AI's Errors May Be Impossible To Eliminate - What That Means For Its Use In The Workplace

This session gives workplace professionals at all levels a clear-eyed, accessible examination of why AI systems make errors, why those errors can be difficult to detect, and what that means for how AI should - and should not - be used in professional contexts. Rather than dismissing AI or uncritically adopting it, participants will leave with a calibrated understanding of AI's capabilities and limitations and a concrete framework for responsible, appropriately skeptical AI use in their own roles.

Why you should Attend: If your organization is adopting AI tools faster than it is building frameworks for managing their risks - this session is the responsible counterweight. If you've noticed AI producing outputs that were wrong but hard to catch, or if you're uncertain about when to trust AI and when to override it, this session gives you both the conceptual clarity and the practical tools to navigate that uncertainty with confidence.

Areas Covered in the Session:

• How AI errors happen: hallucinations, gaps in training data, and confident inaccuracies
• Why AI errors may not be fully solvable: a plain-language explanation for non-technical audiences
• Recognizing AI errors in practice: what they look like in real workplace outputs
• High-stakes vs. low-stakes AI use: calibrating oversight to context
• Building an effective human review habit: fast, practical, and sustainable
• Organizational responsibility: policies, accountability, and documentation
• A personal framework: when to trust AI, when to verify, and when to override

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