Section 163 · Front Matter, Executive Brief
Executive Summary: Why Testing AI Is Different
AI makes generation cheap, but trust still has to be earned with evidence.
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What to do
- Define runnable checks that exercise variance, latency, and executive summary why different.
- Set acceptable outcomes and blocker failures for variance, latency, and executive summary why different before running the evaluation.
- Run representative cases for variance, latency, and executive summary why different and preserve the failures that would change the decision.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for variance, latency, executive summary why different needed to reproduce work on Executive Summary: Why Testing AI Is Different.
- Report results for variance, latency, executive summary why different by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
In production work, AI quality becomes a portfolio discipline: invest validation effort where uncertainty, user impact, business value, and downside risk are highest.
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Cite this page
Jason Arbon. "Executive Summary: Why Testing AI Is Different." Testing AI Knowledge Edition, section 163.
https://jarbon.ai/testing-ai/knowledge/ch163-executive-summary-why-different.html