Before AI can drive real impact in customer support, your data needs to be ready for it. From chatbot assistants to predictive routing and self-serve tools, every AI application relies on clean, connected, and context-rich data to perform at its best. This session unpacks what “AI-ready” data actually looks like—and how to get there without over-engineering or slowing down innovation. We’ll cover: • Data audits that matter – How to assess your current support data for gaps, inconsistencies, and silos. • Structuring for scale – Best practices for tagging, labeling, and mapping support interactions for future AI use cases. • Integrations and hygiene – How to align systems and maintain high-quality data without constant manual clean-up. • Measuring readiness – Key indicators that signal when your data is mature enough for AI deployment. You’ll leave with a practical checklist for preparing your data stack—so your AI tools can deliver on their promise, without the guesswork.