Systems | Development | Analytics | API | Testing

AI is redefining how software is created and delivered. It’s transforming development speed, decision-making, and user expectations all while introducing new layers of complexity and risk. To keep pace, testing is evolving beyond automation into true AI-augmented testing, where intelligent systems help teams predict risk and defects, optimize coverage and efficiency, and deliver at the speed of AI-driven change. The industry has moved forward – now users need to catch up.

Our goal with the Tricentis blog is to distill insights that help QA professionals navigate the massive, AI-driven transformation happening across the software delivery landscape. To that end, I reached out to experts across Tricentis, from product and services to marketing and strategy, to hear what they’re really thinking about AI in QA right now. This group brings decades of experience building testing products, guiding enterprise transformations, and shaping how organizations adopt AI.

Many organizations run data quality programs that, on the surface, serve teams well enough. They validate data, flag missing fields, remove duplicates, and reconcile reports. Most of the time, that feels secure enough. When teams collaborate and compare datasets, discrepancies often appear but are dismissed as negligible. Fixing them is built into workflows and job descriptions, even if it takes hours or days. This approach is starting to show its age.

“We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” That’s Amara’s Law, a principle coined by futurist Roy Amara. It explains how emerging technologies, like the early internet, are often overhyped at first, followed by a shift toward recognizing their value and integrating them over time. This thinking is a lot like what we’re seeing today with agentic AI.

AI is fundamentally reshaping software quality, and the organizations leading this shift aren’t waiting to adapt. In October 2025, we brought together over 1,000 quality engineering leaders, practitioners, and innovators for Transform, our annual conference exploring what’s next in software delivery.

After a year of breakthrough innovation in 2025, Tricentis NeoLoad is headed into 2026 with even bigger goals: more intelligence, more automation, and more speed for performance testing at scale. In the coming year, NeoLoad will continue to provide the advanced foundational features to support effective performance engineering practices as well as intelligent workflows that enable quality and performance teams to work more efficiently than ever before.

When Oracle updates hit, many IT teams brace for impact. Backlogs swell, manual checks slow releases, and a patch that should take hours can stretch into days. For enterprise teams running Oracle at scale, outdated testing tools can be inefficient, costly, and difficult to manage. At Oracle AI World, two global organizations shared the stories of how they moved past those bottlenecks.

AI’s impact on quality engineering has been widely discussed, with some predicting a crisis for software testers. The more dire forecasts have narrowed in on the junior tester, as some anticipate that AI’s ability to perform routine tasks will eliminate entry-level roles. As Tricentis has explored, AI will not replace junior testers but will rather remake their jobs, enabling them to engage in strategic work earlier in their careers. But what about the senior tester?

AI code generation is rapidly evolving from a novelty into a key building block of modern software development. According to the Tricentis 2025 Quality Transformation Report, 82% of software professionals are excited about AI agents handling repetitive development tasks, and 84% believe AI will help teams meet increasingly compressed deadlines. Tools like GitHub Copilot and Codex are driving this revolution, offering real-time suggestions and automating boilerplate work.

If you find that your team is struggling to get releases out the door, it could be inefficient testing practices. Oftentimes, software teams don’t know what their tests actually cover, or which tests are relevant after each code change — so they run everything. This means spending hours executing full test suites for minor updates or burning through CI/CD resources while bugs slip through untested paths. On top of this, software is always becoming more complex.