Systems | Development | Analytics | API | Testing

In the world of software testing, one crucial element often overlooked is Test Data Management (TDM). As development and testing cycles become shorter, automated, and more continuous, the need for efficient management of test data grows. Whether you’re working in Agile, DevOps, or Continuous Integration (CI), having a robust test data management system in place ensures that your tests are reliable, reproducible, and efficient.

Every technology cycle comes with hype, backlash, and eventually… utility. AI is shaping up to be no different. As we head into 2026, the conversation is already shifting from “AI will replace everything” to “why isn’t this paying off yet?” This shift is heavily influenced by evolving market trends, as businesses and technologists respond to changes in customer behavior, operational patterns, and broader market conditions that shape expectations around AI.

Writing maintainable Gherkin test cases requires focusing on behavior over implementation while avoiding common pitfalls that create brittle tests. The difference between a valuable test suite and an expensive maintenance burden often comes down to how you structure your Gherkin scenarios from day one.

AI test case generation reduces manual testing effort significantly while expanding test coverage and catching edge cases humans overlook. The shift from manual to AI-assisted test case creation is inevitable for teams serious about keeping pace with modern development cycles.

At their best, code generation LLMs reduce cognitive load, accelerate iteration, and serve as a great pair programmer for well-scoped tasks. That said, they also introduce a level of risk. Whether it’s using a variable that was never declared, making up functions that aren’t part of a class, using code from outdated packages, or misdiagnosing an issue, code generation models can create problems.

As businesses continue to amass vast amounts of data, the need for robust data governance procedures has become more critical than ever. Examining data governance procedures has long been a crucial practice for businesses that collect data because it ensures that collected data is managed, stored, and utilized in a secure, compliant, and efficient manner. It also enhances data quality, risk mitigation, and better decision-making.

This is Part 2 of our WEX series. In this blog, we explore how the company scaled self-service analytics by embedding AI—read Part 1 on their people-first approach. You’ve got AI pressure from every angle: execs, customers, and competitors. But legacy analytics doesn’t just slow down development—it frustrates users and undermines the value your product is supposed to deliver.

Automate the boilerplate so you can focus on what actually matters. Developers spend somewhere between 30-50% of their time on repetitive tasks that add little value to the final product. In API development, this overhead is particularly painful: writing nearly identical CRUD endpoints over and over, manually updating documentation that immediately drifts out of sync, copying data between environments, and handling routine maintenance that should happen automatically.

Let me introduce you to Jake. He’s four years old, loves puzzles, and sees the world in his own unique, beautiful way. Like one in 31 children in the US, Jake has autism. His parents are told that early intervention is crucial for him to develop the skills and behaviors he’ll need to thrive. He just needs a little help with things many of us take for granted: pointing to make a request, speaking up, asking for things, making friends.

You may be surprised to learn that more than 70% of software failures due to unaddressed functional issues that could have been caught during testing. Think about it: you release a new app or system, a user clicks their way through a common user flow…and it fails. In our competitive digital economy, we assume performance and intuitiveness—one hiccup, and users will stop using you as a provider, and possibly undermine your credibility.