The complexity of biological imaging data has surpassed the analytic capacity of the individual researcher. State-of-the-art Artificial Intelligence (AI) methods allow automated microscopy image analysis, segmentation and interpretation, thereby reducing these complexities to manageable datasets. For example, 2D/3D image segmentation, resolution enhancement, analysis of high-content multispectral data, and label-free phenotypic profiling. In addition, these methods allows for standardization and automation of analysis pipelines used in disease model screens. The advanced technology platform for AI in Imaging and Automation (AIMA) serves as a centre of expertise for AI usage in these challenges, resting on four pillars: R&D (1), creating new state-of-the-art AI techniques in microscopy and automation. Applied (2), supporting researchers in the integration of AI in cellular disease models and automation in collaborative applied projects. Best practices (3), providing documentation and standards for coding, model tracing, tool usage, FAIR data and software to the community. Education (4), training the new generation of AI researchers in cellular imaging. AIMA is embedded in the Centre for Living Technologies, thereby introducing expertise and a supportive community on advanced quantitative imaging and analysis.