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.

AI is everywhere, and while it’s transforming industries, many organizations are still trying to identify how to use it to achieve tangible value. This is especially true for AIOps, where platforms often fall short of the promises to automate IT operations and improve incident response. As a result, many leaders are skeptical about whether AIOps can deliver measurable results quickly or provide outcome-driven value in IT operations.

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.

As pressure increases on enterprise IT teams to streamline processes and reduce downtime, many organizations are looking for new tools and strategies. Customers and stakeholders expect operational efficiency and service reliability. Tools within the AIOps industry can relieve the pressure by reducing alert noise, automating manual workflows, and reducing mean time to resolution (MTTR). However, the challenges don’t end at tool purchase.

When implementing new tech, the challenges don’t end at tool selection, purchase, and initial deployment. You can have the best technology in the world, but it won’t help your organization if no one uses it. Many teams look to AIOps solutions like BigPanda to reduce noise, improve workflows, and resolve incidents faster through AI and automation. Bringing in a new platform is part of the equation. The other part is organizational change management to support platform adoption.