Most QA teams do not struggle because they cannot automate. They struggle because automation gets buried under everything around it.
Triaging nightly failures. Chasing missing product context. Creating branches, splitting commits, keeping frameworks tidy. Writing routine UI tests that still take hours in a complex application. These are the tasks that consume senior engineering time without delivering proportional value.
In a recent internal meetup, one of our automation engineers shared how we tackled this problem on a large, documentation-light product where a single AQA engineer was maintaining three frameworks (UI, backend, and load) simultaneously. The outcome was notable: by using an AI coding assistant running directly in the terminal, the team automated the repetitive parts of test automation and reclaimed 8-10 hours per week for higher-value work.
On this project, test scenarios were not trivial. Workflows spanned UI and backend behavior, and simply understanding what to automate required significant time investment.
The biggest time sinks were predictable:
Instead of using AI only to write test cases (which many teams already attempt), this team aimed for something broader: AI support across the entire QA lifecycle, from triage to understanding to implementation to knowledge preservation.
The team integrated an AI coding assistant into a Playwright and TypeScript UI automation framework with a small set of command-driven workflows that engineers could run consistently. This approach delivers measurable improvements in test automation efficiency without requiring a complete overhaul of existing QA processes.
Automated test failure analysis with Slack integration. Instead of digging through raw logs every morning, the pipeline parses Playwright test results, sends failures to the assistant with a structured prompt and project knowledge base, then posts a short, readable Slack summary per failed spec including likely cause and suggested fix. The engineer's new workflow: read the Slack summary first, then decide what actually needs hands-on investigation.
A key finding: cost was not a blocker. The team initially added cost controls, but real-world usage came out to approximately $0.10 or less per analysis run, making this approach viable even for teams with limited budgets for AI tooling. This cost efficiency compares favorably to the engineering hours previously spent on manual triage.
Intelligent codebase queries when documentation is missing. This ended up being the biggest day-to-day win for developer productivity. Instead of interrupting developers for context, the engineer could ask questions like: Where is this feature implemented? What is the expected behavior for this flow? How do these selectors get generated? Because the assistant could read project files, it answered with much higher accuracy than generic chat and dramatically reduced back-and-forth communication overhead.
Branch and commit automation for CI/CD efficiency. This sounds minor until you live it. Branch creation becomes a single command tied to a Jira ticket. Commits get auto-generated and split into logical chunks, such as page object changes versus registration versus the test itself. The engineer summarized it well: "I forget when I created a commit by myself last time." This is not about laziness. It is about removing tiny friction that accumulates every day and slows release cycles.
Automated test generation and regression test fixes from Jira tickets. For new automation: provide a Jira ticket ID, the assistant fetches steps, generates the Playwright test script, and creates or updates page objects only when needed. For fixing existing tests in regression suites: provide the ticket ID, the assistant finds the relevant test case in the framework, runs it, analyzes failure, and proposes a fix. This capability alone accounts for significant time savings in test creation and maintenance.
The hardest part was not wiring a tool into the terminal. It was building a reliable project knowledge base, a set of Markdown files capturing framework rules and patterns, common pitfalls, selector strategy, how to structure page objects, how to format Slack output, and how the pipeline should interpret results.
Why it matters: the assistant's output quality tracks directly to how well it understands your rules. The engineer spent the first three to four weeks iterating on this knowledge base to improve accuracy, especially for failure analysis.
This aligns with a principle that applies across AI implementations: garbage in, garbage out. The quality of your documentation and rules directly determines the quality of AI-assisted output. Teams that invest in comprehensive test documentation see proportionally better results from AI-assisted automation.
Jira test-step quality directly affects generated test quality. If a human can re-execute the test from the Jira steps, the assistant generally generates a strong test. If Jira steps are vague, incomplete, or inconsistent, the automation will reflect that. Treat test steps like an API contract. Clear inputs produce reliable automation.
Selector strategy is everything, and data-test attributes win. When the UI has reliable data-test attributes, selector generation is excellent. In this project, the engineer reported approximately 70% of selector generation required no changes. Legacy UI without consistent test attributes still causes issues. This is where humans often need to adjust selectors. Build testability into UI standards early, or pay for it indefinitely.
This approach is not without limitations. Engineering leaders should understand these constraints before implementing AI-assisted QA automation.
Knowledge base investment is front-loaded. Teams should expect three to four weeks of iterative refinement before the system performs reliably. This is not a plug-and-play solution, and organizations expecting immediate ROI may need to adjust expectations.
Self-healing tests remain experimental. The team has a self-healing flow in progress where the assistant creates a fix branch automatically and proposes changes to heal tests. Right now, it is not fully reliable for end-to-end testing scenarios. Even partial automation helps when failure analysis is already fast and structured, but human review remains essential for quality assurance.
Jira quality becomes a hard dependency. If your test specifications are inconsistent or incomplete, AI assistance amplifies rather than solves the problem. Teams with poor documentation discipline will not see the same results.
Legacy UI compatibility varies. Applications without consistent test attributes still require significant manual intervention for selector management. This limitation affects many enterprise applications with technical debt.
This approach is ideal when:
It is not about replacing engineers. It is about removing the friction that keeps them from doing the hard work only humans can do: modeling risk, improving coverage strategy, and raising quality standards across teams.
For teams considering AI-assisted QA automation:
The goal is not more automation. The goal is better use of engineering time and faster, more reliable software delivery.
If your team is running Playwright or any modern test automation stack and feeling the automation tax of triage, documentation gaps, and routine chores, Forte Group can help you design a safe, repeatable rollout starting with the workflows that deliver the fastest ROI. Contact us to discuss your QA automation challenges.