Generative artificial intelligence (AI) astonishes with human-like capabilities that set it apart from earlier information technologies. While the first instruments for measuring declarative AI literacy are now being developed, we propose a prior step: a model and instrument for diagnosing readiness to use AI – one that encompasses proficiency, attitudes toward the technology, and the situational context of its use. We focus on academic research activities, where the technical and creative potential of AI tools can markedly influence performance. Methodologically, the study relies on factor analysis. First, drawing on the literature, we created and validated an AI-Readiness Scale with a sample of faculty members from multiple disciplines. Second, we used a multiple-indicators-multiple-causes model to identify situational, institutional, and personal factors associated with AI readiness. Indicators were inspired by the technology-acceptance model, AI-literacy scales, and information-literacy scales, allowing assessment of four facets of readiness: cognitive, behavioral, affective, and ethical. These findings are preliminary and require replication with a larger, probability-based sample before firm conclusions can be drawn. For now, we observe that accessibility of AI tools and perceptions of usefulness appear insufficient to motivate application in research, while stress linked to job demands and digital transformation has the opposite effect. Readiness is instead fostered by perceived institutional or peer pressure regarding academic performance. Academics view AI tools most favorably in areas where supporting software is already widely accepted and transparent, notably for analytical and editorial tasks. These insights also guide university strategies: beyond providing access and training, institutions should address organizational culture and clearly communicate expectations surrounding new technologies.