Introduction

AI is transforming IT quality assurance by improving test design, detecting defects earlier, strengthening monitoring, and accelerating root-cause analysis. This practical program equips IT quality specialists with AI-enabled methods to enhance QA effectiveness and efficiency—while maintaining governance, validation, and responsible use.

Course Objectives

By the end of this course, participants will be able to:

• Understand where AI supports IT quality assurance and where human review is essential
• Use AI to improve requirements clarity, test design, and coverage
• Apply AI to defect detection, triage, and root-cause support
• Enhance service quality monitoring using AI-assisted signals and alerts
• Establish simple governance, controls, and metrics for AI-enabled QA

Target Audience

This course is designed for:

• Senior IT quality specialists and QA leads
• Software testing and test automation professionals
• IT service quality and operations teams
• PMO/delivery teams responsible for release quality
• Risk, compliance, and audit teams supporting IT controls

Course Outline

Day 1: AI Basics for IT QA

• AI use cases in QA
• AI limits and risks
• Data needed for AI QA
• Human-in-the-loop approach
• Activity: QA use-case shortlist

Day 2: AI for Test Design

• AI-assisted requirements review
• Test case generation basics
• Test prioritization and risk-based testing
• Coverage checks and gaps
• Workshop: Build a test pack

Day 3: AI for Defects and RCA

• Defect prediction concepts
• Smart triage and clustering
• Root-cause support using patterns
• Reducing duplicate defects
• Activity: Defect triage simulation

Day 4: AI for Service Quality

• AIOps overview for QA
• Anomaly detection basics
• Alert noise reduction
• Linking incidents to releases
• Case study: Quality incident review

Day 5: Governance and Metrics

• Controls for AI QA outputs
• Validation and documentation basics
• QA metrics and dashboards
• Adoption plan and training
• Final project: AI QA playbook