AI agents don’t fail because the models aren’t capable — they fail because they’re asked to operate without the trusted, governed, real-time context needed to make sound decisions. In the enterprise, AI is only as reliable as the data behind it, and that data is often fragmented across systems, unevenly governed, and difficult to interpret when it matters most.

As organizations move AI agents from experimentation into critical workflows, this gap between intelligence and context becomes the real barrier to scale. For data and AI leaders, the challenge is no longer just making data accessible — it’s making it trustworthy and contextualized for AI-driven action. This session will explore a practical framework for delivering context-rich, AI-ready data that enables agents to operate with greater accuracy, trust, and business relevance across the organization.

Speaker: Mark Brooks, Technical Specialist, IBM Data Platform, IBM

Mark Brooks is a technical specialist for IBM's Data Platform in the Americas. Mark has more than 25 years of experience in and around data, including the early days of Hadoop at Cloudera, several years of streaming data integration at StreamSets and Software AG, and now at IBM supporting watsonx.data intelligence and watsonx.dataintegration. Mark has worked as a Principal Solution Architect for data implementations within healthcare, insurance, and cybersecurity verticals.