Veeam has presented a new global study at VeeamON in London focused on trust in data and artificial intelligence. Veeam’s new Data and AI Trust Gap report reveals a significant and growing gap at the core of enterprise AI, especially when it comes to trust.
88% of organizations already use or test AI agents, but only 7% meet the requirements to be truly AI-ready with an adequate level of confidence. Additionally, 95% say data-related challenges have already slowed their progress in AI. As agentic AI moves from pilots to production environments, organizations face an urgent challenge: ensuring that the data powering those systems is visible, governed, secure, resilient, and trustworthy.
The study, based on a global survey of 600 senior managers across the financial, healthcare, manufacturing, retail and technology sectors, reveals that AI adoption is growing at a much faster pace than the governance structures designed to manage it, weakening organizational trust. Despite heavy investment and executive intent, the ability to control, monitor and recover from AI failures is underdeveloped, directly impacting operational confidence.
Key findings show that AI is scaling faster than control and trust:
- Only 7% of organizations are truly ready for AI.
- 88% already use or are testing AI agents.
- Only 28% are confident they can detect AI systems operating outside approved parameters, reflecting a critical lack of trust.
- 95% say data challenges have already slowed their AI advancements.
The numbers reveal a clear trust gap between AI adoption and the governance, visibility and control needed to support it.
“Most organizations don’t have a problem with AI adoption, but with trust in it,” said Anand Eswaran, CEO of Veeam. “The first phase of AI was characterized 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 is no longer whether AI can be used, but whether you can ensure that all data is secure, governed, compliant, and consistently trustworthy. And if something goes wrong, can it be accurately recovered? That’s the key to accelerating secure AI at speed. scale without simultaneously increasing operational and reputational risks.”
Managers’ confidence hides an operational gap
The study reveals a significant perception gap between senior management and operational teams responsible for AI implementation, which directly impacts internal trust. Progress often stalls between intent and execution: governance is inconsistent, data is managed reactively, and responsibility is assigned but fragmented, weakening trust in systems.
- 65% of CEOs believe they have a complete AI inventory, compared to only 48% of technical leaders.
- 52% of CEOs believe they actively lead data management, but only 41% of CISOs and 38% of CIOs agree.
- 48% of CEOs believe that having reliable, secure and compliant data – that is, with greater confidence – could generate more than 25% revenue growth.
- 83% of CEOs feel pressure to accelerate their AI and data management capabilities.
This combination of rapid AI adoption, coupled with incomplete visibility and unclear responsibilities, creates the perfect conditions for failures that are difficult to detect, explain and contain, eroding organizational trust.
When AI fails, it doesn’t manifest as a service interruption
As AI systems become more autonomous, the nature of failures is changing, putting trust in the results at risk. Risk is shifting from traditional system outages to data-level failures that are harder to detect, explain, and contain. The study warns that errors produced at machine speed can exceed detection capacity, forcing resilience to evolve towards precise recovery that preserves trust: restoring only what was affected, rather than reverting entire environments.
From the inside out and from the outside in: governance converges on data
The governance challenge converges on data from two perspectives: internal demand and external regulatory pressure, both keys to building trust.
Within organizations, unauthorized use of AI is now commonplace and impacts trust:
- 95% report unauthorized use of AI and 93% consider it a significant risk to trust.
- However, only 25% offer approved alternatives, meaning the majority are trying to suppress demand rather than confidently manage it.
- 44% point to the increase in cyber risk as the main threat associated with “Shadow AI”.
At the same time, regulatory pressure external to the organization is intensifying. 61% of organizations say the EU AI Law has already influenced AI investment strategies, reinforcing the need for trust, while 47% indicate that maintaining audit trails is their biggest concern.
Trust requires shared responsibility, not ambiguity
The study shows that the main barriers to progress are fragmentation of responsibilities and lack of operational alignment, which limits confidence in execution. When “everyone is accountable,” no one can decisively set policies, enforce controls, or demonstrate results, weakening organizational trust.
When responsibility is clearly defined, trust and results improve significantly:
Organizations where CISOs are responsible for risk associated with AI agents are 24% more likely to detect irregular behavior.
Organizations that rely on shared responsibility models are 47% less likely to detect these behaviors.
Data doesn’t need another champion, it needs responsible leadership that builds trust and aligns governance, security, privacy, compliance and resilience.
Trust is becoming the operating foundation of enterprise AI
There is a clear difference between organizations that can operate with confidence and those that cannot. Organizations that successfully align ambition, visibility, governance and trust significantly outperform others.
Within organizations, unauthorized use of AI is now commonplace and affects trust
Among organizations classified as fully AI-ready, 97% report having achieved measurable business benefits, demonstrating the value of turning trust into an enterprise-scale operational capability.
Veeam: creating the trust layer for data and AI
Veeam addresses this challenge by combining resiliency, security, and data governance to help organizations visualize what data AI uses, control how people and agents access it, and retrieve clean, trusted data accurately when incidents occur, reinforcing trust.
“The results are unequivocal. When 95% of executives say that data-related challenges are already slowing their progress in AI, the bottleneck is not the model, but trusted, governed and recoverable data,” Eswaran added. “Veeam is creating the trust layer for data and AI to give enterprises the visibility, control and precise recovery needed to safely scale AI and drive real business value.”
