Operations | Monitoring | ITSM | DevOps | Cloud

Deployments are one of the few moments where software development still feels risky. Teams may have tests, a staging environment, and careful review processes, yet the final step still carries uncertainty. Will this change behave the same way in production? Will it interact cleanly with existing data, traffic, and infrastructure? Will it introduce regressions no one anticipated? Preview environments exist to reduce that uncertainty.

Here’s the uncomfortable truth: most companies do not have an AI problem. They have a delivery problem wearing an AI costume. MIT’s Project NANDA research has been widely cited for a brutal headline statistic: roughly 95% of corporate generative AI pilots fail to produce measurable business impact or returns, while only about 5% break through to meaningful outcomes. (Yahoo Finance) The models are impressive. The demos are dazzling. The budgets are real.

AI is the new frontier for FinOps maturity. It introduces fresh spend patterns and new opportunities for value. As GPUs, inference, and retraining reshape costs, FinOps maturity grows through visibility, forecasting, and shared mindset about how these workloads drive business impact. In this 2025 post, I gave my guidelines for implementing AI tagging to give business context and clarity to vague AI invoices. Now, I’m sharing the next level up: how to drive FinOps in AI with AI.

When Anthony Leung talks about FinOps, he’s speaking from operating at real scale — not theory. As VP of Engineering Platforms and Security Research at Forcepoint, he led a transformation that cut cloud spend in half while improving availability, and built a culture where engineers own their economics.

When I joined Kubex last year, the company was already well aware of the growing power of Large Language Models. As a company focused on intelligent resource optimization for Kubernetes, GPUs, and cloud infrastructure, generative AI didn’t feel like a threat so much as a natural extension of where the industry was heading. Kubex had already invested heavily in machine learning, but it was becoming clear that foundation models could unlock an entirely new class of capabilities for our customers.

Most IT teams don’t lack AI policies. They lack policies that survive a Git push. In many organizations, AI governance is a paper tiger. There are comprehensive documents outlining data usage, approved models, and risk management. On an auditor's desk, these policies look complete. But inside the workflow, the reality is different. AI tools are being embedded directly into IDEs, CI pipelines, and internal automation scripts.

AI agents are quickly shifting from experimentation into day-to-day operations. That shift is showing up in the data. McKinsey’s latest State of AI research highlights both broader AI use and the growing focus on “agentic AI,” even as many organizations still struggle to scale safely. For mid-market IT teams, agents can feel like the unlock: automate repetitive workflows, reduce backlog pressure, and deliver more output without expanding headcount.

Tags have long been the backbone of cloud cost visibility and governance. They help teams understand who owns what, where spend comes from, and how infrastructure maps back to the value the business delivers. However, AI workloads have altered that model, and exposed the limitations of traditional AI tags in the process. In fact, many of the most expensive AI operations don’t run on taggable cloud resources at all.

You may have heard that keeping an existing customer is five times cheaper than acquiring a new one. But that isn’t always true. “Hidden costs” often accompany customer retention, loyalty, and increasing “share of customer”. Could you be spending more on customer retention than on winning new customers? This quick guide will walk you through the meaning of Customer Retention Cost (CRC), why it’s important to calculate it, and how to calculate it.