AI, Observability and the Reality of Hybrid IT: Why Infrastructure Complexity Isn’t Going Away

Hybrid IT has become the default operating model for most organizations. Not by design, but through years of incremental decisions driven by business needs. In a recent Petri Dish interview, Brad Cline, Vice President of IT Operations at SolarWinds, shared a frontline perspective on how infrastructure complexity emerged, why it continues to grow, and how AI is reshaping the way IT teams manage it.

“The thing that drew me to tech in the beginning was that it was always this constantly changing, evolving environment,” said Brad Cline, Vice President of IT Operations at SolarWinds. “You’re always going to have to learn and relearn your job, figure out how new puzzle pieces fit together, and how to apply them to make the business more effective.”

That need for constant adaptation has defined IT for decades. What’s different today, Cline argues, is the speed and scale of change, and the operational pressure it puts on IT teams.

How virtualization created hybrid sprawl

Virtualization was once seen as a major simplification. Abstracting workloads from hardware improved efficiency and made environments easier to scale. But over time, virtualization became layered with public cloud platforms, SaaS services and bespoke applications.

According to Cline, most organizations didn’t set out to build overly complex environments.

“It’s very rare to see a business that’s completely aligned across every team,” he said. “An executive or engineer comes in with experience in a particular platform, the use case makes sense, it delivers ROI. And once it’s successful, unwinding it later becomes very difficult.”

Those decisions accumulate. The result is a mix of platforms: VMware, Hyper‑V, Azure, AWS, GCP and on‑premises systems, all delivering value, but rarely conforming to a single standard. Cline describes this as structural technical debt, even when the technology itself is modern.

The hidden cost of managing multiple platforms

Hybrid environments introduce challenges that go far beyond infrastructure provisioning. Tooling, skills and operational overhead multiply quickly.

“Your monitoring tool might be perfect in one environment and not great in another,” Cline explained. “So you add another tool. Then another. And each one requires a different skill set.”

Those costs don’t always show up clearly on a balance sheet. Duplication of monitoring, security and FinOps tools, combined with the specialists required to operate them, quietly increases operational spend while slowing incident response and troubleshooting.

When everything is revenue‑critical

The real impact of complexity becomes obvious when something breaks. Especially in systems tied directly to revenue.

Cline described application stacks spanning orchestration layers, data platforms and customer‑facing services, often running across multiple clouds, data centers and geographies.

“Anytime you have revenue tied to something, it becomes a higher‑stress situation for everyone involved,” he said. “I’ve seen incidents where 30 or 40 people are pulled into a call because it’s revenue‑impacting.”

In these situations, traditional troubleshooting often leads to finger‑pointing. Each team sees its own systems operating normally, while the real problem exists in the interaction between layers.

“It’s like a hot ball of coal that people pass around,” Cline added. “Everyone’s systems look fine, so it must be someone else’s problem.”

Why AI‑driven observability changes the game

AI‑powered observability platforms are starting to break that cycle by reducing noise and correlating signals across complex environments.

“We used to manually decide that a certain amount of memory or network utilization was a problem,” Cline said. “That led to a lot of false alerts. Now, AI can learn what’s normal in your environment and trigger only when something is truly abnormal.”

Just as importantly, AI helps bridge skill gaps between teams. Engineers can ask natural‑language questions about systems they don’t deeply understand and still get meaningful insights.

“That ability to distil a very complex situation into terms that make sense to me is a huge game changer,” he said.

Automation doesn’t eliminate the human element

Despite rapid advances, Cline is clear that AI doesn’t replace experienced engineers. And it can even introduce new risks if misunderstood.

“As we simplify the front‑end interface, there’s a perception that there’s nothing technical happening behind it,” he warned. “That’s one of the biggest mistakes I’ve seen.”

Poorly governed AI deployments can increase operational and security risk, from excessive API usage to overly permissive integrations. Human expertise remains essential for architecture design, risk management and governance.

How the IT leadership role is changing

As self‑service tools and AI spread across the business, IT leadership is shifting from pure operations to strategic enablement.

“You give people a corporate card and let them run free, and you end up with 20 different apps that don’t integrate,” Cline said. “IT’s role is to guide the business. To align technology choices with real requirements.”

Rather than blocking innovation, modern IT teams help organizations adopt new tools cohesively, avoiding unnecessary duplication and shadow IT.

Bridging legacy and modern infrastructure

One of the biggest ongoing challenges, Cline noted, is ensuring legacy systems don’t get ignored simply because newer platforms offer cleaner interfaces.

“The business sees the simple interface,” he said. “But there may still be a 10‑ or 20‑person team running complex workloads behind the scenes that haven’t gone away.”

This is where hybrid observability platforms remain critical, providing unified visibility across on‑premises, cloud and legacy environments without forcing every workload into a single model.

Complexity isn’t disappearing but it can be managed

AI won’t eliminate infrastructure complexity overnight. But it can dramatically reduce alert fatigue, accelerate root‑cause analysis and help IT teams focus on what matters most.

For organizations navigating hybrid IT, the challenge isn’t choosing between old and new technologies. It’s managing both intelligently, as the pace of change continues to accelerate.