This six-week training series introduces practical, foundational applications of artificial intelligence across the CMQ/OE Body of Knowledge. Designed for quality leaders and individual contributors, the course progresses from basic prompting to structured analytical support, integrating AI into leadership, strategy deployment, problem solving, measurement interpretation, customer analysis, supplier management, and training systems. Each session features a real-world use case and guided breakout discussion to reinforce learning. Emphasis remains on validation, governance, and ethical use in regulated environments. Participants leave with repeatable prompts, a personal AI playbook, and disciplined practices that enhance judgment rather than replace it.
What this course is
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A structured, six-week introduction to practical AI applications across the CMQ/OE domains
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A disciplined approach to using AI as a thinking partner in quality leadership and operations
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A hands-on learning experience built around real-world quality scenarios
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A progressive skill-building series that starts basic and builds gradually
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A forum for thoughtful discussion about validation, ethics, and governance
What this course is not
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Not a coding or programming course
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Not a replacement for statistical rigor or professional judgment
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Not automation implementation training
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Not a technology deep dive into algorithms or data science
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Not a promise of instant efficiency without verification discipline
Session Overviews
Week 1 – AI Foundations for Quality Leaders (Apr 6)
An introduction to what AI is, how it works at a practical level, and how to use simple prompts responsibly. Participants apply AI to a change management and communication scenario tied to quality system updates.
Week 2 – Structured Thinking and Project Alignment (Apr 13)
Participants use AI to clarify roles, cross-functional dependencies, and project risk. The session focuses on improving prompt quality and translating ambiguity into structured plans.
Week 3 – Measurement, Metrics, and Translation (Apr 20)
This session focuses on interpreting performance data and translating SPC and capability concepts for different audiences. AI is used to draft investigation questions and executive-ready summaries.
Week 4 – AI-Assisted Problem Solving (Apr 27)
Using a recurring customer complaint scenario, participants explore how AI can support 5 Whys, fishbone analysis, and structured CAPA drafting while reinforcing the need for human validation.
Week 5 – Supplier Risk and Training Systems (May 4)
Participants examine supplier performance trends and internal capability gaps using AI-supported analysis. The session explores how AI can help structure supplier evaluations, training needs analysis, and risk communication.
Week 6 – Customer Insight and Voice of the Customer (May 11)
The final session focuses on analyzing qualitative customer feedback and identifying systemic patterns. Participants use AI to categorize VOC data and translate insights into improvement priorities while managing bias and interpretation risks.
Instructors
Steve Kuhlman (CFSQA, CMQ/OE, CQE), Cincinnati Section Secretary, is Global Quality Assurance Systems Senior Manager at Procter & Gamble. Steve has taken the lead within his organization to build AI skills that apply directly to the business. He has led multiple process validation initiatives across North America, Latin America, and Europe, integrating cGMP compliance, 21 CFR Parts 210/211 training, supplier quality audits, and FDA readiness, achieving zero 483 observations in multiple inspections. Steve’s expertise spans method development to USP <233> standards, CAPA execution, and statistical process analysis, supported by an MBA from Wayne State University and BS in Chemistry from University of North Texas.
William Harvey (CMQ/OE, CQE), ASQ Cincinnati Section Past Chair, is Strategic Initiatives Program Manager at Michelman. William is an annual adjunct Assistant Professor at the University of Cincinnati. As one of the top 1% global ChatGPT users, William has done the difficult work to learn what AI is good at, not good at yet, and how you can practically apply this to your work to solve real problems faster, sharpen thinking, and elevate decision quality without outsourcing judgment.