Role-Based Programme
RB1581

AI-Powered Quality Control

Smarter Inspection, Defect Analysis & Quality Performance Monitoring

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Understand how AI can support Quality Control across inspection, testing, defect analysis, deviation review, and reporting.
  • Apply AI-assisted techniques to analyse quality records, inspection results, test data, defects, rejects, and process deviations.
  • Use structured prompting for QC checklists, defect classification, exception analysis, root-cause questioning, and corrective-action support.
  • Explore AI-supported approaches for identifying recurring defects, process variation, rejection trends, and quality improvement opportunities.
  • Build responsible AI-assisted Quality Control workflows while maintaining measurement accuracy, traceability, evidence integrity, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchDocument AISpreadsheet & Data AnalysisQuality Control AnalysisDefect Analysis AIRoot-Cause AnalysisVisual AIWorkflow Automation

Who should attend

  • Quality Control Executives
  • Quality Control Engineers
  • Quality Inspectors
  • QC Analysts
  • Quality Supervisors
  • Quality Assurance Professionals
  • Process Quality Professionals
  • Production Quality Professionals
  • Inspection Engineers
  • Testing Professionals
  • Quality Technicians
  • CAPA Coordinators
  • Quality Managers
  • Quality Control Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of Quality Control, inspection, or testing activities
  • Familiarity with specifications, defects, test results, quality records, or rejection data is helpful
  • Basic computer, spreadsheet, and document-handling skills
  • No AI or programming knowledge required
  • No previous AI training required

TOC Modules

Concepts
  • Understanding Generative AI and its relevance to Quality Control activities
  • Identifying AI applications across inspection, testing, defect analysis, documentation, and reporting
  • Understanding AI assistance versus QC, engineering, and process-owner judgement
  • Recognising limitations such as incomplete evidence, incorrect interpretation, and unsupported conclusions
Practical activities
  • Mapping a typical Quality Control workflow
  • Identifying repetitive and information-intensive QC activities suitable for AI assistance
  • Comparing a manual Quality Control task with an AI-assisted approach

Scenarios

Quality Inspection to Defect & Corrective Action

Inspection Results → AI-Assisted Defect Classification → Rejection Analysis → Root-Cause Questions → Corrective Action → Verification

Participants use AI to analyse sample Quality Control results, identify recurring defect patterns, develop investigation questions, and prepare a corrective-action workflow for authorised review.

QC Data to Quality Performance Improvement

Test Results + Defect Data + Rejection Data + Rework → AI Analysis → Quality Trends → Priority Issues → Improvement Actions → Management Report

Participants use AI to analyse sample QC performance data, identify recurring quality concerns, and prepare a management-ready Quality Control Improvement Plan.

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