Behind every overextended IT department are pressures pushing outdated systems beyond their limits. Today’s ticket volume, end-user expectations, and workforce turnover have all outpaced what traditional IT service management (ITSM) platforms and people structures were designed to handle. While the operating environment has changed dramatically over the past decade, most ITSM functions have remained fundamentally the same, creating a growing gap between demand and capability. It’s a big opportunity for digital workers.
How AI and digital workers help ITSM teams do more with less
Historically, extracting value from ITSM platforms has been expensive. The setup required for successful utilization has often depended on experts, who are in demand (and difficult to place in full-time roles) or bill by the hour (and require many hours). But artificial intelligence (AI) is now moving upstream into the builder layer of ITSM, which changes how ITSM environments are built, how risk is managed, and how value is realized over time.
For CIOs and ITSM leaders, I believe the emerging mandate is to leverage this technology to be more productive with fewer resources, achieve better organization-wide outcomes, and reduce risk.
1. Use fewer resources: scale ITSM without scaling headcount
ITSM teams are being asked to deliver more – more automation, more integrations, more responsiveness, more end-users – without a corresponding increase in resources, or, in some cases, with fewer resources. The paradox is that as the system evolves, variability and costs increase as well. Traditional ITSM doesn’t scale well without more people or more budget.
Yet in many organizations, hiring has slowed or stopped altogether. According to a CompTIA labor report, tech hiring has remained flat due to broad uncertainty. Even when budget is available, experienced architects and administrators can be hard to attract and even harder to keep because builder work is long and repetitive. While some organizations may be willing to invest in training, the attention to detail required for success can’t always be taught, so employees still churn. Furthermore, both workforce development and system improvements are often deprioritized out of necessity to keep up with ticket volume.
The data from the same CompTIA report confirms that employers are increasingly citing AI as a skill requirement, up 111% from the same period last year. This underscores the very real expectation placed on teams to use AI as a capacity multiplier.
AI-driven automation offers a compelling solution for ITSM workers to adapt to the new operating reality of “doing more with less” and scale their platforms without adding headcount. Digital workers, designed by an AI consultancy specifically for a given ITSM environment, reduce the volume of manual configuration work required to evolve the platform. At its full potential, this means:
- Repetitive build tasks that consume development or consulting time are automated
- Reduced backlog for workflow creation and updates
- Digital workers can continuously analyze ticket data to identify recurring issues
- Digital workers find gaps, and recommend and generate knowledge articles
- Faster implementation and time to value
- Higher platform utilization
- Reduced reliance on external consultants
- More predictable, controlled costs
- Institutional knowledge is retained even when there is ITSM team turnover.
Most significant is the inherent shift in the nature of ITSM work. The improvement in AI capabilities allows teams to shift their focus from repetitive building and maintenance work to making more strategic design improvements to the system.
2. Achieve better outcomes: go from reactive maintenance to self‑healing systems at a lower cost
It’s no secret that slow IT resolution times directly fuel employee frustration. VentureBeat reported that 91% of employees are frustrated with workplace tech, with top complaints being:
- Increased stress (49%)
- Extended IT response times (34%)
- Lack of collaboration between departments (30%)
- Missing important features/capabilities (28%)
- Lack of automation (25%).
These employee frustrations feed into the reactive nature of ITSM, creating a constant flywheel of stress. Burnout and turnover follow, leading to poor business performance outcomes. When it takes several hours to address issues or several workdays to resolve more complex ones, IT productivity plummets and meaningful system improvements don’t get done because IT must react to tickets and maintenance issues.
AI is making a difference – it’s reported that organizations that do not use AI have longer resolution times (30+ hours) compared to those that do (under 15 hours), easing the burden on both employees and IT teams.
AI also helps rein in the hidden costs of enterprise ITSM. Implementation and maintenance often exceed 1–3x the initial software license, with long-term costs reaching into the tens of millions. Digital workers can take repetitive tasks off your team’s plate to make it easier and more cost-effective to make changes. They can also identify and resolve issues early (before they escalate), leading to a more efficient, self-healing system that allows teams to focus on higher-value work.
