IT's Motivation Crisis Is Really a Process Crisis

Phil Christianson
Xurrent

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.

When things fall apart, the work can be exhausting, but it's often empowering because there is a clear purpose. Employees are fixing something and working toward a goal. The flip side is that when nothing is actively breaking, the day fills up with routine requests, recurring issues and operational housekeeping. This is when exhaustion can set in.

When IT service teams spend too much time on repetitive tasks, motivation drains. Yet, routine tasks like ticket triage, onboarding and provisioning could be dramatically reduced through automation.

The Busywork Burden Is a Strategic Risk

The busywork burden is the repeatable work that keeps services running but rarely needs deep engineering judgment. Think ticket sorting, password resets, account unlocks, onboarding, access provisioning and constant context gathering across multiple tools.

IT has historically been built on frameworks like ITIL, with clear rules and processes. The benefit of this structure is consistency, but the drawback is that it creates repetition. The most common requests are things like, "I'm locked out of my machine, can you help me log back in?"

Modern stacks that contain monitoring and observability tools generate alerts based on thresholds and log messages. Since this information lacks context, teams often chase signals that never turn into incidents. Over time, this can create alert fatigue and divided attention while real issues hide until users report outages.

The issue is compounded by the fact that around 80% of incidents are repeats. Even when teams restore service, the root causes are often unaddressed because work that could actually improve the process gets pushed down the priority list.

This cycle creates what's called an innovation tax, and it shows up in things like delayed product launches, slower digital transformation or continually delayed modernization work.

Automation Can Improve Morale + Retention

If you're doing the same repetitive tasks every week and you're already annoyed knowing you'll have to do it again next week, you need a solution to improve morale. Automation can provide relief.

When routine work is automated, engineers get time back to spend on more meaningful problems like improving reliability, cutting down on repeat incidents or modernizing infrastructure. If people spend more of their day on work they actually care about, frustration drops and engagement rises.

The complication is that a lot of IT professionals are anxious about AI right now, and for understandable reasons. Agentic AI is being discussed constantly, and the messaging is often vague or alarmist. That quickly leads people to ask, "Is this going to take my job?"

Some of that fear comes from how AI is being described. When marketing language hints at sweeping automation without explaining what it actually means, people fill in the blanks with worst-case scenarios.

The first step in addressing this is to define what agentic AI actually means. While it automates structured workflows, it doesn't replace human judgment. Once people understand that, the conversation shifts from "Will this replace me?" to "How can I use this?", and upskilling starts to feel like an opportunity rather than a warning sign.

Practical AI Use Cases in IT Operations

Some of the most practical AI use cases in IT operations are service management and incident response.

In service management, large language models (LLMs) help resolve requests faster by organizing the information engineers need to act. Rather than reading through long ticket threads, engineers get a concise summary, key details pulled from the request and suggested routing based on similar historical tickets.

Onboarding and provisioning workflows benefit even more from automation because the steps are structured and repeatable. Generative AI drafts summaries and recommends next steps. Agentic AI builds on that by connecting LLMs to APIs. This turns approved requests into controlled sequences of provisioning actions, cuts manual handoffs and speeds up the process.

On the incident management side, AI helps teams to focus on high-priority items. Monitoring tools generate a high volume of alerts, many of which are duplicates or symptoms of the same underlying issue. AI groups related alerts, filters out noise and surfaces clusters that likely point to a single problem. This makes triage faster and more accurate.

Across these workflows, human operators still determine whether there is a real incident, decide how to resolve it and who owns the remediation. What changes is how much time teams spend gathering context and sorting through noise. With the mechanical work removed, engineers are freed up to focus on diagnosis, resolution and improvement.

AI handles the noise so engineers can focus on the work that engages them and requires their judgment. That is not a threat to the team, but rather what makes the job worth doing again.

Phil Christianson is Chief Product Officer at Xurrent

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IT's Motivation Crisis Is Really a Process Crisis

Phil Christianson
Xurrent

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.

