06 testing trust and scaling

Testing, Trust and Scaling explains how managers can evaluate AI agents, establish trust in their performance, and expand their use responsibly. It emphasizes that a successful demonstration is insufficient for deployment, as AI agents must be tested under normal conditions, unusual situations, and potential failures. Evaluation should consider factual accuracy, task completion, compliance with permissions, error handling, and the quality of business outcomes. The chapter introduces the Progressive Trust Model, which moves from high human oversight to selective review and eventually strategic oversight, with greater autonomy granted only when supported by demonstrated performance. It also highlights the importance of continuous testing, user feedback, performance monitoring, and identifying the actual causes of errors before making improvements. A balanced scorecard can track completion rates, errors, escalations, processing time, user experience, and operating costs. Using HR policy assistance as an example, the chapter demonstrates why successful pilot results do not automatically justify large-scale deployment, particularly when increased demand places pressure on human reviewers, technical systems, and budgets. Ultimately, responsible scaling requires verified performance, clear approval boundaries, sufficient operational capacity, and continuous human accountability, with managers prepared to expand, redesign, or suspend AI services based on evidence.