As an IT leader, you’re under significant pressure to control the constant alerts. Somehow, you must manage non-stop IT alerts while also ensuring ultra-high service availability. The task is far from easy, and even the most sophisticated teams struggle to keep up and turn alerts into action with tech stacks that are constantly growing in size and complexity. IT alert management is the first line of defense.

Effective IT change management is essential for maintaining smooth operations in today’s fast-paced, agile IT environment. Given that 85%, or the vast majority, of incident-impacting alerts result from changes, optimizing your change management means improving your incident management and ensuring critical system reliability. So whether your organization uses ServiceNow for change management or is considering using ServiceNow, we’ll walk you through everything you need to know.

This blog summarizes the discussion of our recent CIO Boardroom on the theme of AI-Driven Enterprise IT in association with the CIO Association of India held at JW Marriott, Bangalore, on 17th October, 2023.

PagerDuty is an IT operations management platform and cloud computing company launched in 2009. They provide a suite of tools designed to help IT and DevOps teams detect and respond to infrastructure problems, streamline workflows, and improve operational reliability. The PagerDuty platform bridges different systems and the teams that maintain them, centralizing the detection and reporting of incidents. It allows organizations to minimize downtime and resolve issues efficiently.

In the ever-evolving world of IT, keeping an eye on application, service and system performance and addressing issues in real-time is crucial both to an organization’s customer experience, as well as its overall success. Two terms and approaches that have gained significant attention in recent years are AIOps and observability. While they both relate to improving IT monitoring and management, they serve distinct roles in enhancing operational efficiency.

Network monitoring is the process of continuously scrutinizing a computer network for failures or deficiencies to ensure availability and performance. AIOps (Artificial Intelligence for IT Operations) is an umbrella term for the integration of artificial intelligence (AI) technologies into network operations. It involves using machine learning, analytics, and big data to automate and enhance IT operations.

When it comes to observability, we’ve found that most organizations have ~20 tools installed in their IT environments. With so many tools, it’s difficult for IT leaders to gain insight into how their tools are performing and determine how much value ITOps is bringing to the organization.

In IT, the introduction of Artificial Intelligence for IT Operations (AIOps) has been nothing short of revolutionary. As networks become increasingly complex and data-driven, traditional network management methods are proving inadequate. AIOps has emerged as a critical tool in the arsenal of network managers, offering innovative solutions to manage and optimize networks in real-time.

Tool consolidation is the process of analyzing which IT observability and monitoring tools to use, which to add, and which to retire. By carefully determining the usage and value of your current observability stack, your ITOps teams can consolidate redundant tools and those providing little value to reduce your operational costs. While the benefits of tool consolidation are clear, doing so is anything but.

Choosing, deploying, maintaining, and rationalizing observability and monitoring tools can be a constant challenge for ITOps, DevOps, and SRE teams. As teams monitor increasingly complex systems, the need for instrumentation that monitors those systems grows at the same rate, leading directly to a growing problem of observability data engineering, integration, and enrichment.

The digital shift has particularly influenced the world of network management, where traditional monitoring is gradually giving way to AI operations (AIOps) solutions. It illustrates a clear shift towards automated and predictive IT operations management. While traditional systems have their place, particularly in smaller or less complex environments, AIOps represents the future of network monitoring, offering the ability to anticipate and prevent issues.

Many ITOps organizations we speak with want a state of self-healing systems capable of identifying and resolving issues without human intervention. Thanks to the progress in AI and ML, AIOps has made significant advancements in areas that automate many of the steps involved with identifying and triaging incidents. We ask ITOps leaders why they aren’t taking the next step with auto-remediating incident response workflows.

As an ITOps leader, you know managing enterprise IT can be challenging, with its mix of old and new, on-site and cloud-based systems. Closely monitoring each part of the system infrastructure and its many components is a constant struggle, forcing you and your team to juggle non-stop alerts and keep services up and running. How can you stop alert fatigue and gain clarity when alerts are incessant, unclear, and lack the necessary context? The answer lies in intelligent alerts.

In the past year, the term Large Language Models (LLMs) and its models like OpenAI’s CHATGPT, Google’s Bard, Meta’s LLAMA-2, and the open-source Falcon have buzzed throughout the tech sphere. But what exactly is an LLM, and how does it function? Let’s dive in for a clearer insight.

Tool rationalization, sometimes called tool consolidation, is the systematic analysis of observability and monitoring tools, the consideration of onboarding new tools to fill gaps, and the retirement of unnecessary tools. Perhaps you and your IT team are struggling with constantly buying new tools to meet a very niche use case to unlock new capabilities.

Information technology (IT) departments are always juggling multiple tasks in the fast-paced world of modern business, from maintaining the network infrastructure to guaranteeing data security and enhancing system performance. Even the most seasoned IT professionals can get overwhelmed by the sheer volume of data created and the rising complexity of IT systems. Artificial intelligence for IT operations (AIOps) can revolutionize the way businesses manage their IT environments in this situation.

Is your company using ServiceNow IT Operations Management or considering using it? If so, you know the importance of enhancing the visibility of your IT infrastructure and services, protecting against service disruptions, and enhancing your company’s operational flexibility. In this blog, we’ll discuss how ServiceNow ITOM works, improves visibility across the entire IT infrastructure, and streamlines operations. We’ll also discuss how ServiceNow ITOM is better together with AIOps.

If you’re juggling complex, cloud-based, containerized systems and aiming to meet high customer expectations, your old monitoring processes probably don’t cut it anymore. Increasing infrastructure complexity means you need to instrument more, log more, and monitor more. That leads to even more complexity. The answer is better observability, right? Yes and no. Observability and monitoring are critical, but they are only part of what you need for service awareness and availability.

At ScienceLogic, we’ve always believed in the power of innovation and the importance of customer satisfaction. We are excited to announce that we have been honored with the TrustRadius Best of Award in the AIOps category for 2023. This is a testament to our dedication to providing exceptional value, top-notch features, and building enduring relationships with our customers.

Imagine you’re in the middle of a critical project, and suddenly, your system crashes. Or perhaps it’s the middle of the night, and your server goes down, affecting countless users. Some IT incidents are inevitable, but the way you manage them makes all the difference in minimizing their impact. You know that proper incident management is critical – and that incidents can become costly.

AIOps for DevOps. This is a phrase that you’ll hear a lot often in the coming years. Leveraging AIOps is becoming an emerging practice for DevOps. And why not? After all, AIOps offers so many benefits for DevOps, making it a must have for every business. But what exactly are these benefits? To know more, keep reading this blog where we will discuss everything about AIOps for DevOps including its.

What is AIOps? Simply put, AIOps uses big data, analytics and machine learning to automate and improve IT operations (ITOps). AI is particularly important in ITOps functions such as anomaly detection and event correlation, as it has the ability to analyze large volumes of network and machine data to find patterns, identify the cause of existing problems and find ways to forecast and prevent future issues.

Change is the only constant in today’s continuously shifting IT landscape. Whether you’re adding new observability tools, retiring existing monitoring systems, establishing new business units, or onboarding IT systems from acquisitions, managing these non-stop changes can challenge even your expert ITOps team. Trying to get your monitoring house in order is a daunting task.