Role-Based Programme
RB1615

AI for Quality Management

Advanced Quality Analytics, Process Improvement & Intelligent Compliance Automation

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced capability to apply AI across quality planning, assurance, control, process improvement, audits, CAPA, and management reporting.
  • Use AI to analyse quality records, inspection data, customer complaints, non-conformities, audit findings, process metrics, SOPs, and corrective-action information.
  • Build repeatable AI-assisted workflows for quality monitoring, root-cause analysis, CAPA management, document review, audit preparation, and reporting.
  • Apply AI to identify recurring defects, process deviations, quality trends, control weaknesses, improvement opportunities, and areas requiring deeper investigation.
  • Design responsible AI-enabled Quality Management workflows with appropriate controls for data accuracy, compliance, evidence integrity, traceability, auditability, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchDocument AISpreadsheet & Data AnalysisQuality AnalyticsStatistical Analysis ToolsRoot Cause Analysis ToolsProcess Mapping AIReporting & Dashboard ToolsWorkflow Automation

Who should attend

  • Quality Managers
  • Quality Assurance Managers
  • Quality Control Managers
  • Quality Engineers
  • Quality Analysts
  • Process Excellence Professionals
  • Continuous Improvement Professionals
  • Quality Auditors
  • Compliance & Quality Professionals
  • CAPA Coordinators
  • Process Improvement Managers
  • Operations Quality Professionals
  • Supplier Quality Professionals
  • Quality Systems Professionals
  • Quality Management Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of Quality Management, Quality Assurance, Quality Control, process improvement, or operational excellence
  • Familiarity with quality records, audits, non-conformities, CAPA, SOPs, inspections, or process metrics
  • Basic proficiency with spreadsheets, documents, data interpretation, and workplace productivity applications
  • No previous AI course attendance required
  • No programming background required

TOC Modules

Concepts
  • Understanding Generative AI, analytical AI, Document AI, and workflow automation
  • Mapping AI opportunities across Quality Planning, Assurance, Control, and Improvement
  • Understanding AI assistance versus qualified quality judgement and authorised decision-making
  • Recognising data-quality, hallucination, traceability, and compliance risks
Practical activities
  • Mapping an existing Quality Management lifecycle and identifying AI opportunities
  • Comparing manual and AI-assisted quality activities
  • Creating a Quality Management AI opportunity matrix

Scenarios

Quality Issue to CAPA Closure

Quality Issue → Non-Conformity → Data Analysis → Root Cause Investigation → Corrective Action → Preventive Action → Effectiveness Check → Closure → Management Reporting

Participants use AI-assisted techniques to analyse a simulated quality problem, structure root-cause investigation, design CAPA actions, track implementation, and prepare a management-ready closure report.

Quality Data to Continuous Improvement Workflow

Defect Data + Audit Findings + Customer Complaints + Supplier Quality + CAPA Status → AI Analysis → Recurring Themes → Priority Improvements → Automated Follow-Up → Dashboard → Quality Review

Participants design an AI-enabled continuous quality workflow that consolidates quality signals, highlights recurring issues, strengthens action tracking, and improves management visibility while retaining final quality decisions with authorised professionals.

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