Systems | Development | Analytics | API | Testing

As enterprises race to adopt AI, weak data foundations are preventing more than half (58%) of organizations in the United States and Canada from realizing value and contributing to an estimated $108 billion in wasted global AI investment each year, according to a report from Hitachi Vantara. The reason is rarely bad models or lack of ambition.

Open a brokerage’s CRM instance a few years in, and it rarely looks like a sales tool anymore. Somewhere along the way, it picked up MLS feeds, transaction history, integration logic, reporting dashboards, and lately, the raw data behind a first AI pilot. None of that was the plan. Each piece got bolted on because the CRM was the system already sitting there. A CRM was built for a narrower job than that: logging a call, tracking a pipeline, managing the relationship an agent owns.

Reaching a milestone is always exciting. Some milestones carry a deeper meaning. Today, we're proud to share that the N|Solid Runtime has surpassed 2 million downloads. The milestone reflects growing momentum, with downloads accelerating and putting us on track to nearly double last year's total. To us, this isn't simply a download count.

Xray is transitioning into a Forge App, Atlassian's modern cloud development platform, using Forge Remote to strengthen security, align with Atlassian's long-term roadmap, and support future innovation. If you're wondering what the Xray Forge migration means, the short answer is simple: your testing workflows stay exactly the same. The changes happen behind the scenes, improving the platform that powers Xray while maintaining the features, scalability, and performance your team relies on.

While the benefits of LLM orchestration layers and autonomous agents are clear, they also bring a new set of non-deterministic failure modes that traditional unit testing cannot detect. A study by RAND Corporation found that 80.3% of AI projects fail to achieve the desired business outcomes, and this is because of issues in the data pipeline and model integration, not algorithmic problems.Using traditional software, you will get predictable results from known inputs.