Role-Based Programme
RB1614

AI for Quality Management

Smarter Quality Control, Process Improvement & Compliance Assurance

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

Programme Objectives

  • Apply AI across Quality Management activities including quality planning, inspection, process analysis, non-conformance management, CAPA, audits, and reporting.
  • Use AI-assisted techniques to analyse quality records, inspection data, complaints, deviations, audit findings, and process information more efficiently.
  • Develop structured workflows for issue identification, root-cause analysis, corrective and preventive actions, quality monitoring, and continuous improvement.
  • Analyse quality-performance data to identify recurring defects, process variation, control weaknesses, and improvement opportunities.
  • Apply responsible AI practices covering data quality, traceability, compliance, evidence validation, operational risk, and human oversight.

Tools covered

Generative AI AssistantsQuality AnalyticsDocument IntelligenceSpreadsheet AnalysisRoot Cause Analysis SupportCAPA ManagementAudit SupportProcess AnalysisQuality ReportingWorkflow Automation

Who should attend

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

Prerequisites & Participant Readiness

  • Working knowledge of Quality Management, Quality Assurance, Quality Control, or process-improvement activities
  • Familiarity with inspections, audits, deviations, CAPA, quality records, or process 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, automation, and their role in Quality Management
  • Identifying AI opportunities across quality planning, inspection, audits, CAPA, and reporting
  • Understanding AI assistance versus Quality professional judgement and accountability
  • Recognising risks related to incorrect data, unsupported conclusions, and compliance requirements
Practical activities
  • Mapping the Quality Management lifecycle to AI-assisted activities
  • Identifying repetitive quality tasks suitable for AI support
  • Comparing traditional and AI-assisted quality workflows

Scenarios

Quality Defect to CAPA Closure

Quality Defect → AI-Assisted Classification → Root Cause Analysis → Corrective Action → Preventive Action → Effectiveness Check → Closure

Participants analyse a simulated quality defect, identify potential causes, develop corrective and preventive actions, and create a structured CAPA workflow through effectiveness verification and closure.

Quality Data to Continuous Improvement Plan

Inspection Data + Defects + Complaints + Audit Findings → AI Analysis → Trends & Root Causes → Priority Issues → Improvement Actions → Management Report

Participants consolidate multiple quality information sources, identify recurring issues and process weaknesses, and prepare a management-ready Quality Improvement Plan with actions, owners, and priorities.

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