When things fall apart, the work can be exhausting, but it's often empowering because there is a clear purpose. Employees are fixing something and working toward a goal. The flip side is that when nothing is actively breaking, the day fills up with routine requests, recurring issues and operational housekeeping. This is when exhaustion can set in.

When IT service teams spend too much time on repetitive tasks, motivation drains. Yet, routine tasks like ticket triage, onboarding and provisioning could be dramatically reduced through automation.

The Busywork Burden Is a Strategic Risk

The busywork burden is the repeatable work that keeps services running but rarely needs deep engineering judgment. Think ticket sorting, password resets, account unlocks, onboarding, access provisioning and constant context gathering across multiple tools.

IT has historically been built on frameworks like ITIL, with clear rules and processes. The benefit of this structure is consistency, but the drawback is that it creates repetition. The most common requests are things like, "I'm locked out of my machine, can you help me log back in?"

Modern stacks that contain monitoring and observability tools generate alerts based on thresholds and log messages. Since this information lacks context, teams often chase signals that never turn into incidents. Over time, this can create alert fatigue and divided attention while real issues hide until users report outages.

The issue is compounded by the fact that around 80% of incidents are repeats. Even when teams restore service, the root causes are often unaddressed because work that could actually improve the process gets pushed down the priority list.

This cycle creates what's called an innovation tax, and it shows up in things like delayed product launches, slower digital transformation or continually delayed modernization work.

Automation Can Improve Morale + Retention

If you're doing the same repetitive tasks every week and you're already annoyed knowing you'll have to do it again next week, you need a solution to improve morale. Automation can provide relief.

When routine work is automated, engineers get time back to spend on more meaningful problems like improving reliability, cutting down on repeat incidents or modernizing infrastructure. If people spend more of their day on work they actually care about, frustration drops and engagement rises.

The complication is that a lot of IT professionals are anxious about AI right now, and for understandable reasons. Agentic AI is being discussed constantly, and the messaging is often vague or alarmist. That quickly leads people to ask, "Is this going to take my job?"

Some of that fear comes from how AI is being described. When marketing language hints at sweeping automation without explaining what it actually means, people fill in the blanks with worst-case scenarios.

The first step in addressing this is to define what agentic AI actually means. While it automates structured workflows, it doesn't replace human judgment. Once people understand that, the conversation shifts from "Will this replace me?" to "How can I use this?", and upskilling starts to feel like an opportunity rather than a warning sign.

Practical AI Use Cases in IT Operations

Some of the most practical AI use cases in IT operations are service management and incident response.

In service management, large language models (LLMs) help resolve requests faster by organizing the information engineers need to act. Rather than reading through long ticket threads, engineers get a concise summary, key details pulled from the request and suggested routing based on similar historical tickets.

Onboarding and provisioning workflows benefit even more from automation because the steps are structured and repeatable. Generative AI drafts summaries and recommends next steps. Agentic AI builds on that by connecting LLMs to APIs. This turns approved requests into controlled sequences of provisioning actions, cuts manual handoffs and speeds up the process.

On the incident management side, AI helps teams to focus on high-priority items. Monitoring tools generate a high volume of alerts, many of which are duplicates or symptoms of the same underlying issue. AI groups related alerts, filters out noise and surfaces clusters that likely point to a single problem. This makes triage faster and more accurate.

Across these workflows, human operators still determine whether there is a real incident, decide how to resolve it and who owns the remediation. What changes is how much time teams spend gathering context and sorting through noise. With the mechanical work removed, engineers are freed up to focus on diagnosis, resolution and improvement.

AI handles the noise so engineers can focus on the work that engages them and requires their judgment. That is not a threat to the team, but rather what makes the job worth doing again.

Phil Christianson is Chief Product Officer at Xurrent

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 ...

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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 ...

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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 ...

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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 ...

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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 ...

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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 ...

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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 ...