The tech debt reckoning

What if a single decision — one most AI investment teams overlook — could lift the ROI on your AI initiatives by up to 29%?

New research from the IBM Institute for Business Value shows that enterprises that fully account for the cost of addressing technical debt in their AI business cases project 29% higher ROI than those that don't. Not by deploying more models, hiring more talent, or spending more — but by strategically tackling the liabilities hidden in their technology environment.

That’s the quiet differentiator between AI portfolios that scale profitably and those that stall. Because the same research shows the flip side: ignore technical debt, and returns can drop by 18% to 29%, turning strong margins into marginal outcomes.

86% of executives say technical debt is already constraining AI success.

From IT nuisance to strategic lever

For decades, technical debt was treated as a background IT chore — a CIO headache to be managed quietly. In the era of AI, it’s become a board‑level concern. 81% of executives say technical debt is already constraining AI success. 69% believe it will render some initiatives financially untenable. And they project it will add 15% to 22% to schedules — turning 30-month implementations into 36-month implementations.

In markets where advantage is measured in quarters, those extra months can mean arriving after competitors have secured customers or locked down market share.
 

Why AI magnifies the technical debt problem

AI initiatives inherit the pre‑existing conditions of your technology estate: data trapped in incompatible systems, brittle infrastructure that can’t support modern workloads, fragmented architectures that slow integration, and deferred upgrades that inflate complexity.

These aren’t passive inefficiencies; they are active constraints. They inflate costs, derail timelines, and tie up scarce AI talent in firefighting instead of innovation. 
 

Survey findings quantify the extent of the impact

Three moves to capture the 29% lift

The report identifies three strategic decisions that can unlock the ROI upside while reducing risk:

• Make debt‑adjusted ROI your investment filter. 
  This reframes remediation as a direct enabler of business value, not an IT maintenance chore. By accounting for modernization, integration, and vulnerability fixes up front, you avoid the bad surprises that sink projects mid‑implementation.
• Focus investments where debt fixes compound. 
  Spreading AI budgets across too many domains multiplies debt problems. Concentrating initiatives in a few critical areas lets each debt fix accelerate the next project. 80% of executives agree that remediating debt in one initiative can improve the ROI of related future initiatives.
• Position IT as the multiplier.
  Debt is resolved in IT, and by funding AI capabilities within IT — such as automated refactoring, intelligent testing, and shared data pipelines — you create a foundation every initiative can reuse. This consistency reduces risk, accelerates timelines, and frees budget for business‑facing AI projects.
   

The goal isn’t debt‑free perfection — it’s debt by design, where liabilities are known, targeted, and priced in for ROI.

The stakes: Measured in hundreds of millions

For a $20‑billion enterprise putting 20% of IT spend into AI, tech debt could add more than $120 million a year in hidden implementation costs. That’s the kind of drain that can leave a three‑year AI program delivering far less value than planned — while competitors pull ahead and investors lose patience.
 

The window for action

By 2027, AI’s share of IT spending is projected to rise from roughly 11% to over 18%, part of a deliberate rebalancing of technology portfolios toward what executives expect to be a consistently high‑return category.

That growth signals confidence, but the survey reveals a gap: most organizations still lack a shared definition of technical debt, a consistent way to quantify it, and a clear plan to address it. Without those foundations, increased AI spend risks being undermined by hidden remediation costs and extended timelines.

The time to act is now — while ROI projections remain high enough to fund the modernization required for sustainable success.
 

Where the conversation goes next

The data in this study points to a simple but profound shift in how AI investments should be planned: technical debt is not an IT maintenance issue — it is a quantifiable business variable that can be managed for advantage.

Organizations that build debt‑adjusted ROI into their decision process are not just avoiding risk; they are creating a repeatable way to clear barriers, accelerate implementation, and compound returns across the portfolio.

This report explores that shift in detail — from the metrics that expose the real cost of debt, to the governance patterns that prevent new liabilities, to the investment strategies that turn remediation into a source of competitive speed.

For leaders shaping their first large‑scale AI programs, the next step is to examine the full set of findings and frameworks. They offer a sharper lens on the opportunity in front of you — and on the consequences of letting it pass.

To learn more, including how IBM built multiple AI capabilities on a shared, continuously improving foundation for its human resources and IT functions, download the report.