Systems | Development | Analytics | API | Testing

Take this scenario: Your team ships a release with 91% code coverage. Every test in the suite passes. The pipeline is green, and leadership signs off. But two days later, a critical defect surfaces in production. Upon investigation, you find that the changed code was never actually tested, and the tests that were run covered different paths entirely. That 91% was real, but it was just measuring the wrong thing. And as AI tools generate more of the code inside those pipelines, the gap widens.

This year has already marked a leap forward for Tricentis NeoLoad with the arrival of agentic capabilities that open the door to a new era of performance engineering. But even as we reshape what’s possible, we remain focused on the everyday realities and priorities of performance teams. With the 2026.1 release, NeoLoad continues to evolve in practical, customer-centered ways.

Data analytics and science professions have undergone a dramatic transformation in the last decade. Demand for data talent has risen steadily, but the skills required to succeed in the field have evolved right alongside it. And now AI is a defining variable shaping what the next decade of analytics work will look like. It speeds up routine tasks, gives data access to more business users, and raises new questions about what skills will matter most in the not-so-distant future.

Software testing has a bad rap for bottlenecks — and nowhere is that truer than in the SAP world. An overwhelming majority of SAP orgs continue to rely on manual testing practices that can consume up to 30% of implementation budgets, making QA out to be a persistent roadblock to transformation. To be fair to SAP QA teams, the issue is not as much about inefficiency as complexity.

Over 1,000 people around the world tuned in as Tricentis CEO Kevin Thompson and VP of AI David Colwell unveiled our new integrated platform, followed by a live demo from Enterprise Solution Architect Matt Serpone. From our headquarters in Austin, Texas, we unveiled a unified solution designed to help enterprises treat quality as a coordinated system rather than a collection of disconnected tools.

Last August, MIT released a landmark report that confirmed what many enterprise leaders had started to fear: most AI pilots are failing. After reviewing hundreds of AI initiatives, researchers found that 95% of generative AI pilots failed to reach production or deliver measurable results. The headline quickly hardened into a cliché: AI doesn’t scale.

Modern SAP customers often face competing demands. While navigating the routine complexities of an SAP system, they must also prepare for faster releases and looming S/4HANA deadlines, juggling the day-to-day with long-term innovation. Intelligent quality assurance helps SAP users balance these priorities.

Thanks to AI, software today ships faster and with more complexity than ever before, and performance teams that rely on workflows built for a slower era are at risk of falling behind. Reliance on manual steps, niche expertise, and disconnected tools create bottlenecks that add risk to every release. Tricentis NeoLoad is leading this paradigm shift with AI-powered performance capabilities that close the gap and match the pace of validation to that of modern software delivery.