Data debt will cripple your AI strategy if left unaddressed

As every CIO knows, AI success hinges on rock-solid data practices. But as CEOs and boards have emphasized digital transformations in recent years, funding for data management transformation efforts has been piecemeal at best. Now, with AI atop the CEO agenda, many CIOs find themselves in a bind, having to also overhaul data operations and address years, or decades, of accumulated data debt.   

If your enterprise has data debt, AI will expose it. In fact, data debt can lead to devastating failure rates with AI projects. For technology leaders, there’s no time like the present to pay down this debt with a comprehensive remediation strategy.

Data debt can arise for a variety of reasons, including old and outdated data management practices, shortcuts and compromises in infrastructure to meet near-term goals, poorly documented data sources, and inefficient data storage practices.

Research firm IDC in its 2026 CIO Agenda Predictions notes that by 2027, CIOs who delay the launch of data debt remediation will face 50% higher AI failure rates and rising costs, as model underperformance exposes issues from siloed, redundant, or poor-quality data.

“These findings reinforce that scaling AI requires disciplined investment in data foundations and integrated platforms, and that postponing these fundamentals risks turning AI ambition into sustained operational friction,” the report says.

“AI doesn’t create data problems; it exposes and accelerates them,” says Hrishikesh Pippadipally, CIO at accounting and advisory firm Wiss. “When organizations lack standardized processes, consistent definitions, and disciplined data governance, data naturally decays over time. That decay may not be visible in traditional reporting environments, but AI systems surface those inconsistencies quickly.”

Data debt is often the result of process drift — multiple teams using different definitions, inconsistent data entry standards, and siloed systems evolving independently, Pippadipally says.

“Without standardization and clear ownership, even modern systems degrade,” he says. “At our organization, we’ve learned that remediation isn’t just about cleaning historical data. It’s about instituting disciplined processes that prevent decay going forward: clear data ownership, standardized workflows, and governance embedded into daily operations.”

That said, not all AI initiatives are blocked by imperfect data, Pippadipally says. “There are smaller, well-bounded use cases, such as document summarization, drafting assistance, anomaly flagging, or reconciliation support, that can deliver value with human-in-the-loop verification,” he says. “These contained applications allow organizations to build AI maturity while foundational data improvements are under way.”

A mounting problem that requires a fast fix

A widespread problem, data debt at most organizations has grown organically over decades. In addition to increasing emphasis on data collection, companies have also accumulated data debt over years of mergers and acquisitions, as well as the deployment of new systems and services either enterprisewide or by departments.

“Systems were layered in response to immediate needs, acquisitions, regulatory requirements, or departmental preferences,” Pippadipally says. “Over time, inconsistent processes and standards lead to fragmented data environments.”

Moreover, data management inefficiencies have historically been addressed with manual work-arounds, Pippadipally says. “Teams reconciled reports manually,” he says. “Analysts compensated for inconsistent definitions. But AI reduces tolerance for ambiguity. When automated systems operate at scale, inconsistencies multiply rather than average out.”

It’s vital to address this now because AI initiatives are moving faster than process maturity. There is a clear sense of urgency.

“If organizations don’t institutionalize process discipline and standardization, they risk automating chaos instead of improving outcomes,” Pippadipally says. “The issue is not simply poor data; it is the absence of sustained governance to keep data reliable over time.”

For many enterprises, data debt can stay hidden while they are conducting traditional business intelligence or one-off analytics, says Juan Nassif, regional CTO at software development provider BairesDev.

“AI is different; it’s far less forgiving and it quickly exposes duplicates, inconsistent definitions, missing context, and ‘mystery fields’ with unclear lineage,” Nassif says. “When you scale beyond pilots, those issues show up as model underperformance, higher iteration cycles, and rising operational costs. It’s absolutely a concern for us, too, and we treat it as a prerequisite for scaling AI responsibly.”

If data is incomplete, inconsistent, or duplicated, the output from AI models becomes unreliable. “That can mean wrong answers, poor recommendations, or automations that break at the worst time,” Nassif says. “Teams end up spending most of their time wrangling data, reworking pipelines, and compensating for poor inputs with repeated tuning and exceptions.”

Some form of data debt is present in every sector, and in virtually all sizes of organizations.

“I witness the consequences of data debt in my daily work with schools in the UK every single week,” says Mark Friend, director of Classroom365, which consults educational institutions on technology and architecture and strategies.

