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A year ago we announced that Databox is becoming an AI-first company. At the time, that mostly meant what it meant for many companies in 2025: AI becoming a strategic priority. Teams were encouraged to experiment and adopt new tools, as it was clear that AI wasn’t a trend we could ignore. That was the easy part. What’s become clear over the last year is that there’s a significant difference between being AI-first and being AI-native.

If you have ever been asked "how do we know we tested everything?" and did not have a clean answer, a requirements traceability matrix is what you were missing. It is one of those deliverables that sounds bureaucratic until the moment you actually need it: an audit, a release sign-off, a requirement that slipped through without a single test covering it. Then it becomes the most useful document you own.

Every bug that gets fixed in your product goes through the same journey. Someone finds it, someone confirms it, someone fixes it, and someone proves the fix actually works. That journey is the bug life cycle, and teams that manage it well ship faster than teams that treat bug tracking as an afterthought.

It was 2am and I was paying for the privilege. Something was on fire in production, and I’d done the modern thing: I pointed an AI agent at it. It ingested the dashboards. It read the logs. It walked the traces. Then it handed me back a beautifully formatted paragraph that said, in effect, “latency is elevated on the checkout path.” I knew that. The page told me that.

Learn to use BigQuery’s new AI.AGG function to analyze unstructured data such as logs and documents at scale.

Data analysis at scale to identify hidden fraud and achieve transaction savings.

Every CIO has a slide deck full of AI pilots by now. A chatbot that answers FAQs. A copilot that drafts emails. An agent that summarizes meetings and files the notes nobody reads. The demos get applause in the boardroom, the budget gets approved for “phase two,” and then largely nothing happens. The pilot quietly lives on in a sandbox, forever 80% done, forever six weeks from launch. If that sounds familiar, you are not behind. You are, statistically, in the majority.

AI-assisted builds grew 164x in a year. The rate teams ship to users fell by a fifth. Arpad Kun, VP of Engineering and Infrastructure at Bitrise, on the gap that opened up in between, and what his team built to close it.

If you’re running Microsoft Dynamics 365 and relying on RSAT (Regression Suite Automation Tool) to keep your test automation ticking, there’s a deadline you cannot afford to ignore: RSAT support ends on 15 May 2027. That’s not far away, and the decisions you make now will shape how painlessly your QA practice survives the transition. So let’s talk about what comes next and why the answer is more interesting than you might expect.

Data center teams are skilled at solving familiar problems such as storage outages, missed forecasts, and late refresh cycles. These are known quantities. Teams have playbooks for them. But 2026 has brought a different kind of pressure. After years of enterprise AI investment concentrated almost entirely on model training, the industry has crossed a threshold: the workload that now defines AI infrastructure isn’t building models. It’s running them. Continuously. At scale. Every day.