Role-Based Programme
RB1612

AI for Quality Management

Smarter Quality Analysis, Process Control & Continuous Improvement

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

Programme Objectives

  • Understand how AI can support Quality Management across inspections, quality analysis, documentation, root-cause review, CAPA, and reporting.
  • Explore practical prompting techniques for quality issues, process deviations, corrective actions, audit observations, and management summaries.
  • Apply AI to organise quality information, identify patterns, structure findings, and prepare quality-management outputs.
  • Identify opportunities to improve documentation quality, issue visibility, analysis efficiency, and continuous-improvement activities.
  • Recognise data accuracy, regulatory requirements, quality standards, approvals, and human-review responsibilities when using AI.

Tools covered

Generative AI AssistantsAI-Assisted Quality AnalysisDocument IntelligenceSpreadsheet AnalysisRoot Cause Analysis SupportCAPA DocumentationQuality ReportingBasic Workflow Automation

Who should attend

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

Prerequisites & Participant Readiness

  • Basic understanding of quality, process, or operational-management activities
  • Familiarity with quality records, inspections, audits, CAPA, or process documentation 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 Management
  • Identifying AI applications across quality analysis, inspections, documentation, and reporting
  • Understanding AI assistance versus quality-professional judgement and accountability
  • Recognising activities where approved procedures, evidence, and human validation remain mandatory
Practical activities
  • Mapping common Quality Management activities to potential AI applications
  • Comparing a traditional quality task with an AI-assisted approach

Scenarios

Quality Issue to Root Cause & CAPA

Quality Issue → AI-Assisted Analysis → Pattern Review → Potential Root Causes → Corrective Action → Preventive Action → CAPA Summary

Participants use AI to organise a sample quality problem, identify potential contributing factors, and prepare a structured CAPA summary for professional review.

Quality Records to Management Review

Inspection Data + Audit Findings + Complaints → AI Analysis → Recurring Issues → Trends → Priority Actions → Management Summary

Participants use AI to consolidate sample quality information, identify recurring themes and improvement opportunities, and prepare a concise management-ready quality brief.

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