Operations | Monitoring | ITSM | DevOps | Cloud

Reliable internet connectivity isn’t a convenience. It’s mission-critical infrastructure for modern organizations. Every organization today depends on high-speed, reliable internet access for daily operations—from cloud collaboration and data transfer to streaming, remote work, and customer engagement. As digital transformation accelerates, the rise of AI, large language models (LLMs), IoT, and device sprawl has massively increased bandwidth demand and network complexity.

AI is being adopted at a faster rate than ever across the business world. According to Stanford, 78% of organizations had implemented AI in some form by 2024. And if that’s not convincing enough, 92% of companies plan to expand their AI investment over the next three years. Practically everyone, including your competitors, is already using AI to gain a competitive edge. If you don’t act soon, there's a real risk of falling behind.

Large Language Models (LLMs) are transforming how businesses interact with users, automate workflows, and deliver insights in real time. But as powerful as these models are, running them at scale comes with unique challenges, from hallucinations and latency spikes to cost overruns and user trust issues.

As organizations modernize, most applications don’t fit neatly into one category—they span both traditional three-tier architectures and cloud-native microservices. To monitor these hybrid environments effectively, teams need APM tools that can seamlessly connect the two worlds.

In 2025, microservices are everywhere and Kubernetes is the de facto standard for operating cloud native applications. But not all apps are built in microservices architectures. For most enterprises, hybrid environments are the reality, with their business run on a mix of three-tier and cloud native applications.

At this year’s.conf25, we introduced an exciting new chapter in observability at Splunk — one that is unified, AI-powered, and agentic — to ensure ITOps and engineering teams are digitally resilient in the AI era.

Every day, IT teams are flooded with alerts—thousands of messages about performance issues, service outages, or suspicious activity. With so many notifications, it’s easy to get overwhelmed, miss critical problems, or waste time chasing false alarms. Correlating related alerts into groups can help reduce the noise and make sense of everything, but setting up those correlations takes time, experience, and a lot of both system and historic knowledge.

If you are leading technology innovation in your organization, you know the relentless surge of machine data is rewriting the rules of the digital enterprise. The upside? Incredible opportunities for AI-driven transformation. The challenge? Unprecedented complexity. Today’s leaders are under enormous pressure to unify, analyze, and act on a deluge of data streams across multiple environments.

Managing data ingestion at scale is no easy task. As organizations onboard hundreds or even thousands of data sources into the Splunk platform for security, observability, and other business-critical use cases, it becomes increasingly complex to ensure data is consistently available and onboarded efficiently.