Enterprise generative AI investments often stall because adoption metrics are mistaken for business impact. Token spend, seat counts and pilot activity may show usage, but they do not prove value if AI cannot be reliably connected to correctness-critical workflows.
This session examines why many AI initiatives fail to produce measurable returns and argues that the underlying issue is often semantic, not model-related. The session will show how ambiguous retrieval, inconsistent definitions and weak integration patterns can prevent AI from being trusted in operational settings. It will also explain where formalized semantics can make a material difference by clarifying meaning, improving retrieval quality, supporting governance and creating a more reliable foundation for automation.
Attendees will leave with a practical field test for deciding when semantic infrastructure earns its cost and when a lighter approach is sufficient. The session is designed to help leaders avoid overengineering while recognizing the moments when semantics becomes essential to accuracy, trust and business value. Participants will gain a clearer framework for connecting AI investments to outcomes rather than activity.
