Drowning in data from complex environments? Ditch the reactive approach. Artificial intelligence for IT operations (AIOps) empowers proactive management with comprehensive observability. According to Gartner, IT spending will continue to mount sky-high despite the global economic instability; the IT expenditure is predicted to surge by 8.6% in 2024. Manual monitoring often fails to keep up with the complexity of modern IT environments, leaving critical issues undetected.

It’s great to be back at another action and innovation-packed Cisco Live 2024. Continuing our tradition of posting Cisco Live announcements and highlights (catch Cisco Live 2023 Highlights here), I am putting together my thoughts and perspective on the Top-10 Cisco Live 2024 Announcements and Highlights. This year, I also had the pleasure of representing CloudFabrix at the event, which helped gain deeper insights on customer needs and expectations on Observability, Asset Insights and AIOps.

The past few years have been dominated by digital transformation characterized by a move away from legacy on-premises systems to the cloud. However, there are also instances when bringing certain assets back from the cloud – a process known as “cloud repatriation” – can be a strategic and cost-effective move. Questions persist about when cloud repatriation makes sense and how organizations should craft their strategy.

Many organizations face increased costs from excess noise, manual workflows, and long outage times. These inefficiencies negatively impact budget, service uptime, and, ultimately, customer satisfaction. With effective use of AI, you can give operators the most relevant, full-context incident data, providing a greater understanding of an incident within seconds.

In today’s digital age, IT organizations face numerous challenges that can hinder their ability to provide seamless services. Common pain-points include frequent outages, unexplained end-user experiences, negative brand impact, unaccomplished business demands, and complex application environments. These issues are exacerbated by technology silos, an overload of alerts, inaccurate and prolonged root cause analyses, and inadequate current SRE/DevOps tools.

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.

In today’s fast-paced digital business landscape, IT service management teams face immense pressure to swiftly adapt to new technologies and meet stringent SLAs. To ensure optimal customer experiences and drive business growth, organizations need an approach that goes beyond current AIOps and semi-autonomous market offerings – they need Autonomic IT. Imagine a self-managing IT environment that monitors and optimizes technology investments as it runs.