AI Judgment for Leaders Micro-Credential

AI Judgment for Leaders Micro-Credential

Duration: 2 Hours

Description

The biggest AI skills gap in most organizations isn’t technical — it’s judgment: knowing when to trust an AI-generated answer, when to double-check it, and how to connect it to an actual business decision. This isn’t a how-to-prompt session — see Generative AI Essentials for Business for that. It’s built for leaders and managers who don’t write code and don’t build AI systems themselves, and covers how to read AI output critically, spot likely hallucinations, understand what governance and risk oversight requires of them personally, and set realistic expectations for AI initiatives on their teams.

Audience

This session is for people managers, directors, and executives who oversee teams using AI tools or are sponsoring AI initiatives, but who don’t build or configure AI systems themselves. No technical background is assumed.

Objectives

  • Evaluate whether an AI-generated output is likely accurate or misleading
  • Ask the right questions before approving an AI-driven decision or initiative
  • Understand baseline governance and risk-oversight responsibilities for leaders
  • Set realistic expectations and success criteria for AI adoption on a team

Prerequisites

No prior AI or technical experience is required.

Related AI Courses

See the full AI training roadmap and course directory for how this fits into a broader learning path.

For hands-on prompting and tool fluency, see Generative AI Essentials for Business. For the full governance and risk framework this session previews, see AI Governance & Risk or the shorter AI Governance & Risk Management Micro-Credential. Curious about the fairness and accountability side of AI? See AI Ethics and Bias Micro-Credential.

Course Outline

Module 1: What Leaders Actually Need to Know

  • AI Literacy Without the Jargon
  • What These Tools Are Actually Good and Bad At
  • Lab – Spot the Hallucination in a Set of AI Outputs

Module 2: Judgment and Decision-Making

  • Reading AI Output Critically
  • Connecting AI Outputs to Business Decisions
  • Questions to Ask Before Approving an AI Initiative
  • Discussion – Evaluate a Real (Anonymized) AI Use Case

Module 3: Leading AI Adoption Responsibly

  • Your Role in Governance and Risk Oversight
  • Setting Realistic Expectations and Success Metrics
  • Supporting Your Team Through the Transition
  • Lab – Draft an AI Adoption Checklist for Your Team