Reimagine test design, regression and defect triage.
Three QA workflows and the decisions people own.
Test case design
Responsibilities to review
- Read the requirements or PRD
- Draft happy-path and edge cases
- Draft negative and boundary cases
- Add domain-specific cases
Illustrative workflow responsibilities. Confirm the tools, data, human decisions and review criteria for your work.
AI reads the requirements or PRD or design doc, generates the test case matrix (happy path, edge cases, negative cases, boundary conditions). QA reviews and adds domain-specific cases the AI could not infer.
Regression maintenance
Responsibilities to review
- Read failing test logs
- Cross-reference recent code changes
- Propose test-vs-application fix
- Confirm and apply the fix
Illustrative workflow responsibilities. Confirm the tools, data, human decisions and review criteria for your work.
AI reads failing test logs and recent code changes, proposes whether the fix belongs in the test or the application code. Engineer confirms and applies. Flake-detection becomes investigative, not janitorial.
Defect triage
Responsibilities to review
- Read the bug report
- Classify severity, component, and root-cause area
- Draft developer-facing context
- Review, route, and add release-impact judgment
Illustrative workflow responsibilities. Confirm the tools, data, human decisions and review criteria for your work.
AI reads the bug report, classifies severity, component, and likely root-cause area, drafts the developer-facing context. QA lead reviews, routes, and adds release-impact judgment.
Prepare your workforce for the redesigned work.
Your workforce brings people and AI agents together. Workflows define how they share the work. Define what agents do, who reviews their output, and when they hand work back. Use Task Intelligence to understand the tasks within each step, then prepare your people to build and run the new version through simulations, hands-on projects and skill validation. Revisit affected responsibilities and readiness when the work changes.
Task evidence within QA workflows
These research inputs include vendor and source-reported software capabilities, product features and descriptions of work. The stored classifications in this selection contain 977 source examples for AI support and 1,985 for automation. They are not validated workflow outcomes or proof of agent performance. Task Intelligence helps examine the work in context; review each example against your responsibilities, controls and exceptions before using it in a redesign. The first 5 AI-support examples are below.
1,985 source examples classified for automation›
How we work with QA orgs
Co-sponsored model. QA Manager or Director of Quality plus your VP Engineering or Head of Release in the room together. The 6 to 8 week AI Bootcamp prepares people for agreed workflow responsibilities through simulations and assessments. The Transformation Engagement scales it across the QA org.
Want a QA-org-specific walkthrough?
20 minutes. We pull your top three QA task patterns from the dataset and show you the redesign live, with your stack and team mix.
Book a time