Most of us in the technology space are familiar with the old project management adage that you can only pick 2 options: Good, Fast, or Cheap. Up until now, that was ironclad. For the first time, that tradeoff is becoming negotiable, but perhaps not in the way one would imagine.

Countless leaders envision AI dancing through their development ecosystem, magically creating new products with little need for the challenges that come with human designers or developers. Currently, that is a dangerous pipe dream. AI will change over time, but organizations that do not maintain oversight by skilled individuals will spend far more than they save to fix what they create.

The organizations that gain the most from AI over the next several years will not be the ones chasing fully autonomous software development. They will be the ones applying AI surgically to high-friction operational bottlenecks with measurable ROI.

But there is an area where AI is a paradigm shift that will enable success today. Ironically, one of AI’s clearest near-term enterprise use cases may sit in one of the least glamorous areas of software delivery: QA. Yes, that team that is always giving developers defects they cannot reproduce, or that finds a critical defect right before the team is supposed to ship.

Automated testing is not new. Teams have been rolling it into their release pipelines for quite a while now. However, while it helps speed up both testing for new features and regression testing, it has a few major drawbacks: creating and maintaining automated tests is both expensive and time-consuming; changes to functionality or UI can easily break the tests; and scripts and test data need to be recreated repeatedly.

As a result, to make automated testing a viable advantage, an organization requires a sizeable, dedicated team of automation engineers not only to create the initial tests but also to continually hunt down issues that break tests and fix them. It’s an expensive proposition.

Not anymore. How do we know this? Easy. We built it. Imagine this scenario: Mid-sprint, the QA team is ready to test a new feature. With the press of a button, our platform reviews the user stories, and within minutes, not days, a full suite of test scripts is created, including edge cases. Days later, a developer changes a button within the feature. Previously, that would have been a broken test that would have taken half a day of detective work to fix. With our platform, however, the test heals itself and keeps running. In case of a serious failure, a screenshot and video show exactly what broke and where it is. Triage has been reduced from hours to minutes, and the team ships on time.

For the CTO, that means faster, more predictable releases with less firefighting. The QA manager sees their team focused on actual high-priority issues, rather than manually creating scripts and chasing ghosts. Product and Marketing have an effective, brand-strengthening outcome, and Finance recognizes the value of a small automation team doing the work of a large one.

The organizations that gain the most from AI over the next several years will not be the ones chasing fully autonomous software development. They will be the ones applying AI surgically to high-friction operational bottlenecks with measurable ROI. QA is one of the clearest examples available today. That is exactly why we built TestWyze.

Ready to stop choosing 2 options? TestWyze helps organizations modernize QA with AI-driven automation that scales with the pace of development. Let it help you get Fast, Cheap, and Good.