitive Pressure, dan Vendor Support yang berkontribusi terhadap perceived ease of use dan perceived usefulness untuk mengetahui apakah variabel-variabel tersebut mempengaruhi ke variabel intention to adopt. Analisis data dilakukan menggunakan Structural Equation Model Partial Least Square (PLS-SEM) untuk menguji hubungan antara variabel-variabel tersebut dan intention to adopt AI. Hasil penelitian menunjukkan bahwa faktor-faktor employee capability, top management support, financial resources, competitive pressure, dan vendor support berpengaruh terhadap perceived ease of use dan perceived usefulness dan mendorong UMKM untuk mengadopsi AI, yang pada gilirannya dapat meningkatkan kinerja dan daya saing mereka di pasar. Penelitian ini memberikan wawasan bagi pengembang AI dan UMKM tentang pentingnya faktor-faktor tersebut dalam strategi implementasi AI yang efektif dan berkelanjutan.

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This study aims to analyze the factors influencing the adoption of artificial intelligence (AI) in Micro, Small, and Medium-sized Enterprises (MSMEs) in Indonesia. Using the Technology Acceptance Model (TAM) and Technology Organization Environment (TOE) Framework, the methodology includes qualitative data collection through in-depth interviews with MSME owners/managers. This study employs a mixed-method approach with 11 online interview respondents and 342 questionnaire respondents, where both types of informants are MSME actors who understand general understanding of AI. The study identifies variables such as Employee Capability, Financial Resources, Top Management Support, Competitive Pressure, and Vendor Support contributing to perceived ease of use and perceived usefulness to determine whether these variables affect the intention to adopt AI. Data analysis is conducted using Partial Least Square (PLS-SEM) Structural Equation Model to test the relationship between these variables and the intention to adopt AI. The study's results show that factors such as employee capability, top management support, financial resources, competitive pressure, and vendor support influence perceived ease of use and perceived usefulness, encouraging MSMEs to adopt AI, which in turn can enhance their performance and competitiveness in the market. This study provides insights for AI developers and MSMEs about the importance of these factors in an effective and sustainable AI implementation strategy.