Systems | Development | Analytics | API | Testing

Discover insights from a recent TDWI expert panel on integrating data and AI platforms.

In software product development, many teams tend to ignore quality metrics and focus more on quantity. Such teams face challenges when building for production. They end up pushing to production very low-quality software that is filled with bugs. These bugs alone irritate and drive away product users. In 2022, research done by the Consortium for Information and Software Quality (CISQ) revealed that the cost of poor software quality in the US has grown to at least $2.41 trillion.

You're in the Sprint Review, and the team is feeling pretty good about the new feature, it’s done, the CI (Continuous Integration) pipeline is green, and they have a Friday release planned. Things are going according to plan. Then something worse happens. A test fails. But no one has an explanation. It passed yesterday. It works on my machine. Perhaps it is just the test environment again? You rerun it; green. Rerun it; red. The inconsistency starts introducing doubt. Is it an actual problem?

As Artificial Intelligence (AI) becomes deeply embedded in decision-making across fraud detection, chatbots, and virtual assistants, trust in AI agents is now critical. Users and stakeholders need clear assurance that these systems will behave fairly, clearly, evidently, and reliably in all situations. However, building that trust does not happen by chance; it requires smarter testing strategies specifically designed for the non-deterministic and robust nature of AI.

Your customers want dashboards and real-time analytics from your data. You’ve built a REST API thinking you’re providing great data access. But there’s a fundamental architectural mismatch creating hidden friction.