After my last post about responsible AI practices, some people rightfully inquired about Zebra’s practices. So, I thought it would be best to answer these questions publicly. The first thing I want to stress is that our underlying Code of Conduct, company policies, and departmental procedures provide the foundation for everything we do at Zebra.

Artificial intelligence is reshaping industries at an unprecedented pace. AI has found its way into almost every vertical, from writing code to diagnosing illnesses, promising efficiency and innovation. The idea of an AI Copilot—a tool that acts as your assistant to tackle complex tasks—is particularly exciting. In our space, observability, the possibilities seemed endless. We asked ourselves how AI could simplify troubleshooting in microservices.

There are plenty of good SRE/Ops related podcasts out there. I follow a few of them and listen to episodes whose titles sound interesting. The problem with podcasts is that some episodes focus on one topic, and other episodes deal with a host of topics. In between there is filler and things that are not relevant to the topic but are necessary to carry on a conversation. Spending 30-60 minutes listening to podcasts is not always a great use of time.

Generative AI (GenAI) is revolutionizing industries by driving innovation and delivering unparalleled customer value. Across sectors, its impact is evident.

LLM observability is key to ensuring model performance. Learn its importance, best practices, and actionable steps for optimal results and reliability.

Another day, another AI headline designed to spike your blood pressure. The Service Management industry is no stranger to dramatic proclamations about AI, and neither are you. But what’s the real story behind CNBC’s latest take, “The tech support desk at work is one of the first jobs AI is rapidly replacing?” Does the Palo Alto Networks company need media coverage? Does CNBC? CompTIA? All of them? Perhaps there is a hidden agenda behind the article being published.

With all the hype surrounding AI, it’s critical to focus on building resilient networks capable of handling the performance demands that AI will introduce. As I often say, if your network observability solution isn’t detecting packet loss, neither will your AI engine. When you ask, “What’s the status of our global network health this morning?” a flawed or incomplete response could jeopardize critical decisions.

I’ve been quite open about the many ways AI-driven projects can go wrong. With so many ways to fail, what makes Sumo Logic Mo Copilot set up for success? Let’s discuss each of these points in detail.

When you have a complex IT environment with many disparate tools, data sources, and teams, alert noise becomes overwhelming. This can delay incident response and cause missed alerts, ultimately leading to critical incidents and outages. Datadog Event Management’s Event Correlation groups and deduplicates events and alerts, reducing noise and helping response teams act on alerts faster.

In many ways, the upcoming year is shaping up to be one of opportunity and innovation as IT leaders see more benefits and options around AI than ever before in running the enterprise. By the same token, this progress is creating new complexities and choices for organizations to navigate. Through conversations with ScienceLogic customers, leading industry analysts, partner companies and key executives, several AI-related themes have emerged moving into 2025.