AI for IT Quality AssuranceArtificial Intelligence (AI)

In any city around the world 00447455203759 Course Code: s

Course Description

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 Outlines

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