Use AI with accountability
Develop a common practice for checking AI-generated analysis before using it in engineering and improvement decisions.

Scale quality and productivity while preserving statistical rigor, data control, and professional accountability across technical and operational teams.
BUILD TEAM CAPABILITY
Develop a common practice for checking AI-generated analysis before using it in engineering and improvement decisions.
Practice identifying unsupported conclusions and explaining the evidence, uncertainty and limits.
Use the workplace toolkit on a suitable team problem. Review analysis effort, decision quality and resulting improvement actions.
Tell us your team’s responsibilities and current challenges. We’ll discuss course fit, scheduling, delivery options and corporate pricing.
Create a focused cohort for your organization.
Choose a delivery format that fits your team.
Align teams around a shared method and decision-validation approach.
Connect quality, operations, engineering, process excellence, and business teams.
TEAM CAPABILITY WITH ACCOUNTABILITY
AI6Sigma combines a structured 4-stage methodology and defined step-by-step workflows with ChatGPT and Microsoft Copilot acceleration, JMP statistical validation, unsupported-reasoning checks, and professional accountability. Private cohorts include manager and L&D approval support, with secure JMP/JSL automation available for appropriate teams.
Supported by senior instructional leadership spanning engineering, Lean Six Sigma, JMP analysis, technical operations, and professional training.
ENGINEERING TRAINING
Measurement, Capability & Process Control for Improvement
Improve measurement reliability, reduce defects and variation, and assess process performance with GenAI, independent JMP analysis, MSA, capability, hypothesis and proportion testing, and SPC.
Quality • Manufacturing • Process • Yield Engineers
Program Details →ENGINEERING TRAINING
Engineering Evidence for Product & Hardware Improvement
Compare alternatives, identify performance drivers and plan experiments with GenAI, independent JMP analysis, regression, and full and fractional factorial DOE.
Product • Hardware • Systems • Validation & Test Engineers
Program Details →ENGINEERING TRAINING
Evidence for Product Reliability Decisions
Evaluate life data, failure risk and test results with GenAI and independent JMP analysis. Explain the conclusion, uncertainty and engineering limits.
Product • Hardware • Reliability • Validation • Quality Engineers
Program Details →AI LEAN SIX SIGMA
Complete DMAIC Practitioner Training
Green Belt training combining DMAIC with AI-assisted JMP analysis, root cause investigation, statistical validation, project documentation, and management-ready reporting.
Quality • Operations • Engineering • Improvement Professionals
Program Details →AI LEAN SIX SIGMA
Advanced Analysis & Improvement Leadership
Advanced training for professionals leading complex process, quality, operational, engineering, and cross-functional improvement projects using Lean Six Sigma, JMP, decision validation, and responsible AI governance.
Experienced Practitioners • Managers • Improvement Leaders
Program Details →AI LEAN SIX SIGMA
Advanced Training for Green Belt Graduates
Build advanced analytical skills with GenAI, independent JMP validation and practical improvement cases. Day one covers JMP and the AI6Sigma workflow.
Prerequisite: completed AI6Sigma Green Belt or traditional Lean Six Sigma Green Belt training from another provider.
Program Details →ADVANCED JMP AUTOMATION
Advanced Engineering & Operations Workflow Automation
Uses a repeatable 11-step automation framework to build validated JMP/JSL automation engines for recurring analysis and process-improvement workflows.
For JMP-enabled teams that want to automate validated analytical and process-improvement workflows.
FROM TRAINING TO IMPLEMENTATION
AI6Sigma can extend beyond classroom training to help individuals and company teams apply the methods to real improvement work and turn validated analysis into sustainable operating results.
Instructor-guided application of DMAIC, JMP, statistical analysis, and AI-assisted investigation to real improvement projects.
Review assumptions, JMP results, AI-generated reasoning, root-cause conclusions, and supporting evidence before important decisions.
Help teams translate validated findings into improvement actions, control plans, monitoring, management review, and sustainment.
For JMP-enabled teams, extend appropriate repeatable analytical processes into controlled JMP/JSL automation with human review.
PROVEN TRAINING FOUNDATION
Professionals from leading organizations have participated in prior Global Lean Sigma training.






HOW YOU WILL WORK MORE PRODUCTIVELY AFTER AI6SIGMA TRAINING
Students get an AI answer, run the appropriate analysis independently in JMP, compare the evidence and assumptions, correct errors, and explain the decision.
Use GenAI to support investigation, interpretation and communication.
Evaluate statistical claims with independent JMP analysis and engineering judgment. Agreement alone does not establish validity.
Reduce unsupported reasoning and analytical rework with a repeatable workplace practice.
FROM CLASSROOM TO WORKPLACE
Use a condensed course PDF and a workplace application packet: a problem statement template, reusable prompts, a validation checklist, a decision summary template and course-specific practice materials.
Practice independently with course datasets and guidance. Additional project coaching and implementation support can be discussed separately.