Systems | Development | Analytics | API | Testing

Last August, MIT released a landmark report that confirmed what many enterprise leaders had started to fear: most AI pilots are failing. After reviewing hundreds of AI initiatives, researchers found that 95% of generative AI pilots failed to reach production or deliver measurable results. The headline quickly hardened into a cliché: AI doesn’t scale.

Your developers are shipping more code than ever. GitHub Copilot, Cursor, and tools like them have fundamentally changed developer throughput - some teams are seeing 40-76% more code per person per sprint. That is the headline everyone celebrates. The part that keeps engineering leaders up at night is the other side of that equation: your testing pipeline has not changed at the same pace. Tests that used to gate two releases a week now need to gate ten.

Testing a FastAPI app that calls OpenAI, Anthropic, or Gemini gets expensive fast. The problem is not just the API bill in production. It is all the repeated traffic in development: prompt tweaks, CI runs, regression checks, and the load tests you keep putting off because every run burns tokens. Hand-written mocks do not help much once the app is doing multi-step LLM work.

The automotive industry has been undergoing significant changes as it works to adapt growing market demands and challenges associated with vehicles that are becoming much more software-defined. Here, we take a look at some notable automotive trends 2026, including highlights from our report, the 2026 State of Automotive Software Development, in partnership with Auto IQ and the Eclipse Foundation.

It is Monday morning. Your VP of Engineering just forwarded a company-wide memo: every team needs to demonstrate AI adoption by end of quarter. At the same time, you learned last week that your QA budget was trimmed by 15%, because leadership assumes AI will "make testing more efficient." And your developers? Thanks to Copilot, Cursor, and Claude Code, they are now shipping 76% more code per person than they were two years ago.

Key Takeaways Risk in health insurance no longer sits still. It changes with every diagnosis, claim, wearable signal, and care interaction. Treating it as a one-time underwriting event no longer works. Automation doesn’t just make things faster — it keeps things going. With automated risk assessment, insurers can track health risk as it changes, using live, up‑to‑date data instead of one‑time snapshots.

Electronic Health Records (EHRs) are the digital backbone of today’s healthcare. They first began as a simple replacement for paper charts. Over time, they’ve grown into powerful, centralized systems that manage clinical workflows, support data sharing, and help keep patients more engaged in their care. Now, EHRs are much more than just digital filing cabinets.

Most teams know they are spending money on AI in production. Far fewer realize how much they are spending outside production. It’s easy to get lost as you evaluate which model has the best responses, is fast enough, and cheap enough to run in production. That is because the AI bill usually shows up as a giant blob. It is easy to see the total.

As organizations embed Quality Assurance into their SDLC with Jira and Xray, the resulting test data becomes a strategic enterprise asset, vital for product quality, test case traceability, and regulatory compliance. Protecting this asset is paramount, and as its scale and importance grow, organizations require specialized data management capabilities that go beyond standard application features to ensure complete resilience and governance.