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Data management has emerged as the single most pressing barrier to AI success, overtaking cost and talent concerns, according to a new Semarchy survey of 1,000 global C-level executives in the UK, US, and France. With 51% of leaders citing data management as their primary challenge, the findings highlight a critical gap: enterprises are accelerating AI investment—significant spending has tripled year-over-year, and half now allocate more than 20% of their total tech budget to scaling initiatives—but many lack the foundational data infrastructure needed for reliable, agentic AI capabilities.
Quick Intel
As enterprises race to build agentic AI—autonomous systems capable of reasoning, planning, and executing tasks—the absence of strong MDM and data quality foundations risks creating unreliable, costly, and unscalable capabilities. The survey underscores a growing divide: organizations with robust data infrastructure are positioned for trusted AI outcomes, while others accumulate significant technical debt.
“Significant” AI investment has tripled compared to the previous year, reflecting aggressive enterprise adoption. Nearly half of organizations now commit over 20% of their tech budgets to scaling AI, driven by ambitions to achieve agentic capabilities (65% priority this year). Yet the survey reveals widespread foundational weaknesses:
These gaps directly contributed to last year’s setbacks, including project delays (22%), operational inefficiencies (21%), and compliance/regulatory issues (19%) tied to poor data quality.
Craig Gravina, Chief Technology Officer at Semarchy, stated: “We are seeing a stark divide. One half of leaders building on strong MDM foundations are positioning themselves to deliver trusted data products – the essential building blocks for scaling agentic AI reliably. The other half aren't just lagging behind; they are actively accumulating AI technical debt. Trying to scale agentic AI on top of fragmented data foundations and a disjointed strategy isn't just inefficient – it creates a compounding liability that could do significant long-term harm to the business.”
Despite acknowledged gaps in data skills (83%) and strategy (82%), optimism for achieving AI goals has surged to 92% (from 46% in 2025). Nearly half (48%) are now investing in DataOps—applying software engineering discipline to data delivery—to enable rapid, reliable, high-quality data products.
A concerning trend is the sidelining of data leaders: only 7% of Chief Data Officers (CDOs) and 18% of Chief Information Officers (CIOs) are viewed as holding primary responsibility for AI strategy. Gravina added: “The disconnect between ambition and reality often starts at the top. It’s alarming that while data management is the single biggest hurdle, only 7% of CDOs and 18% of CIOs are viewed as holding a chief role in their organization's AI strategy. You simply cannot separate the AI vision from the data reality. When the architects of your data infrastructure are sidelined from the strategy room, execution gaps are inevitable.”
While only half of leaders prioritized AI ethics and regulation in 2025, 77% have now fully integrated these considerations into data governance policies. This rapid formalization indicates many organizations are retrofitting compliance in response to emerging risks rather than building it proactively.
About Semarchy
Semarchy is the modern data management company. The Semarchy Data Platform (SDP) – our AI-driven MDM and governance platform built for DataOps – ensures the rapid delivery of trusted, governed data products at scale, so businesses can easily find, understand, and consume the data they need. With a proven record of customer success, Semarchy helps customers meet the growing demand for accurate, trusted data and drive the success of data-focused initiatives like AI, Customer 360, and more. SDP is available as SaaS, as a self-managed (on-premises, private cloud) deployment, or as the only MDM platform offered as a native Snowflake application. It's also available through major cloud marketplaces, including Snowflake, Microsoft Azure, AWS, and Google Cloud Platform.
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