This has direct implications for portfolio management and funding. A backlog framed only through closure rates or ticket age will usually lose out to visible transformation work. Yet recurring incidents, emergency replacement spending, and extended support contracts are all signals that maintenance debt is already consuming budget and capacity. CIOs should require a service-level view of exposure: which business capabilities are affected, what the likely operational or financial consequence is, and whether remediation, containment, or formal risk acceptance is the best use of scarce investment.
There is also an important control question around AI-enabled operations. Automation can improve triage and execution speed, but it also accelerates the impact of poor asset ownership, missing dependency maps, and weak change history. Leaders should resist treating AI as a productivity layer on top of unmanaged complexity. Instead, use it selectively where approvals, exception handling, and rollback accountability are clear.
Practical next questions for the CIO office are straightforward:
- Which backlog items expose revenue, compliance, safety, or customer-facing services?
- Who is authorized to accept temporary operational risk, and for how long?
- What percentage of incidents trace back to known but deferred maintenance conditions?
- Where is AI being introduced before underlying service data and governance are reliable enough?
The most damaging IT failures rarely begin when a system stops working. They More often than not, they begin months earlier, when a warning is dismissed, a replacement is postponed, or a temporary exception quietly slides into permanencebecomes permanent.
The damage isn’t obvious at first. Teams repeatedly resolve incidents connected to the same aging system, but they treat each ticket as a separate event. The tickets are closed, so the reporting looks healthy. Yet Tthe underlying risk continues to grow.
In the Uptime Institute’s 2025 Global Data Center Survey, 57% of the 1,677 respondents said their most recent major outage cost more than $100,000, while one in five reported costs above $1 million in both the 2025 and 2026 surveys. Not every outage begins with deferred maintenance, but unresolved operational conditions can easily become material exposure.
The backlog is bigger than patching
By maintenance backlog, I do not mean only overdue patches, aging hardware or open tickets;. I mean the accumulated work required to keep systems secure, supportable, understood and fit for the business processes that depend on them.
The less visible backlog includes ownerless devices and integrations, unsupported applications performing critical work, undocumented configuration changes, recurring incidents that are never eliminated, unused licenses, forgotten accounts and temporary workarounds that became standard procedure.
Together, these conditions create a widening gap between how leaders believe the technology environment operates and how it actually operates. Maintenance work is rarely connected clearly enough to its business consequences clearly enough.
Deferred work compounds the issues
Deferred maintenance does not remain isolated within an IT queue. Aging or poorly understood technology can create recurring incidents, slower troubleshooting and downtime. Monitoring may identify unsupported devices, unpatched software and forgotten accounts, but it cannot resolve them.
Deferred work can also lead to unused licenses, emergency replacements and continued spending on systems that should have been retired. CIOs may then make lifecycle decisions using incomplete information, worsening situations they are trying to fix.
These problems compound. Missing ownership delays patching, while . Iincomplete history slows diagnosis. Delayed replacement creates more incidents, . Ras repeated workarounds make systems harder to change. Poor records weaken future automation.
Each postponed decision increases the cost and uncertainty of the next one.
AI cannot solve maintenance debt
This is before you introduce AI. AI is becoming part of how IT teams triage incidents, recommend responses and automate routine actions. That But that makes neglected maintenance more consequential, not less.
Take the case of Aan AI assistant. It might summarize an incident accurately yet lack the context needed to recommend a safe response. Who owns the system? Which service depends on it? Was its configuration recently changed? Could an automated action disrupt another workflow? Without these answers, the value of the AI action is inhibited.
More broadly, Aan automated workflow may not execute at scale unless uncertainty, human oversight and approval requirements are built into its controls. The National Institute of Standards and Technology treats context, data and human oversight as dimensions of AI risk. The NIST Generative AI Profile also identifies automation bias — excessive deference to automated outputs — as a risk.
This means Aautomation does cannot erase maintenance debt. Instead, Iit increases the speed at which incomplete records, unclear ownership and neglected dependencies can produce consequences.
Ticket age is not the same as business risk
Most maintenance reporting measures activity, such as open tasks, average backlog age, patch completion rates, tickets closed or assets refreshed. Those metrics do not show exposure.
An old, low-impact task may carry little immediate risk; . Aa newly identified, unsupported system connected to a critical process may be far more urgent. CIOs should ask which service depends on it, what happens if it fails, and whether someone has accepted the risk.
Maintenance should be prioritized by consequence, not simply by age.
CIOs need to manage the backlog as a portfolio of business risk, not merely as a queue of tasks. Four actions can make that shift real.
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Connect work to business dependencies. Link significant maintenance items to the employees, locations, systems and services that rely on the affected technology.
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Separate deferred work from accepted risk. Record why the work was postponed, what could result, who accepted the exposure and when the decision will be reviewed.
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Identify recurring conditions. Look beyond individual tickets to identify devices that are repeatedly affected, postponed patches, extended support agreements and workarounds that have become standard procedure.
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Report exposure, not just completion. Show exposed business services, critical unsupported technology, recurring incidents, ownerless decisions and the likely cost of further delay.
The objective is not to escalate every maintenance task, but. It is to ensure that material exposure receives an explicit business decision.
No CIO has unlimited staff, funding or maintenance windows. No organization will eliminate every item from its backlog. But every organization should know what that backlog puts at risk.
The priority is to keep deferred work from becoming invisible, ownerless and disconnected from its business consequences. Once the backlog is treated as a risk portfolio, the question changes from “How many tasks did IT close?” to “Which risks are we carrying, why, and who agreed to carry them ?”
Note here, encouraging the reader to emailHow do you approach your maintenance backlog and has AI affected the way you handle IT tickets?. Let us know at [email protected].
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