Systems | Development | Analytics | API | Testing

AI has completely reshaped the boundary between human imagination and what’s possible. Along the way, AI use in business has become mainstream, with software delivery among its top adoption areas. In 2026, leading global technology companies are now using AI to generate the majority of their code, with some development teams reporting that they haven’t written code manually in months.

AI is reshaping how software gets built, tested, and delivered. For quality engineering teams, AI agents promise extraordinary acceleration by automating analysis, executing tests, generating assets, and orchestrating tasks across the SDLC. But when enterprises begin experimenting at scale, new challenges appear. Where are these agents running? What exactly are they doing? Who approves their decisions? How do we govern them safely?

In modern software delivery, test results often tell an incomplete story. Test suites run, dashboards turn green, and teams feel momentum. But one important question often remains unanswered: Did we actually test what we changed? This is a gap in traditional testing that is widening as more code is generated by AI. As engineering velocity accelerates (and AI generates increasing volumes of code), the gap between test activity and true coverage is widening.

In the age of AI, where delivery continues to accelerate, release confidence shouldn’t lag behind. Today’s software changes continuously, often generated or modified by AI. That raises complexity while shrinking the time quality teams have to plan, test, and make decisions. Manual workflows and static automation weren’t built for this pace.

Agentic Test Automation for Tosca revolutionizes software testing. Using only natural language prompts, it automatically generates complete, executable test cases — allowing QA teams to keep pace with modern AI-driven development. This latest update expands support for new enterprise technologies and uses Tosca’s automation engine to become even more powerful. Enterprise customers can now create complex, end-to-end tests that are built and supported by Tosca’s proven technology.

In today’s fast‑moving software world, release velocity keeps climbing, and AI is accelerating it even further. To keep quality teams aligned with rapid change, we’ve brought together two powerful capabilities: Tricentis SeaLights’ deep code-level insights and Tricentis qTest’s intelligent test management and AI-generated test creation. Here’s how these technologies integrate to create a complete, AI-driven testing feedback loop.

Do you trust the data your business runs on? Many business leaders I speak with admit they don’t, at least not fully. They find themselves second-guessing dashboards, pulling manual reports “just to be sure,” or worse, relying on gut instinct to make critical decisions. This lack of data trust isn’t just an inconvenience; it’s a business problem.

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.