The adoption of Artificial Intelligence (AI) is reshaping the agri-tech ecosystem by improving operational efficiency, sustainability, and data-driven decision-making. This study presents an integrated Criteria Importance Through Intercriteria Correlation (CRITIC) and q-Rung Orthopair Fuzzy (q-ROF) framework to evaluate the key factors influencing AI adoption. A comprehensive literature review was conducted to identify potential determinants, which were subsequently refined into seven critical factors through expert consultation. To address uncertainty and subjectivity in expert judgments, q-rung orthopair fuzzy sets were employed, while the CRITIC method was applied to derive objective weights by considering both contrast intensity and inter-criteria conflict. The findings reveal that farmer and customer outcomes (0.1988) is the most influential factor, followed by operational efficiency (0.1531) and financial performance (0.1464). Factors such as innovation capability, decision quality, and accuracy demonstrate moderate influence, whereas data
integration, interoperability, market reach, and scalability exhibit comparatively lower impact. The results suggest that financial performance and operational efficiency are primary drivers of AI adoption in agriculture, while other factors play a complementary role in enhancing overall decision effectiveness. The proposed framework offers a systematic, objective, and robust decision support tool to assist stakeholders in formulating informed strategies and facilitating effective AI implementation within the agri-tech ecosystem.