Operations | Monitoring | ITSM | DevOps | Cloud

A new HBR study reveals that the race to build and manage AI agents may be pushing knowledge workers toward a new form of cognitive overload. If you spend any time on LinkedIn these days, you’ve probably seen the same type of post over and over. Someone proudly announces they built an AI agent that now writes their emails, analyzes data, drafts presentations, and maybe even ships code.

You probably use ambient AI every day without even knowing it. When your Apple Watch is telling you to stand up after sitting too long, your CGM recommends you eat a snack, or even when your smart home lights dim around the time you go to bed, every night…that’s ambient AI. Among other things, ambient AI is there to help you stay healthy, tracking what you do in the background and making decisions based on your previous actions and preferences.

Artificial intelligence has moved far beyond experimentation. In 2026, AI systems are embedded into customer support workflows, clinical decision support tools, fraud detection engines, and internal copilots across nearly every industry. Adoption is accelerating quickly. According to McKinsey, 23% of organizations are already scaling agentic AI systems, while another 39% are actively experimenting with them. Yet the path to reliable production AI remains uncertain.

Jobs in the technological sector have been shrinking. The Chief Economist at Glassdoor states that in the first half of 2025, tech employment shrank by an average of 1,583 jobs each month. Looking at tech employment cumulatively, it has declined by 1.9% since peaking in 2022. Despite this downturn, opportunities still exist for skilled professionals who can adapt to evolving industry demands. Companies continue to invest in high-impact positions that drive innovation, efficiency, and growth.

Israel’s healthcare system is widely recognized as one of the most digitally advanced in the world. Electronic health records are deeply embedded across hospitals, and platforms like Chameleon sit at the center of clinical operations. Patient data is captured, structured and accessible at nearly every stage of care delivery. But digital maturity alone does not guarantee operational efficiency.

Platform engineering has emerged as a response to the growing complexity of modern software delivery. As organizations adopt Kubernetes, microservices, CI/CD pipelines, and infrastructure as code, they are creating dedicated teams responsible for building and operating the internal platforms that power developer workflows.

Healthcare is shifting fast, and artificial intelligence is no longer a future concept sitting in research labs or pilot programs. It’s already embedded in clinical workflows, operational systems, and patient interactions, often in ways that feel subtle, uneven, and sometimes uncomfortable.

You’ve done it. Your machine learning model is live in production. It’s serving predictions, powering features, and quietly doing its job. Dashboards are green. There are no errors in the logs. Nothing appears broken. And yet, something is wrong. Predictions are getting less reliable. Users are waiting a little longer for responses. Conversion rates are slipping. Trust is eroding, but no alert fires, no system crashes, and no one knows there’s a problem until the damage has been done.

Over the past couple of weeks, as snowstorms and extreme cold swept across much of the Northeast, something interesting started happening on our end at OnPage. Our phones lit up. Not from healthcare teams or IT operations/tech teams, which is where many people expect us to be used, but from HVAC companies, contractors, and property management firms scrambling to prepare for what they knew was coming.

As we move further into 2026, we wanted to pause for a moment and reflect on what the past year looked like for OnPage, not just in terms of features shipped, but in how the platform evolved to better support the way teams actually work in high-stakes environments. 2025 was a foundational year for us.