Systems | Development | Analytics | API | Testing

Every customer interaction generates signals that matter—a failed checkout, repeated form errors, a frustrated support call, a confusing AI agent exchange, or an unresolved email thread. Individually, these are isolated events. Connected, they reveal customer intent, friction points, operational risk, and opportunities for action.

As regulatory frameworks such as the General Data Protection Regulation (GDPR), Digital Operational Resilience Act (DORA), and Network and Information Security Directive 2 (NIS2) converge with the US Clarifying Lawful Overseas Use of Data Act (CLOUD Act), contractual assurances are no longer a sufficient defense. For senior leadership, digital sovereignty has evolved from a compliance checkbox into a core architectural requirement.

The online learning explosion has exposed a massive gap… The numbers are staggering. The global e-learning market, valued at approximately USD 299.67 billion in 2024, is projected to surpass USD 842.64 billion by 2030, growing at a CAGR of 19.0%.

Every analytics vendor claims AI. Few can prove their AI is doing real analytical work. Here is what executives need to verify before committing budget to an AI-powered analytics tool.

You turned on an AI feature in your analytics tool. It surfaced an insight about your pipeline. You looked at it, paused, and closed the tab because you weren’t sure the number was right. AI-ready data would have made you forward it instead. It’s data that is clean, structured, and governed consistently enough that an AI model can reason about your metrics without a human translating or reconciling them first.

Manual testing is essential to quality assurance, but it doesn’t always scale with fast release cycles. Clicking through forms, checking user flows, and repeating the same regression tests before every release can quickly become a bottleneck. Automation testing takes repetitive checks off your QA team’s plate. Instead of manually checking the same flows again and again, teams use testing tools to run predefined tests automatically.

CodePush is a great way to ship over-the-air (OTA) updates, avoid app store approval delays, and roll out changes cautiously. Even though App Center has closed down, there are many options available to get started with CodePush. But some of the default settings can create unwanted behaviors, stopping updates from installing or making the app look like it’s crashed.

A hospital might have years of EHR data, ICU records, staffing logs, claims history, and diagnostic reports in different systems. Yet it may still miss signs of patient deterioration before an ICU escalation. This gap is why predictive analytics in healthcare has shifted from experimental AI projects to a key strategy in 2026. Now, healthcare organizations use predictive models to identify sepsis risk earlier.

The first-ever Sembi Software Quality Pulse Report is based on nearly 4,000 responses from QA engineers, developers, security professionals, and engineering leaders worldwide. The findings paint a picture of an industry in motion—and a QA function that increasingly relies on tighter integration, thoughtful AI adoption, and better-connected workflows to keep up. Here's a look at some of the data that matters most for agile QA teams working inside Jira-native environments. TL;DR.

It happens to all of us. As software projects grow, parts of the production code we ship end up without a comprehensive test suite. When you take another look at the same area of code after a few months, it may be difficult to understand; even worse, there might be a bug, and we don't know where to begin fixing it. Modifying production code without tests is a major challenge.