Enterprise AI success requires more than data access—it depends on trusted, governed, AI-ready context across both structured and unstructured data, supported by an architecture that can optimize cost and performance by workload. For many organizations, existing data governance programs were built for human-scale consumption—not for the speed, scale, and automation demands of modern analytics and generative AI. As AI adoption accelerates, organizations need a new approach that embeds governance, lineage, quality, and policy enforcement directly into how data is discovered, accessed, and used.

Speaker: Robert Jackson-Jones, Product Manager, Data Intelligence, IBM

Rob Jackson-Jones is a Product Manager, Data Intelligence at IBM covering Data Governance & Privacy. Rob has been with IBM for over 25 years, working in many different roles across the data arena. From designing and building large data warehouses and the first e-commerce sites, to complex Data architecture work across Finance, Retail, and Telco sectors. Rob led IBM's UK Data Governance Services team for a number of years, then spent time working as a Software Client Account Manager, and now enjoys his current role - always with a strong focus on Data.