Role-Based Programme
RB1589

AI-Powered Quality Inspection & Testing

Smarter Defect Detection, Test Analysis & Quality Reporting

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Understand how AI can support Quality Inspection and Testing across inspection planning, defect recording, test-result analysis, documentation, and reporting.
  • Apply AI-assisted techniques to organise inspection data, analyse test results, identify recurring defects, and highlight quality exceptions.
  • Use structured prompting for inspection checklists, defect classification, test summaries, deviation analysis, and management reporting.
  • Explore AI-supported approaches for identifying patterns, trends, repeat failures, and potential improvement areas from quality data.
  • Build responsible AI-assisted inspection and testing workflows while maintaining traceability, measurement accuracy, evidence integrity, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchDocument AISpreadsheet & Data AnalysisQuality Inspection AnalysisTest Result AnalysisVisual AIWorkflow Automation

Who should attend

  • Quality Inspection Executives
  • Quality Inspectors
  • Quality Testing Professionals
  • Quality Control Executives
  • Quality Control Engineers
  • Quality Analysts
  • Quality Assurance Professionals
  • Test Engineers
  • Inspection Engineers
  • Quality Supervisors
  • Process Quality Professionals
  • Production Quality Professionals
  • Quality Managers
  • Quality Inspection & Testing Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of Quality Inspection, Quality Control, or testing activities
  • Familiarity with inspection records, test results, specifications, defects, or quality checklists 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 inspection and testing
  • Identifying AI applications across inspection planning, defect analysis, test review, and reporting
  • Understanding AI assistance versus inspector, engineer, and quality-professional judgement
  • Recognising limitations such as incomplete evidence, inaccurate interpretation, and unsupported conclusions
Practical activities
  • Mapping a typical Quality Inspection & Testing workflow
  • Identifying repetitive and information-intensive activities suitable for AI assistance
  • Comparing a manual inspection-analysis task with an AI-assisted approach

Scenarios

Inspection Data to Defect & Exception Analysis

Inspection Plan → Inspection Results → AI-Assisted Defect Classification → Test Exceptions → Priority Issues → Quality Review Summary

Participants use AI to organise sample inspection and test data, identify recurring defects and exceptions, and prepare a structured quality-review summary.

Testing Results to Quality Improvement Insight

Test Data → AI Analysis → Failure Patterns → Out-of-Specification Results → Follow-Up Questions → Improvement Priorities → Management Report

Participants use AI to analyse sample testing data, identify repeated failure areas, and prepare a management-ready summary for further investigation and process improvement.

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