Software quality evidence is not the same thing as a testing programme, and the difference is what the first serious supervisory review tends to expose. Most regulated financial institutions have QA engineers, test suites, CI/CD pipelines and dashboards that show green. Far fewer have evidence.
When a QA team at a bank evaluates AI testing tools, the conversation tends to start with the same set of questions that any engineering team would ask: how many test cases does it generate, how accurate is the generation, how well does it handle test maintenance, what frameworks does it support. These are legitimate questions. They’re also the wrong starting point, because they treat the problem of AI-assisted QA at a regulated financial institution as though it were the same problem as AI-assisted QA at a SaaS startup. It isn’t.
DORA requires financial institutions to demonstrate operational resilience to supervisors, not simply to run tests. This article covers what QA teams at banks actually have to produce as evidence, which DORA articles govern it, and where most testing programmes fall short when a supervisory review arrives.
Building Qualigentic forced us to be precise about what actually shifts when AI enters the testing workflow — not what gets automated, but what gets rethought. That’s what we’re sharing here.
There is a version of the software quality conversation that most engineering teams have had many times. It goes roughly like this: testing coverage is too low, the QA process is too slow, the tools aren’t integrated well enough, and something needs to change before the next release. That conversation is useful as far as it goes.
Shift-left testing has been an ambition for a decade and rarely a reality, because writing tests early costs more than teams can absorb. Agentic AI changes that arithmetic. This article covers how Qualigentic generates and maintains test suites from requirements, and what shifts when tests stop being a bottleneck.