The major topics that will be covered in this course include:

• Why one visible machine error collapses trust while identical human error does not, and what that asymmetry costs firms in both directions
• Where small and midsize firms actually stand on AI adoption, and why a firm that has not adopted broadly is not automatically behind
• Calibrated trust: the difference between misuse (overtrust), disuse (undertrust), and trust matched to what a tool actually does well
• The five-stop adoption spectrum: paralysis, accidental delay, informed caution, responsible piloting, and reckless adoption
• The AI Trust and Usage Quadrant: four firm postures and the next step each one implies
• Governed versus unstructured AI use, and why staff already bringing their own tools makes abstention a decision rather than an option
• What the evidence says about human and AI combinations, and the two task questions that predict whether a tool will help or hurt
• Reading vendor claims: the gap between a demo and production, and why fluent explanations increase acceptance of wrong answers
• Five trust questions to ask any AI vendor: permission, uncertainty, audit trail, explainability, and change control
• The NIST AI Risk Management Framework trustworthiness characteristics as a technical backbone
• Three vulnerabilities that current firm financial performance can conceal: client expectations, talent, and deferred learning
• Cognitive offloading and skill retention: what firms lose by waiting, and the conditions under which offloading frees capacity instead
• Five actions a firm can take this week, and four evidence-based rollout rules for a first pilot