From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure. But there's a catch: Not every network is ready to support edge deployments ...
Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...
Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...
Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...
In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI ...
The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before. But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck ...
On September 16, the world celebrated the 10th annual IT Pro Day, giving companies a chance to laud the professionals who serve as the backbone to almost every successful business across the globe. Despite the growing importance of their roles, many IT pros still work in the background and often go underappreciated ...
Artificial Intelligence (AI) is reshaping observability, and observability is becoming essential for AI. This is a two-way relationship that is increasingly relevant as enterprises scale generative AI ... This dual role makes AI and observability inseparable. In this blog, I cover more details of each side ...
Artificial intelligence is moving from hype to action, but not all AI is created equal. The current wave of interest primarily focuses on large language models (LLMs) and generative AI tools that create content, summarize data, and automate human workflows. Useful as they are, these systems still depend on people to guide and apply them. Instead of waiting for prompts, agentic AI can decide on a course of action, connect with other systems, and carry out tasks on its own. That level of independence is already drawing interest from attackers, who are testing ways to use automation and adaptation to gain an edge in cyberattacks ...
A recent MIT study reveals that 95% of enterprise AI solutions fail, with 85% of AI project failures attributed to data readiness issues ... The reality is stark: AI effectiveness depends primarily on data quality, and organizations consistently struggle with data discovery, access, quality, structure, readiness, security, and governance. These challenges demand expert solutions, yet they often receive less attention than the flashy "AI will change everything" narratives that dominate industry discourse ...
Collaboration tools have become the backbone of modern business ... Yet despite this central role, collaboration performance remains one of the most poorly monitored aspects of enterprise IT. The issue isn't a lack of investment in tooling. Most organizations have performance dashboards, application uptime metrics, and usage analytics. What they often lack is insight into the actual experience users have when trying to collaborate in real time ...
Application Performance Monitoring (APM) has long been the cornerstone of system reliability ... However, the landscape has evolved ... The question is no longer whether APM is important. The question is: What does observability need to become to support this new era? ...
AI can be a critical part of the IT puzzle by helping to accelerate incident response, reduce downtime and keep customers happy. These gains can shift executive attitudes, as leaders come to see AI agents not just as experimental tools, but as reliable partners in mission-critical situations. It's no surprise, then, that 81% of IT and business executives now trust AI agents to take action during a crisis ...
Every week, a new AI tool claims to reinvent the enterprise. But beneath the hype, many enterprises are grappling with a sobering reality: the GenAI solutions they've deployed are falling far short of expectations ...
New Relic's 2025 Observability Forecast ... found that with a median annual cost of high-impact IT outages reaching $76 million, organizations are investing in AI-strengthened observability to detect and resolve issues faster. Here are 5 key takeaways from this year's report ...
Artificial intelligence is transforming network operations (NetOps), supercharging automation, enabling new predictive capabilities, improving visibility and powering nearly continuous optimization ... When trained on live network data and large libraries of validated automations, GenAI and agentic AI can help NetOps teams be more productive while making networks more reliable, easier to manage and secure. And as agentic AI becomes more powerful, AI's overall usefulness to the NetOps team will increase exponentially ...
For years, the tech industry has treated cloud infrastructure as a destination. Shift the infrastructure to the cloud, win the game. The rise of AWS, GCP, and Azure cemented that belief — shift the infrastructure and let hyperscalers handle the rest. But, in the last year or two, this infrastructure-centered view has started to change. The explosion of AI workloads, the mainstreaming of edge computing, and a wave of developer tooling startups have exposed a new truth: infrastructure is no longer the battlefield. It's the starting point. The differentiator isn't who owns the cloud, it's who makes it usable, fast, and built for modern workloads ...
Every organization is scrambling to adopt AI, but many are missing out on one of its most transformative benefits: the ability to increase creativity within and across teams ... To carve a better path forward, organizations should approach AI rollouts in a strategic way that keeps people at the center of work. Here, I share three ways to build better rollouts that can help organizations harness AI as a creativity multiplier ...
With data, we can drive digital transformation by turning insights into action. At the heart of any meaningful digital transformation strategy lies a critical component that often gets overlooked: Embedded Business Intelligence (BI) ...
Performance issues in today's digital workplace aren't always what they seem. The traditional definition, slow load times or delayed responses, is no longer enough. In reality, what users experience as "slowness" often stems from a complex mix of overlooked bottlenecks, inconsistent access, and poorly optimized infrastructure ... Here are five performance failures that rarely show up in standard dashboards but silently drag down engagement and output across modern teams ...