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The biggest obstacle to enterprise AI isn’t models, data science talent, or even infrastructure. It’s operations.
Across today’s enterprises, hybrid complexity has outpaced IT’s ability to manage it. Applications, workloads, runtimes, and infrastructure now span on‑premises environments, public clouds, edge locations, and air‑gapped sites. Each layer brings its own tools, vendors, and operational language. The result is friction everywhere and a widening gap between AI ambition and operational reality.
Latha Vishnubhotla, chief platform officer at Hewlett Packard Enterprise, tells The New Stack the challenges begin on Day 2.
“People can bring things up and make them functional very quickly,” says. “But where they spend most of their time is after the infrastructure becomes functional. Day 2 to Day N is where they spend a lot of time.”
That’s the problem enterprises are running into now. It’s not getting infrastructure up and running, but keeping it running, optimized, and reliable as AI workloads move from pilot to production.
Read on to dive into not only the Day 2 problem, but to learn how HPE’s GreenLake hybrid cloud management platform has grown to respond to this enterprise complexity — including that cross-platform infusion of agentic AI.
In hybrid environments, operations teams aren’t managing a single stack. They’re juggling multiple runtimes, from bare metal and VMs to containers and AI‑native platforms. Infrastructure across compute, storage, and networking often from different vendors. Workloads spread across data centers, public clouds, edge, and disconnected sites. Legacy systems that were never designed to work together
Each layer has its own management tools and telemetry. When something goes wrong, the symptom rarely appears in the same place as the root cause.
“All these different tiers are talking to each other, but it’s not linear. You have to comb through and figure out where the issue actually is.”
“All these different tiers are talking to each other, but it’s not linear,” Vishnubhotla says. “You have to comb through and figure out where the issue actually is.”
Day Zero provisioning may be fast. Day 2 operations are where complexity compounds and teams burn time reacting rather than optimizing.
AI not only raises the stakes but also delivers a solution.
Enterprises want to run more AI workloads, but data centers have finite capacity. Power, cooling, cost, and sustainability constraints are real. That’s why FinOps and GreenOps have become inseparable from infrastructure operations.
“When you want to run these workloads, you have to ask: what’s not being used?” Vishnubhotla says. “Why am I wasting here? Should I move something? Should I retire it?”
This is where traditional, human‑driven ops models start to break. There’s too much data, too many layers, and too many dependencies to reason about manually, especially at enterprise scale.
What enterprises need isn’t another point tool. It’s an operations platform that acts as connective tissue across the hybrid estate.
That’s the role GreenLake is designed to play.
GreenLake provides a unified platform experience for running and managing hybrid environments across on‑premises, private cloud, edge, and collocated infrastructure while preserving choice and control. Instead of hiding infrastructure behind abstraction, it makes it visible, observable, and operable from a single control plane.
“The control plane is actually running in the cloud,” Vishnubhotla says. “You get visibility across the entire estate.”
For organizations managing thousands of sites and tens of thousands of devices, that visibility is foundational. But visibility alone isn’t enough anymore.
The next step is agentic AI, AI systems embedded directly into the ops platform, trained on the context of specific infrastructure domains.
A networking agent understands networking. A storage agent understands storage. A compute agent understands compute. Each brings deep, domain‑specific intelligence to Day 2 operations.
“Each layer already has intelligence,” Vishnubhotla says. “If we can connect this intelligence, we can unleash very powerful outcomes.”
That’s where the idea of an agentic mesh comes in. Instead of siloed insights, AI agents share context across layers during provisioning, troubleshooting, and optimization. This shortens the time to root cause, reduces alert noise, and opens the door to predictive and, eventually, autonomous operations.
Predictive maintenance is a clear example. Rather than reacting to failures, AI can anticipate what’s likely to break, prioritize what actually matters, and help teams act before outages cascade.
Agentic operations also unlock something enterprises care deeply about: faster AI ROI.
With a shared, platform‑level view, ops teams can answer questions like:
GreenLake supports automation through copilots and MCP servers as well as UI‑driven workflows, reducing provisioning times and operational overhead. AI agents can even help predict demand and close feedback loops that used to take weeks.
“The bottleneck has always been on the ops side. Enterprises are deploying and operating infrastructure from Day Zero to Day N to unlock AI value faster.”
“The bottleneck has always been on the ops side,” Vishnubhotla says. “Enterprises are deploying and operating infrastructure from Day Zero to Day N to unlock AI value faster.”
Hybrid complexity isn’t temporary. AI pressure isn’t slowing down. And Day 2 operations are only getting harder.
That’s why the industry is converging on a clear conclusion: the answer isn’t more tools; it’s a unified, intelligent ops platform.
GreenLake brings together visibility, agentic AIOps, and cross‑domain intelligence in a platform built for how enterprises actually run today. It connects the silos, scales operations teams, and turns infrastructure from a bottleneck into an enabler.
If AI is the future of the enterprise, operations is the gatekeeper. And the ops platform powered by agentic AI is how that future gets unlocked.
