The first generation of agentic AI in IT operations focused on capability — giving machines the ability to investigate, reason, recommend, and execute. The next question is more consequential: what happens when those capabilities are organized around responsibility rather than tasks? The industry has grown sophisticated at decomposing operational work across specialized agents — one investigates a signal, another assesses a change, another recommends or executes a remediation. Individually, these capabilities can be powerful; collectively, they don’t necessarily add up to an operational role. Decomposition…
Artificial intelligence has rapidly become part of everyday business operations. Employees are using AI to summarize meetings, write code, analyze data, draft emails, and automate repetitive tasks. Software vendors are also embedding AI into business software and cybersecurity tools. While AI offers enormous productivity benefits, it also introduces cybersecurity risks. Employees are interacting with AI tools that haven’t been approved by IT, uploading sensitive information into public models, or granting AI-powered applications access to corporate data. The question is no longer whether your organization should…
A significant shift has happened in supply and demand, creating challenges on multiple levels across data centers. Not only is the demand for power and floor space increasing, but there is the dramatic shift in costs driven by a massive increase in worldwide demand for silicon, particularly for memory and storage silicon, because of AI. Gartner says that $2.52 trillion will be spent on AI in 2026, growing to $3.33 trillion in 2027¹. Major hyper-scalers alone are expected to spend $650 billion on AI and…
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…
