Veeam Software has published new global research, the ‘Data and AI Trust Gap’ report. This exposes a ‘stark and widening gap at the heart of enterprise AI’ – while 88% of organizations are already using or piloting AI agents, only 7% qualify as truly AI-ready and 95% say data challenges have already slowed their AI progress. As agentic AI moves from pilots into production, organizations face an urgent challenge: ensuring that the data powering those systems is visible, governed, secure, and resilient.
The research, based on a global survey of 600 senior executives across financial services, healthcare, manufacturing, retail, and technology, shows that AI adoption is scaling dramatically faster than the governance structures designed to manage it. Despite strong executive investment and intent, the ability to control, monitor, and recover from AI failures is critically underdeveloped.
Key findings show AI is scaling faster than control:
- Only seven% of organizations are truly AI-ready.
- 88% are already using or piloting AI agents.
- Only 28% are confident they can detect AI systems operating outside approved parameters.
- 95% say data challenges have already slowed AI progress.
“Most organizations don’t have an AI adoption problem; they have an AI trust problem,” said Anand Eswaran, CEO of Veeam. “The first phase of AI was defined by infrastructure investment, experimentation, and acceleration. The next phase will be defined by trust. With the widespread adoption of autonomous AI agents operating at machine speed, the question transitions from whether you can use AI, to whether you can ensure all your data is secure, governed, compliant, and resilient. And should something go wrong, can you recover with precision? That’s how you accelerate safe AI at scale without accelerating reputational and operational risk.”
Executive confidence masks an operational reality gap
The research uncovers a significant perception gap between the boardroom and the operational teams responsible for delivering AI outcomes. Progress frequently stalls between intent and execution: governance exists inconsistently, data is managed reactively, and ownership is assigned but fragmented:
- 65% of CEOs believe they have a full AI inventory, compared with just 48% of technical leaders.
- 52% of CEOs believe they actively lead on data, but only 41% of CISOs and 38% of CIOs agree.
- 48% of CEOs believe trusted, secure, and compliant data could unlock more than 25% revenue growth.
- 83% of CEOs feel pressure to accelerate their AI and data capabilities.
This combination of rapid AI adoption, coupled with incomplete visibility and unclear accountability, creates the conditions for failures that are difficult to detect, explain, and contain.
When AI fails, it won’t look like downtime
As AI systems become more autonomous, the nature of failure is shifting. Risk is moving away from traditional system outages toward data-level failures that are harder to detect, explain, and contain. The research warns that machine-speed mistakes can outpace detection, forcing resilience to evolve from broad recovery to precision – restoring only what is impacted, rather than rewinding entire environments.
Among organizations running AI today, only a minority could identify within minutes:
- Which systems it accessed (29%).
- What actions it took (25%).
- What decisions it influenced (24%).
- Which data the system used (22%).
Only 40% of leaders are very confident they can isolate and precisely reverse an agentic AI failure.
Governance is converging on data
The governance challenge is converging on data from two directions: internal demand and external scrutiny.
Inside organizations, unauthorised AI use is now mainstream:
- 95% report unauthorised AI use within their organization and 93% view it as a significant risk.
- Yet only 25% offer approved alternatives, meaning most are trying to suppress demand rather than govern it effectively.
- 44% say increased cyber risk is the top shadow AI risk.
At the same time, regulatory pressure outside the organization is intensifying. 61% of organizations say the EU AI Act has already influenced AI investment strategies in the last 12 months, while 47% cite maintaining audit trails for AI decisions as their biggest compliance concern.
Trust requires ownership, not shared ambiguity
The new research shows that the core barriers to progress are fragmented ownership and misaligned operating disciplines – with data, AI, and governance responsibilities often spread across teams in ways that dilute accountability and slow execution. When ‘everyone owns it’, no one can decisively set policy, enforce controls, or prove outcomes.
Where ownership is clearly defined, outcomes improve significantly:
- 24% more likely to detect rogue AI behaviour in organizations where CISOs own AI agent risk.
- 47% less likely to detect rogue AI behaviour in organizations relying on shared ownership.
Data doesn’t need another champion – it needs accountable leadership strong enough to align governance, security, privacy, compliance, and resilience.
A clear divide is emerging between organizations that can operationalise trust and those that cannot. Organizations that successfully align ambition, visibility, and governance significantly outperform their peers.
Among organizations classified as fully AI-ready, 97% report measurable business benefits from data and AI investments, compared with 48% overall, demonstrating the value of operationalising trust at enterprise scale.
