Define data governance - This may seem like an odd place to start, but without a data governance policy stating who has access to data and on permissible uses it can lead to organization dysfunction. Some will interpret the lack of a data policy to imply that they can do anything they want with the available data, others will be paralyzed and may prevent basic data from being used in analysis. Data governance teams should also be developing data catalogs, dictionaries, and reviews of data quality that are all important for using data in AI experiments. Establish your data lake - AI and ML algorithms work best when lots of raw data is applied to them. Centralizing primary data sources (including external ones) makes it easier for AI developers and data scientists to tap in and leverage them.