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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

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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AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

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

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.

Hot Topics

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...