01What AI is actually good at in testing
A few uses are genuinely useful today, not someday:
- Test-case generation from context. Point a model at a requirement, spec, or issue and it drafts candidate test cases — titles, steps, expected results. This solves the blank-page problem and catches cases a tired human forgets.
- Summarising and triage. Condensing a long issue or defect thread into a few lines, or clustering similar failures, saves real time.
- Edge-case suggestion. Asking "what could go wrong here?" against a description reliably surfaces a few scenarios worth adding.
02Where AI still needs a human
- Judgement about risk. A model has no idea that your billing path matters more than a settings toggle. Prioritisation stays human.
- Deciding what "correct" means. AI can draft an expected result; it cannot authoritatively confirm one for your product.
- Accountability. Someone has to own what shipped and what was tested. That is a person, not a model.
The pattern that works: AI drafts, a human reviews, the team decides. Tools that respect that — generating reviewable drafts rather than auto-creating untouched cases — fit how QA actually works.
03How this looks in a test management tool
In TestOrchestrator, AI test-case generation produces drafts mapped to your project's template (title, priority, steps, expected result) from a source you choose — pasted text, or an item in a connected tracker like Linear, Notion, Jira, or Azure DevOps. Nothing is created silently: you review the drafts and save the ones you want.
If your team already works with an AI client like Claude or Cursor, you can also let it act in your workspace directly through the MCP server: the agent authors and updates cases under a personal key that can only do what you can. See test management for AI agents.