“Most people assume that when they purchase the latest AI tool, all their problems will be solved no matter how messy the foundation underneath the hood,” Friend says. “My experience with this is that even the most expensive software is useless if the input is not reliable.” Data debt is “a fundamental risk to institutional stability,” he says.

Tips for effective data debt remediation

Enterprise-wide data debt remediation can be a significant, costly undertaking that involves multiple aspects of the business. It’s not just a technology issue, but a discipline issue as well. It requires cleaning up historical data as well as strengthening process governance to keep from repeating the mistakes or poor practices of the past.

Because of this, building and executing an effective strategy requires an organized and thorough approach. Here are some tips from experts.

Get senior management and board-level sponsorship

Any major IT initiative typically needs buy-in from senior business executives and even boards, particularly if it involves a large, global enterprise. Data debt remediation is no different. There is significant financial risk if remediation does not have the blessing and full backing of senior executives and board members.

Explaining the potential ramifications is a good way to bring attention to the need for remediation. “Make data debt visible and tie it to business risk,” Nassif says. “Data debt won’t get prioritized until it’s linked to AI failure rates, rising costs, and compliance exposure.”

Data debt is now a board-level risk, says Adrian Lawrence, founder of executive recruitment firm NED Capital, who advises boards and finance leaders on enterprise data governance, reporting integrity, and AI readiness.

“I see the pressure mounting with boards linking their AI investment to productivity and profitability objectives, but disjointed financial, sales, and operations data severely undermine model accuracy,” Lawrence says. “They lay bare the deficiencies [enterprise platform] upgrades and antiquated technology did not fully address.”

Success with debt remediation “demands executive sponsorship, disciplined data governance, and staged architecture cleanup treating data as an asset on the balance sheet,” Lawrence says.

Standardize core processes before scaling AI

To make the benefits of data debt remediation more long lasting, enterprises need to standardize their core business processes.

“Data quality reflects process quality,” Pippadipally says. “Leaders must align on standardized workflows, definitions, and system usage before expecting AI to operate consistently. Without process standardization, remediation efforts will be temporary.”

AI performs best in predictable environments, Pippadipally says, and standardization creates the stability AI requires.

BairesDev has embedded automated checks for data freshness, completeness, duplicates, and schema changes, so data quality issues get caught before they reach analytics or AI workflows, Nassif says.

Establish data ownership and ongoing governance

Another way to assure long-term benefits from a remediation effort is to have ongoing governance and accountability processes in place.

“Data remediation is not a one-time cleanup initiative,” Pippadipally says.

“Assigning clear ownership at the domain level, and establishing continuous monitoring, prevents data from degrading again.”

This is important, because governance ensures sustainability. “Without discipline, organizations reaccumulate data debt even after cleanup efforts,” Pippadipally says.

“We’ve been tightening dataset ownership and standardizing common business definitions, so teams aren’t training or prompting on conflicting ‘versions of truth,’” Nassif says. “We’ve been strengthening our cataloging and lineage practices, so teams can trace where data comes from, how it transforms, and who can use it — critical for both trust and governance.”

The biggest shift is mindset. “We don’t treat data remediation as a one-time cleanup,” Nassif says. “We treat it as ongoing engineering with guardrails that prevent debt from coming right back.”

Prioritize high-value, contained AI use cases

While large data modernization initiatives progress within an organization, CIOs can deploy AI in tightly scoped areas where outputs are verifiable and human oversight is straightforward, Pippadipally says.

“Examples include drafting support, controlled reconciliations, workflow triage, or anomaly flagging,” Pippadipally says. “This approach builds organizational confidence and demonstrates ROI without overexposing the enterprise to data risk.”

Clean up storage

When it comes to data storage practices, there’s no doubt that organizations need to clean up their act. Poor practices lead to poor data quality, which could impact AI-driven projects.

“Schools are often very good at storing data like [in] an attic where they just keep throwing boxes without looking inside,” Friend says. “Anyone who has lived through a technology refresh knows that messy storage is a massive financial burden.”

Decades of bad collection practices “have created a technical rot that we can no longer ignore,” Friend says. “You might think that your legacy storage is harmless, but it actually places a massive financial burden in the form of rising operational costs,” and can negatively impact AI initiatives.