Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

More automation. More responsibility. The IT role isn't shrinking — it's shifting
Learn more about SolarWinds

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds.

Compared to two years prior, IT pros see their roles as:

  • 52% more strategic
  • 52% more automation-driven
  • 47% more cross-functional
  • 41% more complex

A clear sign of shifting responsibilities is how IT pros are spending their time — more on proactive strategy, tool management, and issue prevention, and less on traditional tasks like incident response. This signals a higher bar for today's IT roles: while AI streamlines workflows, it doesn't reduce workloads — it shifts them toward more complex, strategic work.

AI Effect on IT Roles

The findings show that 81% of IT pros agree that AI is changing how teams work more than how much they work. Also, 71% say AI has made their role more demanding. In addition, AI has added new responsibilities for IT pros, including:

  • Interpreting data and AI-driven insights: 59%
  • Designing intelligent AI-driven workflows: 56%
  • Evaluating and validating AI outputs: 47%

While IT pros highlighted benefits thanks to AI — such as reducing manual effort (65%) and faster root cause analysis (61%) — they also pointed to areas where AI introduces friction, including:

  • Needing to “double-check" AI outputs (71%)
  • Difficulty trusting recommendations (62%)

The upside is clear. So is the unlock: the teams getting the most from AI aren't just adopting it — they're governing it. If teams can address these hurdles, they will be able to further lean into the many positive ways AI is reshaping IT workloads.

"AI is not making IT simpler — it's making it more consequential," said Krishna Sai, Chief Technology Officer at SolarWinds. “The teams thriving in this environment are not usually the ones with the most AI tools. Instead, those who are building the governance and structure to actually trust them are seeing the greatest results. That's what organizations need to get right: not only deploying AI, but also creating the conditions where it can deliver."

What the Data Says IT Leaders Need to Do Next

Make training structural, not optional

61% of frontline managers say formal training is the most critical element for building AI skills — yet fewer than 4 in 10 C-suite leaders agree. As AI moves from assistive to agentic, understanding when to trust it, override it, and govern it is a new skill set that doesn't develop through exposure alone.

Build governance before you need it

Organizations are deploying AI faster than they're defining the rules around it — and the data shows it. An "AI by Design" approach, with clear policies on where AI operates autonomously and where human oversight is required, is what makes adoption sustainable.

Treat infrastructure consolidation as an AI prerequisite

83% of respondents agree AI is only as effective as the data it can see — yet 67% report at least moderate fragmentation in their IT environments. Unified visibility across cloud, on-premises, and hybrid infrastructure isn't just an operational upgrade; it's the foundation that determines what AI can actually deliver.

The data points to a clear need: IT teams don't just need more AI; they need AI that works together, that can be trusted, and that makes the orchestrator's job manageable.

Methodology: In partnership with UserEvidence, SolarWinds surveyed more than 1,000 professionals across IT operations, IT service management, leadership, application and platform engineering, security, and network operations.

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Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

More automation. More responsibility. The IT role isn't shrinking — it's shifting
Learn more about SolarWinds

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds.

Compared to two years prior, IT pros see their roles as:

  • 52% more strategic
  • 52% more automation-driven
  • 47% more cross-functional
  • 41% more complex

A clear sign of shifting responsibilities is how IT pros are spending their time — more on proactive strategy, tool management, and issue prevention, and less on traditional tasks like incident response. This signals a higher bar for today's IT roles: while AI streamlines workflows, it doesn't reduce workloads — it shifts them toward more complex, strategic work.

AI Effect on IT Roles

The findings show that 81% of IT pros agree that AI is changing how teams work more than how much they work. Also, 71% say AI has made their role more demanding. In addition, AI has added new responsibilities for IT pros, including:

  • Interpreting data and AI-driven insights: 59%
  • Designing intelligent AI-driven workflows: 56%
  • Evaluating and validating AI outputs: 47%

While IT pros highlighted benefits thanks to AI — such as reducing manual effort (65%) and faster root cause analysis (61%) — they also pointed to areas where AI introduces friction, including:

  • Needing to “double-check" AI outputs (71%)
  • Difficulty trusting recommendations (62%)

The upside is clear. So is the unlock: the teams getting the most from AI aren't just adopting it — they're governing it. If teams can address these hurdles, they will be able to further lean into the many positive ways AI is reshaping IT workloads.

"AI is not making IT simpler — it's making it more consequential," said Krishna Sai, Chief Technology Officer at SolarWinds. “The teams thriving in this environment are not usually the ones with the most AI tools. Instead, those who are building the governance and structure to actually trust them are seeing the greatest results. That's what organizations need to get right: not only deploying AI, but also creating the conditions where it can deliver."

What the Data Says IT Leaders Need to Do Next

Make training structural, not optional

61% of frontline managers say formal training is the most critical element for building AI skills — yet fewer than 4 in 10 C-suite leaders agree. As AI moves from assistive to agentic, understanding when to trust it, override it, and govern it is a new skill set that doesn't develop through exposure alone.

Build governance before you need it

Organizations are deploying AI faster than they're defining the rules around it — and the data shows it. An "AI by Design" approach, with clear policies on where AI operates autonomously and where human oversight is required, is what makes adoption sustainable.

Treat infrastructure consolidation as an AI prerequisite

83% of respondents agree AI is only as effective as the data it can see — yet 67% report at least moderate fragmentation in their IT environments. Unified visibility across cloud, on-premises, and hybrid infrastructure isn't just an operational upgrade; it's the foundation that determines what AI can actually deliver.

The data points to a clear need: IT teams don't just need more AI; they need AI that works together, that can be trusted, and that makes the orchestrator's job manageable.

Methodology: In partnership with UserEvidence, SolarWinds surveyed more than 1,000 professionals across IT operations, IT service management, leadership, application and platform engineering, security, and network operations.

Related Links

Hot Topics

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...