Technology's role in the workplace has expanded rapidly, framing how we work and communicate. Now, with the explosion of new and innovative AI-driven tools, people are struggling to navigate how to work in this new emergent era. And although the majority of these applications are designed to make our lives easier, for many knowledge workers, they've become a source of stress and anxiety. "Technostress" ... describes the feelings of being overwhelmed by constant connectivity and cognitive overload from information and notifications, and it's on the rise ...
People want to be doing more engaging work, yet their day often gets overrun by addressing urgent IT tickets. But thanks to advances in AI "vibe coding," where a user describes what they want in plain English and the AI turns it into working code, IT teams can automate ticketing workflows and offload much of that work. Password resets that used to take 5 minutes per request now get resolved automatically ...
Governments and social platforms face an escalating challenge: hyperrealistic synthetic media now spreads faster than legacy moderation systems can react. From pandemic-related conspiracies to manipulated election content, disinformation has moved beyond "false text" into the realm of convincing audiovisual deception ...
Traditional monitoring often stops at uptime and server health without any integrated insights. Cross-platform observability covers not just infrastructure telemetry but also client-side behavior, distributed service interactions, and the contextual data that connects them. Emerging technologies like OpenTelemetry, eBPF, and AI-driven anomaly detection have made this vision more achievable, but only if organizations ground their observability strategy in well-defined pillars. Here are the five foundational pillars of cross-platform observability that modern engineering teams should focus on for seamless platform performance ...
For all the attention AI receives in corporate slide decks and strategic roadmaps, many businesses are struggling to translate that ambition into something that holds up at scale. At least, that's the picture that emerged from a recent Forrester study commissioned by Tines ...
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 ...