I am pleased to announce that we have released Avantra 25, the next evolution of the Avantra platform. This year we have focused on all things cloud, from native support of SAP BTP and SAP S/4HANA Public Cloud Edition to SAP RISE capable automation templates in our add-in library and our very own Avantra AIR cloud-based AI extension for Avantra, there’s a lot to like with Avantra 25. There are some great new features though so let’s dig deeper.

Managing modern IT environments is becoming more complex and fragmented as organizations rely on a broader range of applications and services, including cloud, hybrid infrastructure, microservices, and legacy systems. This complexity and velocity surpass human capacity and old processes, making it challenging for IT teams to respond efficiently to incidents.

The handoff between IT operations (ITOps) and incident management is often chaotic. NOC operators receive an overwhelming deluge of noisy low-priority alerts, which prevents them from detecting actionable, important alerts. This delay causes tickets to pile up, SLAs breached, and unnecessary assignments and escalations to L2 and L3 engineers. Concurrently, L1 analysts react to user-initiated tickets with little to zero context, forcing them to escalate the issues.

Managing modern, complex IT systems manually is like navigating a vast, ever-changing city without a map, GPS, or road signs. Imagine driving blindfolded, relying solely on occasional radio updates (logs) about traffic jams (issues) and detours (system changes). Not only does this method slow you down, but by the time you react to a traffic jam, it’s already caused gridlock across the city. Worse, competitors zipping through the city with autonomous cars (AIOps) are faster and more efficient.

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.

Several of our recent blog posts have introduced the characteristics of each phase of the Autonomic IT maturity model, from Siloed IT to Coordinated IT (an essential foundation for Autonomic IT) and the transition to Machine-Assisted IT and AI-Advised IT. We explored how you can identify where your organization stands on this transformative journey, why you might not be as far along as you believe, and what is needed to advance your journey. Now we arrive at IT nirvana: Phase 5, Autonomic IT.

We often hear about the many benefits AIOps (Artificial Intelligence for IT Operations) brings to businesses. But how can you develop an effective AIOps strategy? Where do you even start? What are the best practices or implementation challenges? These and many more questions must be answered before beginning your AIOps journey. In this guide, we will explore the steps for creating an effective AIOps strategy and discuss crucial components, obstacles, and best practices for successful implementation.

For those of us managing the ever-evolving IT infrastructure, the days of simple cause-and-effect relationships are long gone. A performance dip in one application might affect microservices, destabilizing the systems. Alerts – flood in, logs – pile up, and even the most sophisticated monitoring dashboards often leave asking: Where do we even begin?

2024 has been a momentous year for ScienceLogic. In May, we launched our vision of Autonomic IT, which combines data, AI, and automation to create a fully autonomous state of IT operations. We followed that up in July with the introduction of Skylar AI, a suite of services that empower organizations to automate ITOps processes, reach AIOps maturity, and make more accurate and data-driven decisions. A lot of hard work went into the creation of these solutions.