3. Reduce risk: protect against knowledge loss
If capacity is one side of the equation, risk is the other. Knowledge loss is a huge risk factor for ITSM leaders. Every IT leader knows the stomach-dropping moment when a key ITSM architect or administrator leaves the company. Suddenly, no one knows why or how certain workflows were built the way that they are, custom logic problems are untouchable, and upgrades feel risky. Given that tech roles have a 13.2% turnover rate and the time-to-fill for IT roles is 51 days, losing a key team member is a real risk.
Turnover presents the immediate challenge of staffing and the long-term challenge of platform risk. I’ve seen too many organizations discover that their ITSM environment was effectively dependent on one or two individuals. When those individuals leave, they often take with them institutional knowledge of the system’s nuances and behavior under edge conditions. Without institutional knowledge, the system may work at the surface level but is increasingly fragile, opaque, and costly to maintain.
Even with documented processes, the inevitable gaps in institutional memory put your organization at risk. For example, one broken workflow could mean hours or days of remediation to identify the root cause and implement a fix – something that would have been simple for a former employee.
When an AI agent participates in configuration, it retains memory of how things were built and why. With this long-term memory, digital workers operating at the builder level can pinpoint failures faster than humans. Every workflow built, every catalog item configured, and every rule implemented become ingrained into a knowledge layer that does not degrade with turnover.
This has two immediate effects:
- Reduced operational risk from fewer dependencies on individual contributors and legacy knowledge
- Lower support costs from faster troubleshooting, full system understanding, and more consistent updates
Over time, the platform itself becomes more resilient. Instead of accumulating hidden complexity, it develops a permanent memory of how it was built and why and learns from it. This is a critical step toward self-healing systems, where issues are not just resolved faster but anticipated and mitigated before they escalate.
Don’t let the new ITSM AI mandate become “another project we can’t get to”
For IT teams that are already stretched thin, digital workers can make the difference between “another project we can’t get to” and a scalable, self-healing system. With the right support, your IT teams can deliver more with less. Once digital workers are up and running, they help break the cycle of growing backlogs, rising costs, and increasingly fragile systems. IT can finally focus on the work that moves the business forward without the steep cost of burnout.
Digital Worker FAQs
AI is moving into the builder layer of ITSM, automating repetitive configuration work like workflow creation and updates. This frees teams to focus on strategic design improvements rather than manual building and maintenance, and lets them scale their platforms without adding headcount.
A digital worker is an AI system built for a given ITSM environment. It cuts down the manual configuration work needed to evolve the platform, continuously analyzes ticket data to spot recurring issues, and can find gaps and generate knowledge articles.
Organizations without AI report resolution times over 30 hours, while those using AI bring that down to under 15 hours. Shorter resolution times ease the pressure on both IT teams and the employees waiting on them.
Institutional knowledge of why workflows were built a certain way often leaves with them. Custom logic becomes hard to touch, upgrades feel riskier, and a single broken workflow can take hours or days to fix instead of the time it would have taken the person who built it. With statistics showing that tech turnover is at 13.2% and IT roles taking 51 days to fill on average, it’s a real risk for ITSM leaders.
When an AI agent takes part in configuration, it keeps a record of how systems were built and why. That knowledge layer doesn’t degrade when people leave, so digital workers can pinpoint failures faster, cutting the risk that comes from relying on individual contributors and lowering support costs through faster troubleshooting.
As digital workers take on repetitive build and maintenance work, they also catch and resolve issues before they escalate. Over time, the platform builds a permanent record of how it was built and why, so issues get anticipated and addressed rather than just fixed after the fact.
Richard Mendis
Richard Mendis is the Chief Marketing and Strategy Officer at Bytemethod.ai, an AI-focused advisory and delivery firm designed to help companies navigate AI complexity and drive tangible business outcomes.
