Role-Based Programme
RB1579

AI-Powered Quality Assurance

Intelligent Quality Systems, Compliance Monitoring & Continuous Improvement

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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 Assurance planning, documentation, process compliance, audits, CAPA, risk management, and continuous improvement.
  • Use AI to analyse SOPs, quality records, audit evidence, process data, deviations, complaints, corrective actions, and management reports.
  • Build repeatable AI-assisted workflows for document review, compliance monitoring, audit preparation, deviation analysis, CAPA tracking, and management reporting.
  • Apply AI to identify quality-system gaps, recurring deviations, process risks, documentation weaknesses, and improvement opportunities.
  • Design responsible AI-enabled Quality Assurance workflows with appropriate controls for accuracy, evidence integrity, traceability, compliance, auditability, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchDocument AISpreadsheet & Data AnalysisQuality AnalyticsProcess Mapping AIAudit & Compliance ToolsRoot Cause Analysis ToolsReporting & Dashboard ToolsWorkflow Automation

Who should attend

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

Prerequisites & Participant Readiness

  • Working knowledge of Quality Assurance, Quality Management, process compliance, auditing, or quality systems
  • Familiarity with SOPs, quality records, deviations, audits, CAPA, inspections, or quality 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 Assurance and quality-system activities
  • Understanding AI assistance versus authorised Quality Assurance judgement
  • Recognising risks involving hallucination, weak evidence, poor data quality, and compliance
Practical activities
  • Mapping an existing QA lifecycle and identifying AI opportunities
  • Comparing manual and AI-assisted Quality Assurance activities
  • Creating a Quality Assurance AI opportunity matrix

Scenarios

Quality Deviation to CAPA Closure

Deviation → AI Classification → Evidence Review → Root Cause Analysis → Corrective Action → Preventive Action → Effectiveness Verification → Closure

Participants use AI-assisted techniques to investigate a simulated Quality Assurance deviation, organise evidence, validate causes, structure CAPA actions, and prepare an evidence-based closure record.

Quality System Data to Continuous Assurance Workflow

Audit Findings + Deviations + CAPA + Document Status + Complaints + Compliance Data → AI Analysis → Recurring Risks → Priority Actions → Automated Follow-Up → Dashboard → Management Review

Participants design an AI-enabled Quality Assurance workflow that consolidates quality-system information, highlights recurring gaps and overdue actions, improves follow-up, and strengthens management visibility while retaining final QA decisions with authorised professionals.

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