Role-Based Programme
RB1613

AI for Quality Management

Smarter Quality Analysis, Process Improvement & Compliance Monitoring

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Understand how AI can support Quality Management across quality planning, inspections, non-conformance analysis, CAPA, documentation, and reporting.
  • Apply AI-assisted techniques to analyse quality data, audit findings, process deviations, complaints, inspection records, and supporting documents.
  • Use structured prompting for quality issue analysis, root-cause questioning, SOP review, CAPA preparation, and management reporting.
  • Explore AI-supported approaches for identifying recurring defects, process variation, documentation gaps, and improvement opportunities.
  • Build responsible AI-assisted Quality Management workflows while maintaining accuracy, traceability, compliance, professional judgement, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchDocument AISpreadsheet & Data AnalysisQuality Data AnalysisRoot Cause Analysis AICAPA SupportWorkflow Automation

Who should attend

  • Quality Executives
  • Quality Managers
  • Quality Analysts
  • Quality Assurance Professionals
  • Quality Control Professionals
  • Process Excellence Professionals
  • Continuous Improvement Professionals
  • Quality Auditors
  • CAPA Coordinators
  • Operational Excellence Professionals
  • Compliance Professionals
  • Process Owners
  • Quality Systems Professionals
  • Quality Management Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of Quality Management, Quality Assurance, Quality Control, or process improvement
  • Familiarity with quality records, SOPs, audits, non-conformances, CAPA, or inspection 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 Management
  • Identifying AI applications across quality planning, inspection, issue analysis, documentation, and reporting
  • Understanding AI assistance versus quality-professional and management judgement
  • Recognising limitations such as hallucinations, incomplete data, bias, and unsupported conclusions
Practical activities
  • Mapping a typical Quality Management workflow
  • Identifying repetitive and information-intensive activities suitable for AI assistance
  • Comparing a traditional quality task with an AI-assisted approach

Scenarios

Quality Issue to Root Cause & CAPA

Quality Issue → AI-Assisted Data Review → Defect Pattern → Root-Cause Questions → Verified Cause → Corrective Action → CAPA Tracker

Participants use AI to analyse a sample quality issue, organise supporting evidence, develop root-cause questions, and prepare a structured CAPA plan for authorised review.

Quality Data to Management Improvement Plan

Defect Data + Complaints + Audit Findings + CAPA Status → AI Analysis → Recurring Themes → Priority Areas → Improvement Actions → Management Report

Participants use AI to analyse sample quality information, identify recurring issues and improvement opportunities, and prepare a management-ready Quality Improvement Plan.

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