Operations | Monitoring | ITSM | DevOps | Cloud

At IBM’s Think conference this week, the company made a bold announcement: 94% of its HR functions are now handled by AI, a shift they claim will generate $3.5 billion in savings over the next two years. These are staggering numbers. And while the cynic in me can’t ignore that this announcement was made at what is, effectively, a sales conference – especially one that coincided with the launch of IBM’s AI Agent Store – the scale of those numbers deserves attention.

We are standing at the threshold of one of the most transformative technological shifts in modern enterprise history. AI is no longer on the horizon – it’s here, it’s powerful, and it’s already reshaping the way businesses think about productivity, creativity, and competitive advantage. OpenAI’s recent report, ‘AI in the Enterprise‘, offers a concise and thoughtful roadmap for leaders seeking to implement AI within their organizations.

Let’s be honest, most AI (Artificial Intelligence) tools today are relatively smart but not always super useful. You ask them to explain something, write an email, maybe generate some code, but the moment you want them to do something real, like schedule a meeting, file a bug report, or move stuff across your tools, they just stare blankly. That’s where MCP comes in. MCP stands for Model Context Protocol.

As AI adoption increases, the race to real, durable AI value intensifies. Almost every organization that can use AI is using AI — but one of the most troubling trends I’ve observed revolves around talent. Right now, most organizations use AI to increase internal efficiencies — do quick research, write quick emails, start a new project at the 50% mark rather than the 0% mark, etc. But some executives I’ve talked to are taking a more aggressive approach.

In cybersecurity, speed, clarity, and cost control are everything—and AI has the potential to deliver all three. But only if it’s done right. At Observo AI, we use machine learning to eliminate low-value data, reduce alert fatigue, and surface the insights that matter most—all while cutting data volume and storage costs by up to 80%. But for many teams, one critical question still lingers: Can we trust what AI is doing with our data?