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Specialist

AI-Assisted Testing and Product Quality Engineering

Specialist depth for using AI to build quality into the product lifecycle through evidence that connects customer outcomes, engineering decisions, testing, release, and production learning.

The workplace problem

Why this learning matters

Teams often apply AI to generate more tests while product quality remains fragmented across discovery, design, development, release, production, accessibility, performance, security, reliability, and customer feedback.

Intended outcomes

What participants will learn to do

  • Define software-product quality beyond test volume by connecting customer, product, engineering, operational, and risk evidence.
  • Apply AI across specification, test design, analysis, review, release confidence, and production learning with explicit verification boundaries.
  • Create balanced quality decisions and evidence while retaining human accountability for product and release outcomes.

Application at work

How participants can apply it at work

  • A product-quality model and risk map connected to a real change, customer outcome, and production context.
  • A reviewed AI-assisted quality workflow with evidence expectations, quality gates, and an improvement backlog.

Shaped to context

Shaped around your context

Flow Cracker shapes this merged specialist workshop around the customer's product risks, quality model, delivery lifecycle, approved tools, accessibility, security, performance and reliability obligations, and production evidence.

Entry context: Participants should bring practical experience in software delivery, testing, product development, customer feedback, or production operations and one representative quality risk to examine.

Who it is for

Who this is for

  • Quality, QA, test, and software-development engineers in test
  • Software engineers, product owners, business analysts, UX, and accessibility practitioners
  • Technical leads, quality leaders, and engineering managers accountable for release confidence

Opportunity lenses

  • AI to build the product

Capabilities strengthened

  • Engineering, Architecture & Verification
  • Product Discovery & Decision Practice

Connected learning

Discuss the context

Shape the learning around the work that needs to change

Share the audience, workplace problem, constraints, and evidence you want the learning to address.

Discuss this learning need