In high-velocity domains like AdTech, AI systems make thousands of decisions per second but full automation isn't always the right answer. This session introduces a practical decision framework for AI Product Managers to determine when to automate fully, when to augment human judgment and how to design escalation paths that scale.

I'll break down the core heuristics: assessing error costs (reversible vs. irreversible), defining risk thresholds (brand safety, financial exposure), and mapping escalation triggers (anomaly detection, confidence decay). Using real-world examples from campaign optimization and bidding systems, I'll show how to embed these guardrails without sacrificing velocity.

You'll leave with a reusable checklist: a decision tree for automation design, metrics to monitor human-AI handoffs, and patterns for testing escalation logic. While examples draw from AdTech, the framework applies to any domain where AI decisions carry business, ethical, or regulatory weight, fintech, healthtech, logistics, and beyond.

Takeaways: 1) A 4-factor heuristic for automation vs. augmentation decisions 2) Patterns for designing scalable human-in-the-loop escalation 3) Metrics to track system health beyond model accuracy 4) A template for documenting automation boundaries in PRDs