Blog
By Kevin Surace | AI, QA
Why AI in QA Has Been Such a Disappointment
There is a quiet truth in enterprise QA right now.
Many teams feel let down.
For the last several years, vendors have promised an AI revolution in testing. Autonomous agents. Self healing frameworks. Copilots that would “change everything.” Yet when you talk to QA leaders privately, the story is different.
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By Kevin Parker | API, QA
By Kevin Parker | Kubernetes, QA, AI
By Kevin Parker | AI, Recorders, Script Debt
AI vs. Recorders: How Appvance Eliminates Script Debt
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Teams of people read requirements.Teams of people wrote test cases.Teams of people created automation scripts.Teams of people ran regressions.Teams of people maintained the whole thing every release. That model is being replaced right now. Not someday. Now. Appvance clients are already using AIQ to start with intent, including business requirements, existing manual test cases, user
It is here now. Our clients are already using AI to turn business requirements, user stories, Gherkin, manual test cases, and other artifacts into test cases, scripts, execution, and results. That alone changes the economics of QA. But the bigger breakthrough is what happens next. With Appvance AI Script Generation, the AI does not stop
For years, QA leaders have measured the cost of automation by the number of tests they’ve created. They’re measuring the wrong thing. The real cost of test automation isn’t writing scripts. It’s maintaining them. Every UI update. Every workflow change. Every release. Every new browser version. Every modified API. Each change creates another round of
The testing landscape has shifted. What once seemed revolutionary—adding AI features to traditional testing tools—now feels outdated. Organizations adopting “AI-enhanced” solutions are discovering a critical gap between surface-level AI integration and genuinely transformative AI-first platforms. The Rise of AI-Enhanced Testing Over the past few years, testing vendors have rushed to add machine learning capabilities to
Over the last two years, AI copilots have become one of the most visible trends in software development and testing. They can suggest code, generate test scripts, recommend assertions, and help engineers complete tasks faster. For many organizations, these tools represent a meaningful step forward. But they are not the destination. They are a bridge.
For more than two decades, software test automation has revolved around one central artifact: the script. Whether written in Selenium, Cypress, Playwright, or a proprietary framework, automation teams have invested countless hours creating, maintaining, debugging, and updating scripts. Entire organizations have been built around this model. Automation engineers write the code. QA teams maintain it.