Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

The news is filled with buzz about how companies approach AI. As a result, many organizations are trying to identify how AI can effectively support their business goals. There seem to be infinite use cases, but finding those that add the most value is often the first challenge. In the ITOps environment, generative AI copilots can effectively improve team efficiency, share knowledge, and support day-to-day tasks to deliver immediate value.

ITOps and AIOps teams often face an overwhelming volume of notifications, many of which are false positives or low-priority alerts. The constant influx creates a chaotic environment. ITOps and AIOps teams can easily miss critical issues, potentially leading to system failures or prolonged downtime. Spending significant time sifting through irrelevant alerts reduces team efficiency and slows response. Focus on alert noise reduction to ensure that only meaningful and actionable alerts reach your teams.

In the weeks since the Crowdstrike outage brought millions of systems to a halt, countless articles have been written about the cause of the outage, its impact, and the costs companies incur during service disruptions. Nearly every large company had hosts offline due to the faulty update in CrowdStrike’s Falcon software. BigPanda customers were no exception. On July 19, between 04:00 and 07:00 UTC, the BigPanda systems logged an increase in shared incidents.

Rapid, proactive responses to unexpected system behavior and swift, efficient incident remediation are hallmarks of great IT teams. But the most successful NOC and incident management teams share the following: The right context gives teams visibility across systems, helps them collaborate and share knowledge, and makes every team member more efficient.

Are you ready to take your IT operations to the next level and unlock the full power of the BigPanda AIOps platform? Our engaging online learning platform empowers professionals like you with top-notch training and certification opportunities. Our carefully designed courses allow you to learn at your own pace and convenience through asynchronous learning. Whether you are a seasoned IT expert or just starting, our courses cater to all skill levels.

Improving operational efficiency and reducing the time it takes to resolve incidents are big goals. New options to customize your incident feed view in BigPanda allow you to highlight the most relevant context upfront, making a big difference. Reducing data visibility issues and redundant data can give operators greater control. The BigPanda Incident 360 Console is where ITOps teams and NOC operators receive the first notification and ongoing updates for all incidents.

Data aggregation includes systematically collecting, transforming, and summarizing raw data from multiple sources. A unified, consistent view helps IT teams analyze vast amounts of information, uncover patterns, and derive actionable insights for informed decision-making. In our case, it’s all about enhancing incident management.