Role-Based Programme
RB1607

AI-Powered Root-Cause Analysis & Corrective Action

Intelligent Problem Solving, CAPA & Failure Prevention

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

Programme Objectives

  • Develop advanced capability to apply AI across problem definition, evidence analysis, root-cause investigation, corrective action, preventive action, and effectiveness verification.
  • Use AI to analyse defects, deviations, complaints, audit findings, incidents, failure records, process data, and corrective-action history.
  • Apply structured RCA methods such as 5 Whys, Fishbone Analysis, Pareto Analysis, cause-and-effect analysis, and evidence-based validation.
  • Build repeatable AI-assisted workflows for issue intake, investigation, CAPA planning, action tracking, escalation, closure, and management reporting.
  • Design responsible AI-enabled RCA and corrective-action workflows with appropriate controls for evidence quality, causal validation, traceability, accuracy, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchDocument AISpreadsheet & Data AnalysisRoot Cause Analysis ToolsPareto AnalysisFishbone Analysis5 WhysQuality AnalyticsCAPA Tracking ToolsReporting & Dashboard ToolsWorkflow Automation

Who should attend

  • Quality Managers
  • Quality Engineers
  • Quality Assurance Professionals
  • Quality Control Professionals
  • Root Cause Analysis Professionals
  • CAPA Coordinators
  • Process Improvement Professionals
  • Continuous Improvement Professionals
  • Operational Excellence Professionals
  • Manufacturing Quality Professionals
  • Supplier Quality Professionals
  • Process Engineers
  • Quality Auditors
  • Operations Managers
  • RCA & Corrective Action Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of Quality Management, process improvement, operations, engineering, or problem-solving activities
  • Familiarity with defects, deviations, incidents, complaints, CAPA, audits, or process-performance data
  • 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 investigation, RCA, CAPA, verification, and reporting
  • Understanding AI assistance versus professional judgement and evidence-based conclusions
  • Recognising the risks of hallucinated causes and unsupported recommendations
Practical activities
  • Mapping an existing RCA and CAPA workflow
  • Comparing manual and AI-assisted problem-solving activities
  • Creating an RCA AI opportunity matrix based on value, evidence, and risk

Scenarios

Recurring Quality Failure to CAPA Closure

Quality Failure → Problem Definition → Evidence Collection → Pareto Analysis → 5 Whys / Fishbone → Cause Validation → Corrective Action → Effectiveness Check → Closure

Participants use AI-assisted techniques to investigate a recurring quality failure, validate the true root cause, design targeted corrective actions, and complete an evidence-based CAPA closure process.

Multiple Incidents to Systemic Failure Prevention

Incident Data + Complaints + Audit Findings + Defects + CAPA History → AI Analysis → Recurring Patterns → Systemic Causes → Preventive Actions → Automated Tracking → Management Dashboard

Participants design an AI-enabled RCA and CAPA workflow that consolidates multiple quality signals, identifies recurring systemic causes, prioritises preventive action, and strengthens long-term failure prevention.

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