Why AI’s future depends on data you can trust, and how CAT makes it real
In 2026, the AI discussion has matured. The question is no longer „should we use AI?, most organizations already are“. The real question has quietly shifted to something much harder: why does AI work well in some companies, and fail completely in others?
Data Testing in the Age of AI: What’s New and What Stays the Same
AI is changing how we work with data every day. It can write code, generate reports, and analyze patterns faster than ever. That’s exciting – but it also means small mistakes can spread more quickly. Some things, however, never change: if your data is broken, your results will be broken too. Garbage in, garbage out still applies.
You might be managing BI dashboards, feeding data into AI models, or handling big data pipelines. And you’ve probably felt that sinking feeling when a small data issue throws off a report or model. AI won’t magically fix bad data – it just makes the problem more obvious. So how do we handle this new reality? Let’s look at what’s changed, what hasn’t, and how modern tools like CAT help.
