Fragile methods, inefficient workflows and strategic gridlock are just some of the disagreeable negative 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 another way from different digital applied sciences — and it might probably function an accelerant to debt. Legacy methods, siloed information, outmoded APIs and outdated architectures create a debt basis.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,” stated Matt Lyteson, CIO of know-how platform transition at IBM. A 2025 research carried out by the IBM Institute for Enterprise Worth discovered that of the 1,300 senior AI decision-makers surveyed, those that reported their firms 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 know-how 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,” stated Koenraad Schelfaut, a senior managing director at Accenture. “The primary is your current 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 varieties of technical debt. It slows groups down, inflates budgets and short-circuits transformation. However AI dials up the challenges: ageing code, undocumented methods and siloed information increase from an IT headache to a full-blown enterprise drawback. As a result of AI reshapes workflows throughout models and departments, CIOs should look at it by way of a broader lens of change administration and alternative prices.
The implications of this debt compound rapidly. “It is not clear who owns, pays and helps AI initiatives,” stated Carlos Casanova, a principal analyst at Forrester. This makes it troublesome to pin down the supply of an issue — or determine the appropriate consequence. What’s extra, not like an on-premises server or infrastructure within the cloud, AI debt is commonly invisible — till a mission goes astray, a safety hole seems or a finances overrun surfaces.
AI debt usually hides behind early success, Schelfaut stated. Chatbots help employees, pilot initiatives present promise and preliminary rollouts ship progress. Initiatives achieve momentum, and enterprise leaders achieve confidence. Then, all of the sudden, because the group makes an attempt to scale an initiative, issues go astray. “All of a sudden, you possibly can’t get methods to speak to at least one one other, and you may’t accomplish what you had got down to do,” he stated.
A part of the issue is how CIOs body the difficulty. Many view AI debt as an IT upkeep drawback quite than a enterprise problem, Schelfaut stated. In consequence, they concentrate on the price of sustaining legacy methods however overlook the obstacles they impose. AI flips this logic. “Technical debt is much less about what outdated methods are costing you to take care of than what they don’t seem to be permitting you to do,” he stated.
Escaping this myopia begins with an understanding of what technical debt truly prices, Schelfaut stated. He recognized the next 4 distinct dimensions:
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The direct price of operating and sustaining methods and infrastructure.
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The curiosity price related to inefficiencies that reach 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 inconceivable for a corporation to construct out AI.
Most organizations concentrate on solely the primary dimension, Schelfaut stated. The opposite three are the place AI debt does the true injury.
New guidelines, new instruments
Issues aren’t going to get any simpler within the months and years forward. In line with the IBM Institute for Enterprise Worth survey, 69% of executives imagine that unaddressed technical debt will render some AI initiatives financially untenable. “CIOs and CFOs must be speaking about debt-adjusted ROI now,” Lyteson stated.
Agentic AI raises the stakes as a result of it introduces new dangers — and publicity factors. Permissions and controls designed for people usually break down when brokers function at machine velocity. And since these brokers talk with one another in methods which are troublesome to foretell and monitor, compute and token prices can spiral, driving the necessity for AgentOps alongside FinOps.
As brokers proliferate, conventional monitoring instruments fall brief. New metrics and monitoring instruments should ship visibility into AI agent conduct, interactions and the infrastructure, information and fashions they devour. With out this visibility, CIOs cannot clarify prices, dangers or failures to the board, Casanova stated. In addition they cannot intervene earlier than points set off compliance, safety or operational failures.
The repair is not extra know-how; it is higher visibility into AI and the workflows it touches. Lyteson stated a vital start line is to reexamine the way in which initiatives unfold — and who’s accountable for them. IBM makes use of “AI fusion groups” that span IT and enterprise features. These teams “outline the outcomes we need to obtain by way of AI, run fast experiments to gauge how they impression workflow and have interaction staff to see precisely how their work adjustments,” he stated.
As IBM spins up AI initiatives, it measures their worth in opposition to three standards — utilizing every as a device to identify technical debt. Productiveness instruments should show time financial savings. Agentic workflows are held to a distinct commonplace: measurable good points in income development, operational effectivity or per-unit workflow prices. Compliance and safety initiatives should present a transparent discount in threat.
Balancing the books
The concept is not to eradicate technical debt earlier than deploying AI, Schelfaut stated. It is to determine obstacles to progress and engineer important fixes. This requires abandoning the mindset that new AI options can sit immediately atop current infrastructure and performance inside point-to-point interfaces. The excellent news? AI itself is an effective device for figuring out points — documenting legacy methods, rewriting fragile code and figuring out what structure wants to vary.
A robust governance framework is the glue that holds every part collectively, Casanova stated. As AI instruments multiply throughout IT and enterprise models, organizations should totally 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 stated.
Ultimately, CIOs should acknowledge that AI technical debt is not an issue to resolve — it is a situation to handle. Throwing know-how 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 begin and versatile sufficient to refine, so you possibly can iterate on what’s working and weed out what just isn’t.”
