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The hidden data costs missing from AI budgets
Operational work to shore up a weak data foundation can distort the economics of AI projects. Bringing these costs to light helps clarify ROI and what to prioritize.
Ask a room of executives what their AI costs, and they will add up the familiar line items: models, infrastructure, software and implementation. It's a tidy number, but it's also incomplete.
The real cost of AI can be higher than the invoice. Some significant expenses, such as fixing problems with the data AI runs on, never show up in the AI budget. That work is scattered across teams with no one labeling it as AI, even though it affects the initiative's total cost.
The invisible line item
Call it the hidden data tax: the ongoing work of making an unreliable data foundation usable enough for AI. It shows up as time spent reconciling conflicting definitions, correcting bad records, reviewing outputs no one trusts and addressing the business consequences when AI acts on incomplete or inaccurate data.
This is not a minor cost. A July 2026 survey by Omdia, a division of Informa TechTarget, found that 55% of 400 organizations reported spending 10 to 50 hours per month resolving inconsistent KPI or metric definitions. Additionally, 56% reported issues from AI systems operating on incomplete, inaccurate or poorly governed data: 17% reported "significant" incidents that directly harmed business outcomes or financial performance and 39% faced "moderate" disruptions that required remediation.
These findings represent real costs. Labor hours on one side and poor decision-making on the other, which are difficult to identify in an AI budget.
Why the data labor never reaches the AI budget
This is an accounting problem before it's a data problem. The cost is real, but it's distributed and mislabeled, so it disappears from view.
The analyst who spends two days mending "revenue" across three systems records that time against the project, not AI. The operations team that double-checks a model's output charges it to operations. The rework from an AI-driven decision based on unreliable data gets absorbed as "the cost of doing business." Each cost lands with the team that makes the corrections and may never roll up into the total cost of AI.
We count what arrives on an invoice and can overlook what gets taken in as labor. Models and GPUs have a price tag, but the 10 to 50 hours per month spent on data cleanup do not. As a result, labor can be left out of the AI business case, even though its cost often overshadows the infrastructure line.
The price also hides throughout the AI lifecycle. Before a project, there's time spent sourcing and cleaning data to make it usable. During development, it surfaces in the bespoke pipeline built because no reusable data product existed. And after deployment, the most expensive phase, it shows up in human review, rework and the downstream consequences of decisions made on shaky data.
How to account for the full cost of AI
Fixing this starts with counting it. Data leaders can make the hidden tax visible in three steps.
- Name the category. Add a "data remediation" line to every AI business case for the time and effort required to make the foundation trustworthy for that use case. If it takes 40 hours of correction to launch, that is an AI cost.
- Measure it. Track the signals included in the Omdia research, such as reconciliation hours, output-review time, rework rates and incidents traced to bad data. You cannot manage a cost you refuse to measure.
- Connect it to an AI use case. Roll up the distributed costs so the initiative's true cost is visible when leaders decide what to fund.
Why organizations keep paying for data debt
The hidden data tax is not merely inflating AI costs; it's subsidizing poor data architecture. Time spent resolving metric conflicts, reviewing output and correcting bad source data is a payment on data debt. Instead of addressing the root cause, organizations continue to pay through thousands of hours of distributed labor rather than funding shared infrastructure and governance.
Organizations are already paying employees to compensate for gaps in semantic consistency, governance and reusable data products. Without broader fixes, those labor costs can continue indefinitely. The hidden data tax can resemble an interest payment on decades of underinvestment in data management. Enterprises reject the capital fix while approving the far larger operating cost.
Before approving the next AI initiative, ask the question hiding in the spreadsheet: Which costs of compensating for weaknesses in your data foundation belong in the total cost of the AI use case?
The answer is all of them. And until the hidden data tax appears on the ledger, AI business cases will keep understating costs, overstating returns and underinvesting in the one asset that compounds in value -- the enterprise data foundation.
Bill Schmarzo, "The Dean of Big Data," teaches AI-driven innovation at Iowa State University and advises organizations on data science, AI and data monetization. He is a former executive at Dell Technologies, Hitachi Vantara and Yahoo. He has written books on data-driven innovation and applied AI strategy.
