The enterprises that win the AI era won’t be the ones with the most ambitious strategies. They’ll be the ones whose IT teams recognized early that data quality is foundational infrastructure and built accordingly. Most enterprises already know their CMDB drifts from reality. They know source-of-truth data is inconsistent across systems. This isn’t a failure of execution. It’s the predictable result of a system of record trying to track an environment that changes faster than it does. Layering AI on top of that broken foundation…
Despite massive investments in compute, most AI clusters operate at only 50–70% average GPU utilization during peak periods. This efficiency gap is rarely a failure of the silicon itself, most likely it is a failure of the infrastructure to function as a unified system. When AI workloads scale beyond a single node, the network becomes the dominant performance factor, often determining whether a cluster delivers linear scaling or collapses under communication overhead. To bridge this gap, organizations must move away from manually integrating disparate systems…
Automation and AIOps are central to modern IT infrastructure strategy, promising significant improvements in efficiency, accuracy, and velocity. Despite significant progress, many organizations struggle with serious automation lifecycle challenges, particularly across hybrid IT estates spanning cloud and on-premises infrastructure, including networks and data centers. AIOps faces an additional risk: hallucinations and trust failures. When an AI agent queries infrastructure state to make an autonomous decision, and the underlying data is stale, inconsistent, or incomplete, the model confidently acts on a false picture of reality. The…
Network operations is entering a new phase: not just “AI for alerts,” but AI that can explain, recommend, and execute workflows across your existing tooling. The reason many teams are still hesitant is equally simple: the network is the business. When AI touches telemetry, configs, tickets, and change workflows, the fears are legitimate — and ignoring them is the fastest way to stall adoption. Fear #1: “If I feed AI my network data, I’ll leak something sensitive.” The challenge isn’t just employees pasting sensitive content…
Modern networks are living ecosystems — vast, interconnected, and evolving by the second. From cloud to edge, across vendors and domains, they generate more data than traditional tools or teams can absorb. Managing this complexity requires more than automation scripts and dashboards. It demands intelligent systems that deliver answers and actions, not just alerts. That’s the idea behind IBM Network Intelligence, a new network-native AI platform designed to help organizations shift from reactive problem-solving to proactive performance assurance. Developed in collaboration with IBM Research, it…
It’s common to think of IT as its own silo, but the truth is that it’s become existential across the entire enterprise. And yet, when a flood of alerts hits the NOC, the response is often anything but resilient. It’s usually quite hectic as engineers scramble to correlate signals and manually trigger fixes. It’s a familiar story with a familiar question: why haven’t we solved this already? Many enterprises have invested heavily in automation, and while visibility sometimes improves, the ability to autonomously act on…
If you could claw back some cash from your company’s technical debt, no doubt you would. Instead of having 70 to 80% of your IT budget stuck on maintenance on aging infrastructure vs. innovation, imagine having enough budget to put towards cloud modernization and migration, GenAI, agentic AI and intelligent automation, real-time analytics or zero trust security. Now there are certainly legitimate reasons why tackling technical debt isn’t easy. You get caught up in firefighting bugs and there’s no time to improve the design or…
If you’ve gone through an MPLS to SD-WAN transition or simply operate a network with 100+ sites, you’ve likely undergone some form of “bulk” connectivity procurement in the past. Bulk procurement comes with drawbacks – complex RFP creation, tons of quoting to manage, lengthy contracting, and painfully complex installations. That said, I’ve found that IT leaders often espouse the benefits of bulk procurement. Why? The bulk discount! If you buy many circuits at once, you’ll end up with a lower price per Mbps on average…
The evolution of artificial intelligence, particularly the rise of large language models (LLMs) and deep learning, has created an immense demand for computational power primarily driven by GPUs. However, maximizing GPU efficiency requires a robust networking infrastructure that ensures minimal latency and zero packet loss. This post highlights the critical role of Data Center Bridging (DCB) with a focus on its two key components; Priority-based Flow Control (PFC) and Explicit Congestion Notification (ECN) in achieving lossless and high-performance networks suitable for large-scale GPU deployments in…
For the past decade, IT leaders have driven innovation by migrating workloads to the cloud. But now, as AI, machine learning, and high-performance computing take center stage, infrastructure needs have shifted. Compute is no longer just about flexibility and scale—it’s about raw power, speed, and efficiency. And that’s where traditional cloud models are falling short. The Challenge: Cloud Isn’t Keeping Up With AI When cloud computing became mainstream, it promised elasticity and simplicity. But that promise is starting to break under the weight of generative…
