According to GitLab’s 2022 DevSecOps Global Survey, most teams rely on 5 to 10 different tools to test various technologies within their organization.
For years, testing leaders have worked to expand automation coverage to make testing more effective and ensure updates don’t disrupt business operations. But despite these efforts, manual testing is still the norm—which is slow, expensive, and leads to poor production quality and delayed releases.
This makes testing one of the biggest roadblocks in delivering value to customers.
The issue is worsened by the fragmented nature of many testing platforms. Vendors often add capabilities by acquiring other tools, leading to a collection of poorly integrated systems. As a result, users are forced to juggle multiple tools with different architectures just to test or automate different technologies.
These disjointed solutions weren’t built to integrate AI into their workflows, making it harder for organizations to adopt and benefit from the latest AI innovations.
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