Operations | Monitoring | ITSM | DevOps | Cloud

7 Best AI Voice Tools Every Online Educator Should Try

Online education has grown rapidly in recent years, making it easier for teachers, trainers, and course creators to reach learners across the globe. But with this growth comes a new challenge: how to keep students engaged in a virtual classroom. One of the most effective ways educators are addressing this is by using voice tools. These tools allow teachers to create professional-quality voiceovers, enhance presentations, and make learning more interactive.

Skywork AI: Revolutionize Your PPT with Cutting-Edge Tech

Skywork AI transforms how U.S. professionals build presentations. Start with a concept, and the platform scans millions of documents to produce fast, source-backed summaries and polished slide drafts. This cuts research time and helps teams move from idea to stakeholder-ready decks in hours rather than days. Executives, analysts, and students benefit from a single workflow that creates outlines, slides, and final reports. The models draw on an open-source lineage and 3.2TB of multilingual and code training data, giving breadth for complex topics.

Introducing Cortex MCP in Devin: AI Engineering Guided by Your Best Practices

Cortex is now in the Devin Marketplace keeping your AI within the guardrails of your org wide best practices With this integration, Devin, the world’s first AI software engineer, can use Cortex data & best practices, like Scorecards, to understand your engineering standards and automatically fix issues at scale. Here’s how it works: Watch the demo to see how Eyal, one of the engineers at Cortex, used Cortex & Devin to turn best practices into action.

AI in Server Monitoring: Why Human Context Still Matters in 2025

When Microsoft rolled out Windows Server 2025 last November, it marked a turning point in how IT teams think about monitoring. Suddenly, AI-powered features like anomaly detection, predictive resolution, and even self-healing aren’t ideas on a roadmap — they’re built into the very fabric of enterprise infrastructure.

Scaling AI the right way in the enterprise

AI isn’t the future—it’s already here, shaping inboxes, dashboards, and project roadmaps. Yet, despite the hype, most enterprises struggle to scale AI in ways that deliver real impact. In this episode of the ManageEngine Insights Podcast, host Jeremy Spence sits down with Michael Barnes, a trusted advisor to APAC C-level leaders, to uncover why so many AI initiatives stall after the pilot stage.

Automate or Elevate? 5 Steps to Build an AI-Powered Incident Playbook

Modern development tools, CI/CD infrastructure, and AI have accelerated the pace at which companies release software. This speed supports innovation, but it also increases complexity and the chance of something breaking in ways that aren’t immediately obvious. Teams now deal with more operational data, complex failure patterns, and systems where a small configuration change can ripple across dozens of microservices.

How GenAI is Shaping Elastic Customer Support

Discover how GenAI has accelerated Elastic's customer and support efficiency. Built on Elastic’s Search AI Platform, the Support Assistant delivers self-service in-product customer support and capacity gains within our support function. Julie Rudd, VP of Support at Elastic, shares how it speeds up issue resolution by combining generative AI with Elastic’s deep knowledge base. Hear directly from a support engineer how the Support Assistant streamlines case resolution and helps engineers and customers find answers faster.

The Blind Spots That Haunt Legal IT

In a recent survey, Udacity’s team explored the evolving landscape of AI adoption by asking 2000 professionals (including those in the legal sector) if they used AI. Unsurprisingly, over 90% of respondents said they did. More concerning, 72% of managers reported personally paying out of pocket for AI tools to use at work, introducing uncontrolled risk into corporate environments.

How AI Turns Monitoring From "What Now?" Into "What's Next?"

It's 3 AM. Your phone starts buzzing with alerts, and you stumble to your laptop only to be greeted by a dashboard that looks like the control panel of a nuclear reactor in meltdown: Red lights everywhere. Numbers that should be green are decidedly not green. And your brain, still foggy from sleep, is asking the most fundamental question in all of IT operations: "Okay, yes, there's clearly a problem... but, now what?".

The real reason your AI initiatives are failing

AI has made it faster and easier to change a codebase than ever before. But in a system as complex and interdependent as modern software delivery, writing code has never been the biggest challenge. For most teams, the real constraint is getting that code safely into production. So while AI assistants and autonomous coding agents have dramatically accelerated the pace of change, for many organizations those changes are piling up against bottlenecks that were already slowing them down.