As organizations increasingly rely on complex digital infrastructure, they must be ready to move rapidly when major incidents occur. The recent global outage has shown just how fragile IT systems can be. With mounting pressure to deliver seamless customer experiences, GenAI and automation present an opportunity to manage risk more effectively, by ensuring responders have the right information to restore services quickly.

In today's fast-paced business environment, managing change effectively is crucial for organizations aiming to thrive. This is where the Change Advisory Board (CAB) comes into play, acting as the guiding force in the change management process.Imagine a team of experts who assess, evaluate, and approve changes, ensuring that everything runs smoothly while minimizing disruptions. Sounds like a superhero squad, right? Well, that’s precisely what a CAB is!

Back in the day, IT teams often spent countless business hours manually sifting through logs, diagnosing issues, and identifying the root cause of a system failure. This painstaking process frequently led to prolonged downtimes and frustrated users. Today, organizations can’t afford such inefficiencies. Keeping systems running smoothly is key, and that’s where critical metrics like Mean Time to Resolve (MTTR) come into play.

AppSignal's Node.js package now supports monitoring BullMQ jobs. In this article, we'll demonstrate how easy it is to start monitoring BullMQ performance and what insights you can expect to see for your BullMQ jobs in AppSignal.

In the last two years, generative AI offerings have exploded in the cloud space (and everywhere else). Major Cloud Service Providers (CSPs) are perfectly positioned to lead these efforts thanks to the fact that they offer the very resources they require to build these models, and their customers are naturally early adopters and heavy users of AI.

In today’s rapidly-evolving retail landscape, automation and orchestration have become pivotal in driving efficiency and innovation. The retail sector, especially during and post-COVID-19, has undergone a massive transformation. With technological advancements over the past five years surpassing those of the previous 25, retailers are leveraging automation to streamline operations and enhance customer experiences.

While the brick-and-mortar offline retail market is struggling post-pandemic, the online digital retail sector continues to experience strong growth as customers shift to online purchases. This increase in online customer activity is driving a steady increase in digital advertising spending running through the various Retail Digital Media (RDM) platforms. In 2024, ad spending in digital retail media advertising is projected to reach 136.07 billion U.S. dollars, with the bulk coming from the US market.

Monitoring the health and performance of IT infrastructure is crucial for practically all organizations to ensure the reliability, availability, and efficiency of an organization's technology environment. By continuously tracking servers, network devices, applications, and services, organizations can promptly detect and address issues before they escalate into significant problems and impact customers.

Most IT leaders realize that automating repetitive, low-level incident response actions is vital to multiple benefits. To name just a few, these include: In IT, incident response refers to addressing any event that disrupts normal service, application, security operation, or performance. Using AI and machine learning, automation addresses incident analysis, detection, investigation, triage, and response. The question is often identifying where to start or the best approach.

Observability and log management are needed for a strong IT strategy. Two essential tools for these purposes are AWS CloudWatch and OpenTelemetry. AWS Cloudwatch provides real-time data and insights into AWS-powered applications' health, performance, and efficiency. On the other hand, OpenTelemetry is an open-source observability framework that assists developers in creating, gathering, and exporting telemetry data (such as traces, metrics, and logs) for analysis.