AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI.

This reality appears to contradict what we’ve been told about AI: use AI, eliminate manual toil. However, it's clear AI isn't entirely eliminating the workload, but reshaping how teams manage it instead. If IT leaders want to create a mature, AI-powered IT service operation, they must begin treating AI as an operating model, rather than a turnkey point solution.

The IT Strain You Can’t See

To be clear, AI is making a positive impact in ITSM. The data shows teams are saving at least three hours on detecting issues, flagging anomalies, and responding to user requests.

While the shift towards AI-assisted IT isn't generating more alerts or service tickets, it is creating an additional IT strain that can be hard to identify. Although teams are saving time on routine ticket categorization, those hours aren't put into an "hours savings account." Instead, they're being applied to model maintenance, output validation, and an eventual governance tax. The report indicates that 4 out of 5 IT professionals now spend three or more hours every week just on AI maintenance and monitoring.

This strain often materializes due to underestimating the groundwork necessary for AI to work in complex environments like IT. The operational costs are often higher than anticipated, and while it's easy to consider costs like software licenses and tokens, it's also easy to miss costs like data cleanup, staff upskilling, and model refinement as a precursor to AI model deployment. Intentional upfront investments can significantly reduce AI strain down the road.

It's Not About the Numbers

In addition to laying the right foundation for AI deployment, IT teams must reevaluate how they measure success. If improved time to resolution isn't translating to reduced strain on an IT team, that means faster ticket turnarounds can no longer be the standard. In fact, the data indicates teams leveraging an activity-based metric system reported a larger workload than those tracking outcomes.

As a result, top performing IT teams focus on  outcome-based metrics like revenue protection, customer satisfaction, and brand resilience. They gain an AI ROI advantage upstream by laying a foundation of preventative capabilities that proactively addresses issues and/or bounces back from an incident before it proliferates. This kind of AI investment provides a much bigger return that just improving performance of activity-based metrics.

The Playbook for Sustainable AI Payoff

Teams can take a few steps to change a cycle of reactivity and heavy AI workloads:

1. Begin with a clear AI vision: A focus on well-defined, high-frequency tasks is a great starting point for IT teams looking to implement AI. AI tooling can easily help with ticket triage, issue detection, and incident documentation. However, remember that this is simply a baseline for what AI in IT can do. IT will require a few more steps to build an AI-assisted culture of resilience within their teams.
2. Create as little chance for friction as possible: AI cannot simply be a universal plug-and-play across disjointed IT tooling. Teams should deploy AI tooling closer to already established service workflows, reducing maintenance overhead and avoiding integration points that will only cause confusion.
3. Fortify the data foundation: We’ve all heard it before: AI is only as good as the data it’s trained on. Poor data is often why AI fails and why IT teams are seeing large workloads even with AI in the mix. Data foundations must be embedded in company AI strategy to reduce failure rates.
4. Establish what good outcomes look like: Leave the ticket-churn metrics behind and shift from measuring activities to measuring outcomes. IT leaders must work with company leadership to draw direct lines to how AI-powered workflows are directly benefiting the company’s bottom line.  It's important to ask questions like: How many service interruptions are these workflows preventing? How is a faster mean time to resolve (MTTR) minimizing harm to brand reputation? An outcome-based measurement of AI effectiveness not only improves AI workflows and lightens IT workloads, but it also justifies investment in AI-powered IT service tooling in the future.
5. Train, train, and then train some more: AI doesn’t work if humans don’t know how to use and govern it. As a result, regularly scheduled AI upskilling and training should be mandatory across all IT service personnel. This will lead to fewer human-induced errors in AI work and a collective resilience posture prepared to step in when AI makes a mistake.

Operational Payoff Is The Standard

AI implementation is no longer a milestone for ITSM workflows, but rather the bare minimum, and it’s pushing business leaders to ask a question many IT leaders are already facing: where’s the ROI? If IT service desks are still seeing burnout, even with AI in the fold, then the ROI is either hard to see or non-existent. Organizations must be intentional with AI, creating an operational framework and organization-wide discipline to ensure AI is delivering value. Teams will only realize this value when they prioritize data cleaning, emphasize team training, and place business outcomes as the key indicator of success.