Foundations: How Intelligent Systems Actually Work

Purpose

Give non-ML teams a grounded, operational understanding of how modern intelligent systems behave under real-world constraints. This is not AI literacy. It is decision literacy for AI-enabled systems.

Session focus

• How intelligent systems reason, fail, and recover
• Why confidence, uncertainty, and escalation matter more than accuracy
• Where humans must remain in control — and why that boundary shifts over time

Working blocks

• What actually makes a system “intelligent”: Models vs systems, and why notebooks don’t translate to outcomes.
• Failure modes you don’t see in demos: Drift, hallucination, and silent degradation surface operationally.
• Human-in-the-loop as a design constraint: Decision ownership, escalation, and override patterns.
• From experimentation to commitment: What changes when a system is expected to hold a promise.

Teams leave with

• A shared mental model for AI system behavior
• Clear boundaries for safe use
• Language to make better decisions immediately