AI and Process Improvement

Traditional Classroom: ½-day
Virtual Instructor-led: One 3-hour session

Artificial intelligence is rapidly reshaping how organizations operate — and process improvement professionals are at the forefront of that transformation. Yet many leaders, managers, and practitioners still lack a clear, practical understanding of what AI is, what it can and cannot do, and how it meaningfully connects to the improvement work happening across their organizations every day.

This course builds that foundation and immediately puts it into practice. Participants begin by cutting through the hype to develop a shared understanding of core AI concepts, including the AI capabilities most relevant to process improvement, the opportunities AI creates, and the risks and biases professionals must recognize before relying on AI-generated outputs.

The course then shifts from understanding to application, exploring how AI tools can support and accelerate each phase of DMAIC — from synthesizing Voice of the Customer data in Define, to identifying patterns and root causes in Analyze, to monitoring performance and sustaining gains in Control.

Practical, accessible, and designed for mixed audiences, this course equips leaders, managers, and practitioners with the foundational AI literacy and DMAIC application awareness needed to begin using AI more effectively, responsibly, and confidently in process improvement work immediately.

Bring This Program to Your Organization

This course is typically delivered as a private cohort, customized to your organization’s AI maturity, process improvement methodology, and team composition. Contact us to discuss tailoring this program for your team.

Discuss This Program

Target Audience

This course is ideal for:

Leaders, managers, and individual contributors involved in process improvement, operational excellence, or continuous improvement initiatives
Process improvement professionals seeking practical, business-focused AI fluency
Teams preparing to incorporate AI tools into Lean Six Sigma, DMAIC, or other improvement methodologies
Organizations looking to build foundational AI literacy across mixed-function audiences
No prior AI knowledge, technical expertise, or programming background required

Learning Objectives

Distinguish between key AI types — including Generative AI, Machine Learning, Predictive Analytics, and Robotic Process Automation (RPA) — and describe their relevance to process improvement work.
Identify common AI biases and limitations, including prompt order bias, and explain why critical evaluation of AI-generated outputs is essential.
Describe the key opportunities and risks associated with using AI in process improvement environments, including data quality concerns, overreliance, bias, and human accountability.
Explain how AI tools can support and accelerate each phase of DMAIC — from Voice of the Customer synthesis in Define to automated monitoring and sustainability in Control.
Construct effective AI prompts that generate useful, actionable outputs for process analysis, root cause exploration, and improvement activities.
Critically evaluate AI-generated insights, distinguishing between outputs that are reliable and those requiring further validation or human review.
Identify practical opportunities to apply AI tools within current or upcoming process improvement projects and workflows.

Course Outline

Opening: Setting the Stage

Course Introduction and Objectives
Icebreaker: One Word That Describes AI in Your Organization Right Now
Framing: Why This Moment Matters for Process Professionals

Part 1: The AI Landscape

What AI Is — and What It Isn’t: Separating Hype from Reality
Key AI Types for Process Professionals: Generative AI, Machine Learning, Predictive Analytics, Robotic Process Automation (RPA)
Plain-Language Definitions with Real-World Process Improvement Examples
The Natural Intersection: Data, Patterns, Decisions — Where AI and Process Improvement Already Meet

Part 2: Opportunities, Risks, and the Human Role

Three Categories of Opportunity: Speed, Scale, and Insight
Three Categories of Risk: Bias, Data Quality, and Dependency
AI Bias Deep Dive: What It Is, Where It Comes From, and Why It Matters for Practitioners
Prompt Order Bias: How the Sequence of Options in a Prompt Influences AI Recommendations — and What to Do About It
What AI Cannot Do: Judgment, Ethics, Stakeholder Trust, Accountability, and Context
The Co-pilot Mindset: AI Accelerates Your Work — You Remain in Command

Part 3: AI Readiness Discussion

Quick Poll: Where Is Your Organization Today on AI Readiness?
Five Dimensions: Leadership Commitment, Data Quality, Workforce Skills, Process Maturity, Governance
Paired Discussion: What Is Your Biggest AI Opportunity? Your Biggest Risk?

DMAIC Application Overview

The Co-pilot Framing: AI Accelerates DMAIC — It Does Not Replace It
DEFINE — AI for VOC Synthesis, Customer Feedback Theming, CTQ Translation, and Problem Statement Refinement
MEASURE — AI-Assisted Data Collection Design, Pattern Recognition, and Baseline Performance Analysis
ANALYZE — Root Cause Acceleration, AI-Generated Hypothesis Sets, and Predictive Analytics
IMPROVE — Solution Ideation at Scale, Scenario Modeling, and AI-Assisted Prioritization
CONTROL — Automated Monitoring, Anomaly Detection, and Control Plan Intelligence
Prompt Engineering Basics: The Anatomy of an Effective Process Improvement Prompt — Context, Role, Task, Constraints, Format
The Three Verification Questions: Is It Accurate? Is It Complete? Is It Appropriate?
Data Privacy Guardrail: What Should Never Go Into a Public AI Tool

Course Summary and Next Steps

For full program details, contact info@corpedgroup.com or request information online.

MDV188-3 Course Code

Practice Area

Artificial Intelligence