AI tools are rapidly becoming part of the IBM i conversation. Teams ask a question, get an answer in seconds, and the result often looks impressive - until an experienced team member spots a missing business rule, hidden dependency, or overlooked impact path. That's when validation loops, rework, and hidden AI costs begin.
In this session, we'll explore one of the biggest challenges facing IBM i teams today: context. Why do AI tools struggle with long-running applications? What information is typically missing? And how can teams provide the right context to improve answer quality, reduce blind spots, and avoid costly rework?
Using practical IBM i examples and live demonstrations, we'll show how trusted application context can help teams get more value from AI tools, improve confidence in AI-generated outputs, reduce hidden token costs, and preserve critical system knowledge for future teams.
Whether you're already experimenting with AI or just starting to explore the possibilities, this session will provide practical insights you can apply immediately.
