Fragile techniques, inefficient workflows and strategic gridlock are only a few of the disagreeable unintended effects ensuing from technical debt. These issues can undermine efficiency and undercut innovation. However as CIOs try and navigate this more and more difficult area, they encounter a brand new foe: AI.
What makes AI so difficult is that it behaves in a different way from different digital applied sciences — and it could actually function an accelerant to debt. Legacy techniques, siloed information, outmoded APIs and outdated architectures create a debt basis. AI exposes and amplifies these points, whereas introducing a brand new tax that stretches throughout an enterprise — and right into a provide chain.
“AI funding is not simply one other IT funding; it’s a reinvention of how the enterprise operates,” mentioned Matt Lyteson, CIO of expertise platform transition at IBM. A 2025 research performed by the IBM Institute for Enterprise Worth discovered that of the 1,300 senior AI decision-makers surveyed, those that reported their corporations ignored the difficulty of technical debt noticed returns on initiatives drop by 18% to 29%, with timelines increasing by as a lot as 22%, In the meantime, a Forrester report discovered that 75% of expertise decision-makers count on technical debt to rise to a “extreme” stage in 2026.
CIOs could also be on the hook for AI debt, however the issue — and the answer — extends past IT. “There are two elements of the equation,” mentioned Koenraad Schelfaut, a senior managing director at Accenture. “The primary is your present technical debt, which is stopping you from deploying AI at scale. The second is that whereas deploying AI, issues that weren’t technical debt change into technical debt.”
On the margins
At first look, AI-specific debt resembles different sorts of technical debt. It slows groups down, inflates budgets and short-circuits transformation. However AI dials up the challenges: growing older code, undocumented techniques and siloed information broaden from an IT headache to a full-blown enterprise downside. As a result of AI reshapes workflows throughout items and departments, CIOs should look at it by a broader lens of change administration and alternative prices.
The results of this debt compound shortly. “It is not clear who owns, pays and helps AI initiatives,” mentioned Carlos Casanova, a principal analyst at Forrester. This makes it tough to pin down the supply of an issue — or establish the appropriate final result. What’s extra, not like an on-premises server or infrastructure within the cloud, AI debt is usually invisible — till a challenge goes astray, a safety hole seems or a finances overrun surfaces.
AI debt typically hides behind early success, Schelfaut mentioned. Chatbots help employees, pilot initiatives present promise and preliminary rollouts ship progress. Initiatives achieve momentum, and enterprise leaders achieve confidence. Then, out of the blue, because the group makes an attempt to scale an initiative, issues go astray. “All of the sudden, you’ll be able to’t get techniques to speak to at least one one other, and you’ll’t accomplish what you had got down to do,” he mentioned.
A part of the issue is how CIOs body the difficulty. Many view AI debt as an IT upkeep downside quite than a enterprise problem, Schelfaut mentioned. Consequently, they deal with the price of sustaining legacy techniques however overlook the obstacles they impose. AI flips this logic. “Technical debt is much less about what outdated techniques are costing you to keep up than what they are not permitting you to do,” he mentioned.
Escaping this myopia begins with an understanding of what technical debt really prices, Schelfaut mentioned. He recognized the next 4 distinct dimensions:
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The direct price of operating and sustaining techniques and infrastructure.
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The curiosity price related to inefficiencies that stretch over time.
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Legal responsibility prices associated to safety, compliance and resilience dangers.
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The chance prices that make it not possible for a corporation to construct out AI.
Most organizations deal with solely the primary dimension, Schelfaut mentioned. The opposite three are the place AI debt does the true harm.
New guidelines, new instruments
Issues aren’t going to get any simpler within the months and years forward. In accordance with the IBM Institute for Enterprise Worth survey, 69% of executives consider that unaddressed technical debt will render some AI initiatives financially untenable. “CIOs and CFOs have to be speaking about debt-adjusted ROI now,” Lyteson mentioned.
Agentic AI raises the stakes as a result of it introduces new dangers — and publicity factors. Permissions and controls designed for people typically break down when brokers function at machine velocity. And since these brokers talk with one another in methods which might be tough to foretell and monitor, compute and token prices can spiral, driving the necessity for AgentOps alongside FinOps.
As brokers proliferate, conventional monitoring instruments fall quick. New metrics and monitoring instruments should ship visibility into AI agent conduct, interactions and the infrastructure, information and fashions they eat. With out this visibility, CIOs cannot clarify prices, dangers or failures to the board, Casanova mentioned. Additionally they cannot intervene earlier than points set off compliance, safety or operational failures.
The repair is not extra expertise; it is higher visibility into AI and the workflows it touches. Lyteson mentioned an important start line is to reexamine the way in which initiatives unfold — and who’s answerable for them. IBM makes use of “AI fusion groups” that span IT and enterprise capabilities. These teams “outline the outcomes we need to obtain by AI, run fast experiments to gauge how they affect workflow and have interaction staff to see precisely how their work adjustments,” he mentioned.
As IBM spins up AI initiatives, it measures their worth towards three standards — utilizing every as a software to identify technical debt. Productiveness instruments should display time financial savings. Agentic workflows are held to a unique customary: measurable beneficial properties in income development, operational effectivity or per-unit workflow prices. Compliance and safety initiatives should present a transparent discount in danger.
Balancing the books
The thought is not to eradicate technical debt earlier than deploying AI, Schelfaut mentioned. It is to establish obstacles to progress and engineer important fixes. This requires abandoning the mindset that new AI options can sit straight atop present infrastructure and performance inside point-to-point interfaces. The excellent news? AI itself is an efficient software for figuring out points — documenting legacy techniques, rewriting fragile code and figuring out what structure wants to vary.
A robust governance framework is the glue that holds all the pieces collectively, Casanova mentioned. As AI instruments multiply throughout IT and enterprise items, organizations should absolutely perceive hidden infrastructure prices, information sovereignty, entry permissions and controls, AI sprawl and IP leakage. “If somebody creates an agent, maybe it ought to go right into a repository for vetting earlier than it is deployed,” he mentioned.
Ultimately, CIOs should acknowledge that AI technical debt is not an issue to resolve — it is a situation to handle. Throwing expertise on the problem will not pay down the debt. “It is about greater than transformation,” Lyteson concluded. “It’s about steady enchancment. You want a framework that’s adequate to start out and versatile sufficient to refine, so you’ll be able to iterate on what’s working and weed out what shouldn’t be.”
