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Course Overview
AI can accelerate your work—but speed alone does not guarantee better results. The real advantage comes from knowing where AI adds value, how to ask better questions, and when human judgment needs to take the lead.
This highly practical, full-day workshop brings critical thinking and AI-enabled workflow improvement together. Participants learn how to evaluate their current ways of working, identify appropriate opportunities for AI, create more effective prompts, critically evaluate AI-generated results, and redesign workflows with purposeful human review built in.
This course will earn you 7 PDUs
Who Should Attend
This workshop is designed for mid-level leaders, team leads, and knowledge workers who perform operational work themselves and/or influence how their teams use AI.
It is particularly valuable for professionals who want to move beyond experimenting with AI and develop a more disciplined approach to deciding where to use it, how to use it, and how to evaluate the results.
Course Objectives
- Apply critical thinking to AI-assisted work using a structured approach to analysis, questioning, evaluation, and decision-making.
- Identify opportunities for AI within existing workflows by mapping real processes, recognizing friction points, and determining where AI capabilities can add meaningful value.
- Match AI capabilities to business needs by defining the question or problem before selecting how AI should be used.
- Create more effective AI prompts using a five-part structure—Role, Task, Context, Format, and Constraints—grounded in disciplined inquiry rather than trial and error.
- Evaluate AI-generated output critically for accuracy, relevance, completeness, logic, and risk before putting it to use.
- Recognize human and AI-enabled bias, assumptions, and reasoning errors that can affect the quality of AI-supported decisions and work products.
- Apply the LAB framework—Limitations, Assumptions, and Bias to establish meaningful human review checkpoints in AI-enabled workflows.
- Use the COACHES characteristics of effective critical thinkers, with particular emphasis on Curiosity, Humility, and Objectivity, to support responsible AI use.
- Redesign a real workflow with AI intentionally integrated, including clear human review points and measures of success.
- Identify specific behaviors for improving personal and team AI use after the workshop.
Course Outline
Course Introduction
- Course overview
- Learning objectives review
Modern Critical Thinking in the Age of AI
- Thinking, critical thinking, and AI-assisted thinking
- The opportunities and limitations of AI
- Why AI fluency should not be confused with AI accuracy
- Open-mindedness as a workflow skill
Understanding What AI Can (and Cannot) Do
- Explore five practical AI capabilities commonly used in knowledge work
- Exercise: Map your workflow with a critical thinking lens
- Debrief: Identifying Patterns
Utilizing Analysis and Logic with AI as the Resource
- Defining the problem before asking AI to solve it
- Applying analysis and logic to AI-assisted work
- Distinguishing a well-formed question from a poorly formed one
- Identifying assumptions hidden inside prompts
- Using follow-up questions to improve both AI output and human reasoning
Match the AI Capability and Define the Question
- Determine how friction points help determine AI appropriateness
- Exercise: Find where AI fits
- Debrief: What information would AI need?
COACHES for Responsible AI Use
- COACHES model for critical thinking and responsible AI use
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- Curiosity
- Objectivity
- Active listening
- Contextualization
- Humility
- Emotional control
- Self-awareness
Prompt Structure: Turning Questions into Effective Prompts
- How prompts actually work
- Prompt structure in practice
- Exercise: Write and test a real prompt
- Debrief: Determining how to create valuable prompts
LAB: Limitations, Assumptions, and Bias
- Human review is essential to responsible AI use
- Understanding the LAB framework
- AI fallacies in action
Redesign Your Workflow and Apply LAB
- Exercise: Redesigning the original workflow using the LAB framework
- Debrief: Share lessons learned - before, after, and what changed
From Smarter Workflows to Better AI Leadership
- Utilizing the evaluative processes
- Choosing a COACHES characteristic to strengthen
Course Closeout