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Veeam’s 2026 Data & AI Trust Gap report argues that organizations are adopting AI faster than they can reliably see, govern, and recover the data and systems it depends on. Its central finding is that AI ambition is widespread, but only a small share of surveyed organizations say they have the visibility and governance needed to turn that ambition into trusted outcomes.
What is the data and AI trust gap?
Veeam defines the trust gap as the distance between an organization’s AI ambition and the results it can achieve when the data and systems behind AI are not sufficiently trustworthy. In practice, this is not just a question of whether a company uses AI. It is whether the company can identify the data AI uses, govern its use, establish accountability, and restore reliable operations if something goes wrong.
Veeam’s June 3, 2026 announcement, updated August 20, says 83% of surveyed CEOs feel pressure to accelerate AI and data capabilities, while 95% of surveyed leaders say data challenges slowed AI progress in the past year. Veeam also reports that 88% of surveyed organizations already use or pilot AI agents. These are findings from Veeam’s survey, not independently verified estimates of all organizations. The announcement identifies 600 senior leaders across North America, Europe, and Asia Pacific; the publisher pages do not provide full sampling details, weighting, field dates, or confidence intervals. Veeam’s announcement
Veeam CEO Anand Eswaran summarizes the problem this way: “The infrastructure to deploy AI exists, but the infrastructure to trust it doesn’t.” Veeam’s report page
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What makes an organization AI-ready?
Veeam’s readiness framework has three building blocks: ambition, visibility, and governance. Ambition means the organization has a reason and willingness to use AI; visibility means it can understand the data and AI activity involved; governance means rules, responsibilities, and controls are established and applied.
Veeam reports that only 7% of surveyed organizations have all three building blocks in place. Within that AI-ready group, 97% report significant, quantified business outcomes. This is a relationship reported by the survey, not evidence that readiness alone caused those outcomes. Veeam’s report page
The report also describes four conditions for trusting data. They are operational and leadership conditions that complement, rather than replace, the three-part readiness model.
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- Visibility: Know where data lives and how it moves.
- Enforced controls: Put governance into practice rather than leaving policies on paper.
- Tested recovery: Check that the organization can restore clean data under real conditions.
- Executive alignment: Make ownership and accountability clear across leadership.
Why can AI initiatives struggle to deliver results?
Veeam’s survey findings point to data quality and access as practical constraints. It reports that 79% of respondents say their organization’s data needs to be more up to date, 74% say it needs to be more accurate, and 71% say it needs to be more accessible. Each percentage describes a separate reported data need; none, by itself, measures the size of any resulting business loss. Veeam’s announcement
These findings help explain why deploying models or agents is not the same as being ready to use them reliably. If data is stale, inaccurate, difficult to locate, or governed inconsistently, teams may struggle to know what an AI system relied on or whether its output is suitable for a particular decision. The report frames visibility, governance, and recovery as organizational capabilities, not as a single technology purchase.
What does the report say about shadow AI?
Shadow AI is the use of AI tools that have not been approved or provided by an organization. Veeam reports that 95% of surveyed organizations know employees use unapproved AI tools, while 25% provide approved AI tools for all employees. The figures describe awareness and availability, respectively; they do not establish how often employees use unapproved tools or whether a particular use exposes sensitive data. Veeam’s report page
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The mismatch matters because awareness without a workable approved alternative can leave employees choosing tools outside formal controls. A useful response is to define what data may be entered into AI systems, make approved options and guidance accessible, and assign a clear path for reviewing new uses. Those measures address the governance problem without assuming every unapproved use has the same level of risk.
Do executives agree on AI visibility and accountability?
Veeam’s survey reports a difference in confidence about AI inventories: 65% of CEOs say their AI inventory is complete and reliable, compared with 52% of CIOs and 44% of CISOs. It also reports that 28% of respondents are confident they could detect an AI agent operating outside approved parameters. These figures suggest potential gaps in shared visibility and monitoring, but they do not show that one job title should own all AI risk. Veeam’s report page
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For organizations, the practical question is whether relevant leaders can work from the same account of which AI systems and agents are in use, what data they access, who approves their use, and who responds when an agent behaves unexpectedly. Explicit ownership matters because technical teams, security, legal, data teams, and business units may each control different parts of that picture.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can an organization close the trust gap?
Veeam’s recommendations point to coordinated governance and resilience work. A practical sequence is to establish a shared view of AI use, define controls and owners, improve data visibility, and test recovery. The report does not identify a universal product that closes the gap.
- Set the scope and ownership. Record which AI applications and agents are approved, who sponsors each use, who is accountable for its data and controls, and how exceptions are reviewed.
- Make data use visible. Map important data sources, access paths, movement, and permissions so teams can understand what AI systems can use. Data lineage and visibility capabilities may help, but the survey does not evaluate or endorse a particular platform.
- Turn governance into working controls. Set practical rules for approved tools, sensitive information, access, review, and escalation. A data governance platform or AI governance tools may support this work, but product choice should follow the organization’s requirements rather than be inferred from Veeam’s findings.
- Provide usable approved options. Give employees guidance and approved tools appropriate to their roles, and explain where to seek approval for new uses. This makes policy more actionable than a prohibition that leaves no supported alternative.
- Test recovery, including clean restoration. Exercise recovery procedures under realistic conditions and verify that restored data is clean and usable. Tested data recovery is one component of trust, alongside visibility, governance, and accountable ownership.
- Review the picture together. Bring business, technology, security, and data leaders together to check whether their inventories, controls, and responsibilities agree, then revisit them as AI use changes.
What the report establishes—and what it does not
The report is useful as a statement of Veeam’s survey findings and a framework for thinking about readiness: AI ambition needs to be matched by visibility, governance, accountability, and recovery. Its figures should be read as publisher-reported survey results from 600 senior leaders across three broad regions, not as a definitive measure of every organization or proof that one intervention causes better results. Veeam’s report landing page and announcement provide headline findings, while the reviewed publisher pages do not expose the full methodology or underlying detailed report.
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