“Significant” investment in AI has tripled compared to the previous year and half of organizations are now committing more than 20% of their total tech budget to scaling AI initiatives.
As AI investment soars, the race for agentic is outpacing the modern data infrastructure required to support it. This is according to a new Semarchy survey of 1,000 global C-level executives across the UK, US and France, which shows data management (51%) is now viewed as a single most pressing challenge, surpassing both cost and talent.
With half of leaders currently implementing AI initiatives without Master Data Management (MDM) foundations (51%)1, and a third without enforcing data quality standards (38%)2, many are at risk of rendering their new agentic capabilities fundamentally unreliable, increasingly costly, and impossible to scale.
Poor Data Foundations Already Causing Project Delays and Compliance Issues
The new report also reveals the direct consequences of lacking data foundations have already been felt. Last year, one in five leaders experienced AI project delays due to data quality concerns (22%), operational inefficiencies from unreliable data (21%), or compliance issues linked to data protection regulations (19%).
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While only 50% of leaders had prioritized ethics and regulation of the use of AI in their organisation in 2025, that focus has rapidly formalized; this year 77% of leaders have fully integrated AI considerations into their data governance policies. This shift suggests that many are now retrofitting compliance under pressure rather than having built it proactively from the start.
Optimism Surges Despite Acknowledged Skills and Strategy Gaps
The challenges haven’t stemmed business leaders’ rapidly increasing optimism around reaching their AI goals (doubled to 92% from 46% in 2025’s survey), with nearly two-thirds (65%) of leaders now also pushing to develop agentic data management capabilities this year. Yet most acknowledge that their organization’s overall data skills (83%) and strategy (82%) are holding them back from reaching their full potential.
As a result, just under half (48%) are investing in a DataOps approach this year to bridge the gap – applying software engineering discipline to data delivery, with the aim of ensuring rapid, reliable delivery of high-quality data products.
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“We are seeing a stark divide,” says Craig Gravina, Chief Technology Officer at Semarchy. “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.”
Data Leaders Sidelined from AI Strategy Despite Critical Role
“The disconnect between ambition and reality often starts at the top,” Gravina adds. “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.”
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