CIOs often know that technical debt is wasting money or increasing risk, yet still struggle to win budget for reducing it. The reason is partly financial: the cost is scattered across maintenance, delays, integrations and security exposure, while AI, cybersecurity and growth programs offer more visible outcomes. The practical answer is not to treat every old system as an emergency. It is to identify the debt that is blocking a funded business goal and make its cost, risk and payoff legible to decision-makers.
Why does technical debt lose priority?
Technical debt is the accumulated cost and constraint of technology choices that make future work harder. It can include old applications, bloated code, aging hardware, unsupported systems, redundant platforms and unmanaged data dependencies. Because these assets sit across teams and budgets, their effects rarely appear as one clear line item.
By contrast, boards and CEOs can point to AI outcomes, cyber resilience, innovation and revenue as visible priorities. Debt often attracts attention only when it delays a project, raises operating costs, complicates integration or exposes an unsupported system. Daniel Saroff, group vice president for consulting and research at IDC, summed up the visibility problem: “It’s not a sexy subject,” he says. “It’s not a subject the board are pounding their fists over.”
The budget evidence suggests the gap is not simply lack of awareness. IDC’s Future Enterprise Resiliency and Spending Survey, Wave 3, conducted in March 2024 and reported in CIO/IDC analysis, found that 38% of IT professionals anticipated overspending on digital infrastructure; among those respondents, 47% attributed the overspending to excessive technical debt. Separately, IDC’s 2023 CIO Sentiment Survey findings, published in 2024, put the average share of IT budgets allocated to reducing technical debt at 12.8%; 79% of respondents reported having no formal process for tracking and reporting it. These figures describe survey responses, not a universal budget benchmark or a measured cost for every organization.
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What does technical debt cost an IT budget?
There is no single cost figure that fits every company. The useful estimate is the burden the organization can connect to specific assets and work. That burden may be direct, such as maintenance spending, or indirect, such as the extra effort required to keep a transformation project moving.
- Run costs: maintenance and support work for systems that consume staff or vendor resources.
- Integration costs: added work to connect old applications, platforms or databases to a new business program.
- Delay costs: projects slowed or made more expensive by systems that are difficult to change.
- Risk exposure: unsupported hardware or software that cannot receive needed patches. Tim Beerman, CTO at Ensono, said, “In today’s market age where cybersecurity attacks are on the rise, hardware and software that’s not supported obviously leads to vulnerabilities that maybe can’t be patched.”
- Opportunity costs: revenue, efficiency or customer improvements that cannot be delivered promptly because the underlying technology is in the way.
For a board case, estimate only costs that can be tied to evidence: spending by asset, support status, staff effort, project slippage, integration work or a documented security exposure. Separate current costs from projected savings, and distinguish a forecast from a realized saving. That makes the case more credible than presenting a broad “technical debt” estimate with no asset-level explanation.
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How can a CIO justify modernization to the board?
Make the proposal about the business outcome the organization already wants, rather than asking executives to fund cleanup for its own sake. A legacy ERP replacement, for example, can be presented as an enabler of a customer-intimacy program if the existing environment would otherwise require expensive integrations across multiple databases. The connection should be specific: what the current system prevents, what the initiative needs, and what changes if the constraint is removed.
- Build an inventory. List applications, hardware, data stores, platforms and development tools. Record owners, business purpose, dependencies, costs where available, and whether vendor support remains in place.
- Find the pressure points. Identify redundant assets, unsupported systems, unusually costly services and platforms that constrain integration or delivery. Ask what is not working well, where monthly spending is going and what return the organization gets from it.
- Connect each candidate to an outcome. Link remediation to a funded transformation, AI infrastructure, cybersecurity, ERP, mainframe, customer or revenue program. State what can proceed sooner or more safely if the constraint is addressed.
- Show the financial case. Put near-term costs and expected benefits alongside long-term savings, efficiency gains and revenue enablement. Explain assumptions and avoid counting the same maintenance reduction or project benefit in multiple places.
- Set checkpoints. Treat major modernization as a multiyear program with an architecture, design and plan that can be adjusted as delivery proceeds. Beerman cautioned, “These things aren’t flipping a switch,” and said a plan should allow the organization to “course correct along the way.”
The point is to give executives a decision they can evaluate: invest in a defined change to remove a defined constraint, or accept the associated cost, delay or risk. IDC’s Daniel Saroff put the relationship plainly: “You can’t modernize without addressing tech debt.”
Which legacy systems should be prioritized first?
Rank candidates by their effect on business goals, not simply by age. An older system that remains supported and performs reliably may be a poor replacement candidate if its cost and risk are low. A newer but unsupported or highly constraining system may demand faster action.
- Business criticality: How important is the asset to essential operations or a funded initiative?
- Security and support: Is the system unsupported, and does that create an exposure that cannot be adequately patched?
- Agility impact: Does it delay changes, integrations or delivery of business capabilities?
- Maintenance burden: What spending and internal effort does it require, and how is that burden changing?
- Data and integration dependency: How many important applications or data stores rely on it, and what will need to move with it?
- Replacement economics: What is the implementation cost and duration compared with the expected near-term return, longer-term savings or revenue enablement?
Use these factors to create a ranked shortlist, then validate the ranking with business and technology owners. A high-risk asset may warrant urgent remediation; a costly but stable system may be scheduled with a broader program; a reliable system whose replacement costs exceed its benefits may remain in service. The ranking is a portfolio decision, not a blanket rule to replace everything old.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Should technical debt be fixed before launching AI?
Not all of it. Address the debt that prevents the AI initiative from using required data, connecting to necessary systems, meeting security needs or delivering a credible business outcome. A full estate-wide cleanup before any AI work may defer value without reducing a relevant constraint. Conversely, launching on top of fragmented, poorly managed data can reveal problems that the project cannot solve on its own.
Ricardo Madan, senior vice president for global technology services at TEKsystems, described AI as “like a truth serum,” adding, “AI will let you know what that data state is.” The practical implication is to assess data dependencies and system readiness early, then scope remediation to the initiative’s actual blockers. Put the enabling work inside the AI program where that is the clearest way to fund and govern it, while tracking it as a real cost rather than treating it as free infrastructure.
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This approach keeps modernization tied to outcomes while preserving room to defer debt that is not material. It also gives executives a clearer choice: fund the specific work needed for the AI initiative, change the initiative’s scope, or accept the limitations of the current environment.
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