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What happens to data enriched by crowdsourcing, machine learning/AI, or a combination of methods? There's a long history of crowdsourcing projects at cultural heritage institutions (GLAMS, or galleries, libraries, archives and museums; including citizen science and citizen history projects). More recently, GLAMs have experimented with machine learning or 'AI' to create, enrich or enhance data about collections. However, some projects struggle to integrate the data created or enriched by online volunteers and/or machine learning into collections management and discovery systems (catalogues, for short). We're seeking to understand the barriers and successes for projects incorporating enriched data into catalogues or other core systems by gathering information on the types of data, tools and processes used by project teams. We hope these results will help organisations, software suppliers and projects with the work of integrating enriched data appropriately into collections systems. Your insights and experiences are invaluable to this study, and we would be grateful if you could spare 10-15 minutes of your time to share your experiences.