Many organizations still rely on reactive availability models, taking action only after an outage occurs. However, as applications become more complex, this approach often leads to delayed detection, prolonged disruption, and incomplete recovery. Monitoring is evolving from a basic operational function into a foundational capability for sustaining availability in modern environments ...

It's the paradox at the heart of enterprise IT in 2026: teams secure a record-level budget, but instead of immediately innovating and evolving with new tools and seeing results, decision-making has slowed to a crawl. They're flying blind, unable to connect massive investments in areas like AI and cloud to the one thing the business actually cares about: value ...

Organizations are discovering that AI performance reflects the health of their core systems as pilots move into production ... Most stall in the early stages, not because of model limitations, but because their operational foundation isn't ready to support the next level. Lucid's AI Readiness Report found that only 26% of organizations that have implemented AI agents say those efforts have been "completely successful," a clear sign that something beneath the surface is holding teams back ...

Network hardware vendors are raising prices again — and enterprises are feeling it at renewal and refresh time ... Here's the reality: the buy-rack-depreciate cycle is no longer the only way to build a world-class enterprise network — and this isn't a one-off. It's sustained upward pressure across the hardware stack ...

Having more observability data doesn't guarantee better insight. Without a refined alerting strategy, more data means more noise. The teams that sleep well at night aren't the ones with the most dashboards; they're the ones with the clearest alerting logic. Here is exactly how the best ones do it ...

In 2026, AI-native automation is fundamentally reshaping telemetry pipeline management. As a result, around 80% of configuration tasks currently hand-built by enterprise teams, whether they tackle security or pull insights from observability, will be automated. This transforms the roles of those teams from builders to strategic drivers. The acceleration of this shift was made possible by the alignment of several elements, namely the convergence in the standardization of OpenTelemetry, rapidly maturing AI, increasing competition between platform choices, and economic pressure ...

AI agents are starting to do something that used to be slow by design. They are creating databases, spinning up branches, and iterating on the data layer as part of the build loop. You can argue about the exact percentages in any one report, but the direction is unmistakable. The database is moving from foundational infrastructure to active surface area for modern applications, and that shift is going to collide with how most enterprises still control change ...

Enterprise modernization is rarely blocked by a lack of ambition. Most organizations want faster releases, real-time data sharing, more automation, and better customer experiences. The problem is that modernization runs straight through the integration layer, where APIs, middleware, data pipelines, event streams, and third-party connections multiply faster than anyone can govern them. The challenge isn't scale alone, but the lack of end-to-end visibility and control at the level where business-critical flows actually move ...

As enterprise networks get more complex, encompassing on-prem, cloud and hybrid systems and applications, network automation is no longer optional. It's critical for uptime, security and scale. Yet persistent misconceptions about increasingly capable network automation platforms among the very NetOps professionals who would benefit the most from using them are holding back adoption. Here are 5 of the most common of those misconceptions, and why NetOps teams might want to re-think them ...

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done ...

2025 was the year everybody finally saw the cracks in the foundation. If you were running production workloads, you probably lived through at least one outage you could not explain to your executives without pulling up a diagram and a whiteboard ...

Data has never been more central to a greater portion of enterprise operations than it is today. From software development to marketing strategy, data has become an essential component for success. But as data use cases multiply, so too does the diversity of the data itself. This shift is pushing organizations toward increasingly complex data infrastructure ...

For most of the cloud era, site reliability engineers (SREs) were measured by their ability to protect availability, maintain performance, and reduce the operational risk of change. Cost management was someone else's responsibility, typically finance, procurement, or a dedicated FinOps team. That separation of duties made sense when infrastructure was relatively static and cloud bills grew in predictable ways. But modern cloud-native systems don't behave that way ...

Every business today depends on real-time connectivity — for meetings, cloud apps, customer transactions, and increasingly, AI-driven workloads. Yet one of the most common reasons performance feels inconsistent has nothing to do with servers or software. It's packet loss — the silent destroyer of digital experience ...