Systems | Development | Analytics | API | Testing

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.

Your AI agent just placed 47 duplicate orders. It called the wrong API three times in a row. It looped through the same workflow for six minutes before anyone noticed. Nobody caught it in testing because nobody built the right tests. That's not a hypothetical. Enterprises using AI agents face this exact problem every week. The AI agent works perfectly in staging, but fails silently in production, and by the time the on-call engineer gets alerted, real customers are already affected.

Object recognition is the capability of test automation tools to identify, locate, and interact with user interface elements within an application under test. It serves as the bridge between automated test scripts and the visual elements that end users see, enabling tests to accurately simulate user actions and validate application behavior.

Data privacy laws continue to evolve and expand their reach, touching consumers, businesses, and regions of the world. The European Union’s General Data Protection Regulation (GDPR) has inspired many countries to establish their own regulations and set similar parameters for data collection. The Chile Data Protection Law is one of these regulations. While staying compliant isn’t always simple, it’s necessary for your operations and maintaining customer trust.

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.