The Onug Blog
Enterprise Cloud 2.0 Technology Solutions, Strategy and Use Cases
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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…
Enterprise AI adoption is moving faster than traditional technology planning cycles can support. New tools appear constantly, technology planning cycles are shrinking, and organizations are under pressure to deliver measurable value without compromising security, reliability, or governance. At the AI Networking Summit 2026 in Dallas, John Feldmeier, Vice President of Site Engineering and Information Technology at eBay, shared how the company is building an AI-enabled workplace while embedding governance from the beginning. The biggest takeaway: scaling enterprise AI is not primarily about deploying more tools….
Enterprise AI adoption is accelerating, but moving quickly is only part of the challenge. Organizations must also protect sensitive data, manage access, maintain visibility, and ensure new AI systems can operate securely at scale. During the AI Networking Summit 2026 in Frisco | Dallas, FedEx Freight leaders Michael Milligan and Prashanth Karne shared how the company is approaching this balance. Their central message: responsible AI governance should not slow innovation. Done well, it creates a repeatable path for scaling enterprise AI securely and confidently. What…
The AI infrastructure market just crossed a threshold. With GPU-backed debt now underwritten at scale, the constraint on AI compute growth is no longer capital — it is creditworthy enterprise demand. Analysts project AI debt financing will become a multi-trillion-dollar credit market by the end of the decade, second only to U.S. mortgages. Every dollar of that financing rests on one thing: enterprises willing to sign multi-year compute commitments. Those enterprises are the ONUG Community. For 15 years, ONUG has been where enterprise IT leaders…
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…
The AI conversation has moved fast—but for most network and security engineers, the real work is just beginning. The AI Networking Summit in Frisco | Dallas isn’t about abstract ideas or distant futures. It’s about what happens when AI hits production environments, and what that actually means for the people responsible for keeping systems running, secure, and performant. Over the past two years, the Summit has grown rapidly—attendance alone has increased by nearly 90% — and that growth reflects something important: infrastructure teams are realizing that AI…
The work of enterprise IT operations to keep applications and services resilient and available has not fundamentally changed in the past 20 years. The escalation chains, the L1 teams, the bridge calls, the offshore outsourcing, all of it was built on a simple premise: when your tools and automation hit their limits, you throw people at the problem. It was the only option available until now. For decades, the work that overwhelmed IT operations wasn’t technically complex; it was relentless and context-dependent. Triaging ambiguous alerts,…
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…
