Role-Based Programme
RB1580

AI-Powered Quality Control

Smarter Inspection, Defect Analysis & Quality Monitoring

IMAGE REQUIRED
Duration
4 Hours
Level
Awareness
Delivery
Instructor-Led
Format
Awareness Session

Programme Objectives

  • Understand how AI can support Quality Control across inspection, defect analysis, test-result review, documentation, and reporting.
  • Explore practical prompting techniques for quality checks, defect summaries, deviation analysis, inspection records, and corrective-action support.
  • Apply AI to organise quality data, identify recurring patterns, classify defects, and prepare structured Quality Control outputs.
  • Identify opportunities to improve inspection consistency, issue visibility, documentation quality, and quality-monitoring efficiency.
  • Recognise specification accuracy, traceability, measurement requirements, approvals, and human-review responsibilities when using AI.

Tools covered

Generative AI AssistantsAI-Assisted Quality AnalysisDefect ClassificationSpreadsheet AnalysisInspection Data ReviewRoot Cause SupportQuality ReportingBasic Workflow Automation

Who should attend

  • Quality Control Executives
  • Quality Control Engineers
  • Quality Inspectors
  • Quality Analysts
  • Quality Assurance Professionals
  • Quality Engineers
  • Inspection Professionals
  • Testing Professionals
  • Process Quality Professionals
  • Product Quality Professionals
  • Manufacturing Quality Professionals
  • Quality Supervisors
  • Quality Management Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of quality control, inspection, or testing activities
  • Familiarity with specifications, defects, inspection records, or quality measurements is helpful
  • Basic computer and spreadsheet skills
  • No AI or programming knowledge required
  • No previous Generative AI experience required

TOC Modules

Concepts
  • Understanding Generative AI and its relevance to Quality Control
  • Identifying AI applications across inspection, testing, defect review, documentation, and reporting
  • Understanding AI assistance versus Quality Control professional judgement
  • Recognising activities where approved specifications, measurements, and physical verification remain mandatory
Practical activities
  • Mapping common Quality Control activities to potential AI applications
  • Comparing a traditional QC task with an AI-assisted approach

Scenarios

Inspection Results to Non-Conformance Review

Inspection Data → AI-Assisted Review → Specification Comparison → Defects → Severity → Non-Conformance Summary

Participants use AI to organise sample inspection information, identify deviations from supplied acceptance criteria, and prepare a structured non-conformance summary for Quality review.

Defect Data to Corrective Action

Defect Records → AI Analysis → Recurring Patterns → Potential Causes → Corrective Actions → QC Management Summary

Participants use AI to analyse sample defect records, identify recurring quality concerns, explore potential contributing factors, and prepare a concise corrective-action and management summary.

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