Poor data quality is one of the top reasons why AI initiatives fall flat. Yet CXOs struggle to secure the budget they need to get data governance initiatives off the ground. It makes sense: clean data doesn’t ship products, make a splash into new markets, or directly boost revenue. “AI usually isn’t the problem; data quality and readiness are. But those are exactly the areas leaders are least excited to fund,” Guy Bourgault, head of agentic services at technology services provider Concentrix, said in an interview earlier this year.

That’s because the budget needed for data governance competes directly with initiatives that boost revenue. However, poor data governance can be very costly. Gartner’s 2026 AI spending forecast puts worldwide investment at $2.5 trillion, while S&P Global research found that 42% of companies abandoned most of their AI initiatives last year, up from 17% the prior year.

A Precisely/Drexel LeBow survey found that the top data challenge inhibiting AI initiatives is data governance, cited by 62% of respondents. Yet, organizational AI readiness across international organizations remains very low at 17%. Governance is identified as the problem everywhere; it gets funded almost nowhere.

Why Funding Data Hygiene Efforts Falls Short

One of the top reasons CXOs can’t get that funding is that they are making the general business case for better data hygiene and governance. The CXOs getting the funding are those who accurately quantify the risk poor data quality poses to the organization’s AI success for their boards and business leaders.

Good examples abound. Mehul Nagrani, CEO of Integrate, which makes software that cleans and routes B2B sales leads, shared an example with DigitalCXO: An agentic sales development representative is sending 4,000 emails based on data within the organization’s CRM. “That agent can send 4,000 emails before you know what happened. At 3% bounces, you start getting flagged. At 5 to 7% bounces, you are actively damaging your domain reputation. At 20% bad data, you can wake up the next morning with a tanked domain and a three-month remediation ahead of you,” Nagrani says.

That cost is driven by bad marketing data. Consider the cost of bad data, even low single digits of bad data on larger business decisions, research, financial transactions. “The question every AI deployment conversation should begin with,” Nagrani contends, “is what is the downside, and is that harm contained?”

The costs of bad data add up. In a 2026 analysis, DoubleTrack scaled Gartner’s cost-of-bad-data benchmark across 8.36 million businesses in the United States and calculated that dirty data drains $617 billion from the American economy each year. That’s roughly 2% of U.S. gross domestic product. For technology and software companies, the per-employee figure reaches $12,161 per year, nearly 2.5 times the national average. Both figures predate AI deployment, and most of those interviewed for this article agree the costs of bad data only compounded after AI made its splash.

Forrester research found more than a quarter of companies lose over $5 million annually to poor data quality, and 7% lose $25 million or more.

Charles Caldwell, SVP of product management at Redwood Software, uses “stranded investment” as the frame for leadership focused on model selection and platform investments. Every dollar committed to AI models, cloud infrastructure, and ERP transformation returns value only as good as the data beneath it. If that data layer is subpar, the investment doesn’t compound: it’s stranded.

Plenty of research supports Caldwell’s assertion.

Cisco’s 2024 AI Readiness Index found 80% of organizations report shortcomings in data preparation behind their AI projects; MIT’s NANDA initiative, in research published in August 2025, found 95% of enterprise GenAI pilots deliver no measurable P&L impact. That failure is attributed to the inability to integrate AI into actual workflows, and Gartner projects more than 40% of agentic AI projects will be canceled by the end of 2027, citing unclear business value and inadequate risk controls. “The cost of poor data hygiene isn’t a technical line item,” Caldwell says. “It’s the gap between what your AI strategy promises the board and what your operations can actually deliver.”

“Every dollar you spend on the model is a dollar that assumes your data is ready,” says Bob Bloem, senior director and head of data and analytics at Caylent. The question stops being ‘what will governance cost us’ and becomes ‘what is the cost of continuing without it.'”

Ultimately, while arguing for a data governance budget may not get the investment needed to get adequate data governance in place, the argument that poor data poses a direct risk to agentic AI success and the board is more likely to pay attention. The board approved an AI initiative. That initiative carries a serious failure probability tied directly back to data quality. The cost of that failure, such as sunk vendor spend, engineering hours, and reputational exposure, nearly always exceeds the cost of the data work that would have mitigated the risk. That is the calculation CXOs put in front of leadership, and leadership responds to it.

“AI is leverage, and leverage cuts both ways. If the underlying asset is good, AI can create outsized value. If the asset is bad, it accelerates the damage at speed and scale,” Nagrani warns.