As IT environments continue to expand in scope and complexity, understanding their impact on organizations is more important than ever. That’s why ScienceLogic commissioned research specialist Vanson Bourne to survey 400 IT operations professionals across the USA, UK, Germany, and Canada with the goal of understanding the challenges they’re facing and the technologies—including automation, AI, and generative AI—they’re using to overcome them.

Enterprises need strong observability to ensure system reliability, proactively detect and resolve issues, optimize performance, enhance security, and maintain seamless business operations across complex distributed environments.

As organizations in the public sector continue to undergo what Deloitte has called a “radical transformation” and embrace new, innovative technologies, they’re seeing improvements in everything from agility to customer experience. And, the good news is that innovation tends to breed more innovation, meaning the digital transformation of the last two decades laid the groundwork for the widespread use of artificial intelligence (AI).

Since ScienceLogic was founded in 2003, our goal has been to support our partners, including Managed Service Providers (MSPs), with solutions that help them and their clients gain unparalleled visibility into their IT environments. Our objective has always been to help these organizations bring order to complexity, turn inefficiencies into productivity, and, in the process, help service providers and the companies they serve exceed their business objectives.

Strong observability in cloud environments is essential for monitoring the health of interconnected systems. Unlike traditional monitoring, which is limited to specific cloud stacks or devices, observability provides comprehensive visibility across the entire hybrid IT infrastructure including applications, IT systems and services.

So far, we’ve detailed the Autonomic IT maturity model and discussed the characteristics of the early stages of that journey, progressing from “Siloed IT” to “Coordinated IT” and then to “Machine-Assisted IT in recent blog posts.” Wherever your organization is on this journey, there is likely still work to be done.

As AI capabilities become more powerful and expansive across IT operations and use cases, the marketplace has become crowded with contenders looking to provide solutions. But when smart enterprise buyers size up their options, ScienceLogic invariably stands out with key business differentiators that showcase why we win and stand apart from the competition.

By now, you should be no stranger to Autonomic IT. The full realization of AIOps, combining AI, data, and automation to deliver a self-healing and self-optimizing IT infrastructure that operates autonomously, continuously monitoring and optimizing technology investments, and freeing up IT resources for innovation is on the horizon. In our first blog, we discussed Phase 1 of the Autonomic IT journey: Siloed IT.

Artificial intelligence (AI) and generative AI – they may sound similar, but how do they differ? Although these terms are often used interchangeably, generative AI is actually a subtype of AI. At ScienceLogic, we use many different types of AI, from machine learning to generative AI, to analyze IT environments, provide insights to help IT pros take action, automate and streamline workflows, and drive innovation.

A typical enterprise generates a flood of information every day in the form of infrastructure and network data, operational and application data, security data, user access data, and more. With the right visualization capabilities, companies can thoroughly examine the multitudes of data they create daily to glean critical insights. The catch, however, is capturing actionable insights without exhausting the human resources of IT.