Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...
If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...
In a 2026 survey conducted by Liquibase, the research found that 96.5% of organizations reported at least one AI or LLM interaction with their production databases, often through analytics and reporting, training pipelines, internal copilots, and AI generated SQL. Only a small fraction reported no interaction at all. That means the database is no longer a downstream system that AI "might" reach later. AI is already there ...
For years, many infrastructure teams treated the edge as a deployment variation. It was seen as the same cloud model, only stretched outward: more devices, more gateways, more locations and a little more latency. That assumption is proving costly. The edge is not just another place to run workloads. It is a fundamentally different operating condition ...
AI can't fix broken data. CIOs who modernize revenue data governance unlock predictable growth-those who don't risk millions in failed AI investments. For decades, CIOs kept the lights on. Revenue was someone else's problem, owned by sales, led by the CRO, measured by finance. Those days are behind us ...
Over the past few years, organizations have made enormous strides in enabling remote and hybrid work. But the foundational technologies powering today's digital workplace were never designed for the volume, velocity, and complexity that is coming next. By 2026 and beyond, three forces — 5G, the metaverse, and edge AI — will fundamentally reshape how people connect, collaborate, and access enterprise resources ... The businesses that begin preparing now will gain a competitive head start. Those that wait will find themselves trying to secure environments that have already outgrown their architecture ...
Ask where enterprise AI is making its most decisive impact, and the answer might surprise you: not marketing, not finance, not customer experience. It's IT. Across three years of industry research conducted by Digitate, one constant holds true is that IT is both the testing ground and the proving ground for enterprise AI. Last year, that position only strengthened ...
Every enterprise technology conversation right now circles back to AI agents. And for once, the excitement isn't running too far ahead of reality. According to a Zapier survey of over 500 enterprise leaders, 72% of enterprises are already using or testing AI agents, and 84% plan to increase their investment over the next 12 months. Those numbers are big. But they also raise a question that doesn't get asked enough: what exactly are companies doing with these agents, and are they actually getting value from them? ...
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 ...