Systems | Development | Analytics | API | Testing

The expectations of users in the modern digital environment are ruthless. A smooth, user-friendly, and responsive online experience is no longer a competitive edge to be obtained; it is the standard for keeping customers. To design such experiences, Quality Assurance (QA) should extend beyond traditional functional testing.

Suppose you’re hungry and can’t go outside to have food due to ‘n’ number of meetings already lined up, what would you do? You’ll just open your favorite food delivery app, scroll through endless restaurant options, place an order within minutes, and get your meal delivered right to your desk. Convenient, isn’t it?

By 2026, all-in-one data platforms will dominate because they deliver faster time-to-value, built-in governance, and AI copilots that actually work. Orchestration-only tools remain powerful for engineering-heavy teams, but most organizations will move to managed platforms that reduce incidents, simplify compliance, and accelerate insight delivery.

Does it sometimes feel like the world of AI is constantly resetting the clock? One day you’re under pressure to deliver value fast with your AI project. The next, a prominent study claims most projects deliver zero value. Then, a leading business publication explains that, actually, value doesn’t even matter right now. All while the technology itself is advancing at a dizzying pace.

Banking transformation is one of the highest-stakes projects many financial institutions undertake. Whether you are replacing or upgrading core banking systems, launching new digital channels or shifting to cloud-native operations, the risks are many – and the failure rate remains alarmingly high. Recent industry studies suggest more than 55% of banks cite legacy core banking limitations as holding back their digital goals.

The growth in the ERP SAAS space has seen a 17% increase in the last year projected to increase to $62 billion in 2028. Customers prefer to take the enterprise route to manage their business. The hassles of maintaining custom applications, adapting to change at pace to meet the competitive edge and flexibility to provide their customers the best experience are some of the driving factors.

Every time a website crashes under load, or a feature responds so slowly that users abandon it, you’ve run into a missing or poorly defined non-functional requirement. Studies show that unclear requirements contribute to nearly 47% of project failures. Most of those failures come from missing or weak non-functional requirements.

We are pleased to introduce Tricentis Agentic Test Automation for Salesforce, an AI-driven solution that makes it possible to achieve high-quality Salesforce environments faster and with less effort. We recently announced Agentic Test Automation, enabling Tricentis Tosca to generate comprehensive, end-to-end tests using natural language. With this update, we are extending this capability and applying the power of agentic AI to your Salesforce environments.

As enterprises embed gen AI into their workflows, many are discovering a minefield of risks. Data privacy breaches, misinformation, adversarial attacks and hidden bias are just a few of the challenges that can derail gen AI initiatives. These aren't just technical concerns, they're business-critical issues that can erode trust, trigger legal consequences, and tarnish reputations.

Two data challenges exist across almost all organizations: access and trust. These issues scale exponentially as an organization grows to the point that it can no longer hand around sheets of paper or approve database access. The demand for better data access drove the history of data warehousing, following the ethos that better decisions come from more data and that compute would catch up with demand. However, the hunger for collecting more data didn’t come without a cost.