Systems | Development | Analytics | API | Testing

After attending Oracle AI World, one thing is clear: Oracle is all in on AI. From embedded agents across Fusion Applications to the AI-native Oracle Database and OCI’s massive model-training capabilities, Oracle is positioning itself as the platform where enterprise AI comes to life. The energy was infectious. Every session, demo, and conversation carried a sense of acceleration: faster decisions, faster workflows, faster innovation.

The SmartBear community is made up of curious, collaborative builders. They’re developers, testers, creators, and innovators who every day are experimenting with new technologies, customizing workflows, and solving complex problems.

AI systems change faster than traditional QA models can react, which means quality risks now emerge in real time rather than at release.

‍ Note: This article is the second in this series. Check out the first one here: Why we ditched frontier AI agents and built our own.

Data integration has long been one of the most time-intensive parts of enterprise IT. Connecting multiple systems, reconciling formats, and ensuring data reaches its destination reliably often requires weeks of preparation before the first record moves. But with AI-powered integration, that timeline compresses dramatically. What once took weeks can now be designed, validated, and delivered in minutes.

Organizations waste an average of $12.9 million annually due to poor data quality. Traditional ETL processes built on hard-coded rules break with schema changes, batch processing misses real-time insights, and manual mapping creates bottlenecks that delay critical business decisions by days or weeks.