Role-Based Programme
RB1590

AI-Powered Quality Inspection & Testing

Smarter Defect Detection, Test Analysis & Quality Assurance

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Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Apply AI across Quality Inspection and Testing activities including inspection planning, test execution support, defect analysis, specification review, and reporting.
  • Use AI-assisted techniques to analyse inspection records, test results, defect information, measurement data, and acceptance criteria more efficiently.
  • Develop structured workflows for inspection preparation, sample selection, evidence capture, exception handling, reinspection, and final disposition.
  • Analyse inspection and testing data to identify recurring defects, failure patterns, process variation, and quality-control weaknesses.
  • Apply responsible AI practices covering measurement integrity, evidence traceability, specification accuracy, data validation, and human oversight.

Tools covered

Generative AI AssistantsInspection AnalyticsTest Data AnalysisDocument IntelligenceSpreadsheet AnalysisDefect ClassificationSampling SupportSpecification ComparisonQuality ReportingWorkflow Automation

Who should attend

  • Quality Inspection Professionals
  • Quality Testing Professionals
  • Quality Control Engineers
  • Quality Assurance Professionals
  • Quality Engineers
  • Inspection Engineers
  • Test Engineers
  • Quality Technicians
  • Incoming Quality Professionals
  • In-Process Quality Professionals
  • Final Inspection Professionals
  • Process Quality Professionals
  • Quality Analysts
  • Quality Management Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of quality inspection, testing, Quality Control, or production/service quality activities
  • Familiarity with specifications, inspection criteria, test records, defects, or measurement data is helpful
  • Basic spreadsheet and data-analysis skills
  • Basic awareness of Generative AI is helpful
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, analytics, document intelligence, and automation in inspection and testing
  • Identifying AI opportunities across inspection planning, defect analysis, testing, and reporting
  • Understanding AI assistance versus inspector, engineer, and Quality professional judgement
  • Recognising risks related to incorrect specifications, poor measurement data, and unsupported conclusions
Practical activities
  • Mapping the Quality Inspection and Testing lifecycle to AI-assisted activities
  • Identifying repetitive inspection and testing tasks suitable for AI support
  • Comparing traditional and AI-assisted quality-control workflows

Scenarios

Failed Inspection to Reinspection Decision

Inspection Plan → AI-Assisted Checklist → Test Results → Defect Classification → Non-Conformance → Root-Cause Review → Corrective Action → Reinspection → Final Disposition

Participants analyse a simulated failed inspection, review test evidence, classify defects, structure corrective actions, and determine the information required for controlled reinspection and authorised final disposition.

Inspection Data to Quality Control Improvement Plan

Inspection Records + Test Results + Defect Data + Rejection History → AI Analysis → Failure Trends → Priority Defects → Process Actions → Monitoring Plan → Management Report

Participants consolidate inspection and testing information, identify recurring failure patterns and quality-control weaknesses, and prepare a management-ready improvement plan with actions, owners, and monitoring priorities.

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