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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Veeam’s 2026 Data & AI Trust Gap report describes a widening distance between organizations’ ambition to use AI and their ability to trust the data and systems behind it. The report’s central message is that adoption alone does not make an organization AI-ready: visibility into data, practical governance, clear accountability and tested recovery matter too.
What does the Data & AI Trust Gap mean?
Veeam defines the gap as the distance between AI ambition and AI results, constrained by whether an organization can trust its data and is prepared to manage AI in practice. A company may be under pressure to deploy AI and already experimenting with agents, yet still lack a reliable view of which data and systems those tools use, who is accountable for them, or how to recover if something goes wrong.
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In Veeam’s announcement of June 3, 2026, updated August 20, the company reported that 83% of surveyed CEOs felt pressure to accelerate AI and data capabilities, and 95% said data challenges had slowed AI progress in the preceding year. Veeam also reported that 88% of surveyed organizations were already using or piloting AI agents. These are survey findings, not independently verified estimates of all organizations.
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What did Veeam’s survey find?
Veeam says it surveyed 600 senior leaders across North America, Europe and Asia Pacific. Its published pages do not provide full sampling details, weighting, field dates or confidence intervals, so the results should be read as Veeam-reported views of those respondents, not as proof that every region or organization has the same experience.
| Finding reported by Veeam | What it suggests |
|---|---|
| 95% said data challenges slowed AI progress in the past year. | Data readiness is a practical constraint, not just a technical afterthought. |
| 79% said their organization’s data needs to be more up to date, 74% more accurate and 71% more accessible. | Respondents identified freshness, quality and access as distinct data needs. |
| 65% of CEOs said their AI inventory was complete and reliable, compared with 52% of CIOs and 44% of CISOs. | Leaders did not share the same level of confidence about knowing what AI is in use. |
| 28% said they were confident they could detect an AI agent operating outside approved parameters. | Confidence in monitoring agent behavior was limited among respondents. |
| 95% knew employees used unapproved AI tools, while 25% provided approved AI tools for all employees. | Awareness of unsanctioned use was much more common than universal access to an approved alternative. |
What makes an organization AI-ready?
Veeam’s readiness framework has three building blocks. They describe strategic intent and organizational capability; they are not the same thing as the report’s separate list of four operational conditions for trusting data.
Ambition
Leaders need a clear purpose for AI and a realistic view of where it can produce useful outcomes. Pressure to accelerate deployment is not, by itself, a strategy or a measure of readiness.
Visibility
An organization needs to know what data it holds, where it lives, how it moves and which AI systems or agents can access it. Without an inventory that people across roles can rely on, governance and monitoring have an incomplete foundation.
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Governance
Governance turns policy into assigned responsibilities, approval paths and controls that work in day-to-day use. It should cover permitted tools and data, acceptable agent behavior, monitoring, and what happens when a control is breached.
Veeam reports that 7% of surveyed organizations had all three blocks in place. Within that AI-ready group, 97% reported significant, quantified business outcomes. This is a relationship reported by the survey; it does not establish that the three blocks caused those outcomes.
Why can AI initiatives struggle to deliver value?
AI systems depend on the data made available to them and on the organization’s ability to control their use. If data is stale, inaccurate or hard to access, an AI initiative can produce weak or unusable results even when the model or agent is technically capable. If an organization cannot see which tools are operating or who owns them, it may be difficult to identify issues, enforce policy or explain decisions.
Veeam CEO Anand Eswaran summarized the report’s argument this way: “The infrastructure to deploy AI exists, but the infrastructure to trust it doesn’t.” The practical point is not that every AI project will fail without a particular technology, but that deployment capacity and trustworthy operation are different capabilities.
What is shadow AI, and why does it matter?
Shadow AI is the use of AI tools that an organization has not approved or brought under its governance. Employees may turn to these tools when approved options are unavailable or do not meet their needs. That can make it harder for the organization to know what information is being shared, apply consistent controls, or respond when an AI system behaves unexpectedly.
Veeam’s survey found a mismatch between awareness and access: 95% of respondents said they knew employees used unapproved AI tools, but only 25% said their organization provided approved AI tools for all employees. The figures do not show why employees used those tools, but they point to a governance problem that a policy document alone may not solve. Practical approved options, communicated rules and workable oversight all matter.
Who should own AI governance and risk?
Veeam’s published findings do not establish that a single executive role should own every aspect of AI risk. The differences in confidence about AI inventory among CEOs, CIOs and CISOs instead underline why organizations need shared visibility and explicit accountability.
Executives should agree who is accountable for policy and risk decisions, while operational responsibilities are assigned to the teams that manage data, security, AI systems and recovery. The exact structure will vary by organization; what matters is that approval, monitoring, escalation and recovery responsibilities are clear rather than assumed.
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How can organizations close the trust gap?
Veeam describes four conditions for trusting data. These are practical operating and leadership conditions that complement—not replace—the framework’s three readiness blocks.
- Make data visible. Maintain a usable view of where important data lives, how it moves and which systems and agents can access it. Include ownership and permissions, not just storage locations.
- Enforce controls in practice. Translate governance into usable approval rules, access limits, monitoring and escalation. Check that the controls apply to real workflows and approved tools, not only written policies.
- Test recovery under realistic conditions. Practice restoring clean data and verify that the organization can recover the information and systems its AI-dependent work needs. A backup plan that has not been exercised does not demonstrate recovery capability.
- Align executives on ownership. Settle who is accountable for AI inventory, data quality, policy enforcement, incident response and recovery. Make sure the relevant leaders share a credible view of what is deployed and who acts when something falls outside approved parameters.
These steps are organizational practices, not an endorsement of a universal product. A data governance platform, AI governance tools, data lineage and visibility capabilities, and tested data recovery may support parts of the work, but Veeam’s survey did not evaluate specific products or establish that buying one closes the gap.
What the report can—and cannot—tell readers
The report is useful as a snapshot of how surveyed senior leaders describe the tension between AI adoption and trust. It provides a framework for discussing readiness, shadow AI, data conditions and accountability. The public pages reviewed do not expose the full report methodology or enough detail to assess how representative the results are, and the percentages should not be treated as universal benchmarks. The reported link between readiness and outcomes also does not establish causation.
Veeam’s report is available from its June 3, 2026 announcement, its report landing page and its June 2, 2026 whitepaper listing.
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