Survey: AI is Developing Faster Than Data Trust
In the rapidly evolving world of artificial intelligence (AI), enterprises are enthusiastically adopting AI technologies at a pace that surpasses the development of critical data management, visibility, and recovery controls. This accelerating trend has led to what Veeam Software terms a “data and AI trust gap.”
In its latest Data & AI Trust Gap Report, Veeam Software highlights the findings of a global survey involving 600 executives from various industries. The report reveals that while AI adoption itself is not the primary challenge—88% of companies are either using or piloting AI agents—only 7% of these companies are considered “truly AI ready.” Alarmingly, 95% of respondents indicate that data challenges have stymied AI progress.
[Click on image for larger view.] Key findings (Source: Veeam).
AI Adoption: A Trust Issue More Than a Technological One
According to Veeam CEO Anand Eswaran, the main challenge lies not in adopting AI but in establishing trust. He states, “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 shifts from whether you can use AI to whether you can ensure all your data is secure, managed, compliant, and resilient. And if something goes wrong, can you recover with precision?”
If the AI Goes Down, It May Not Look Like Downtime
One of the most operationally significant findings from the report is the warning that AI outages may differ from traditional outages. As AI systems grow more autonomous, the risk is shifting from widespread system failures to data-level errors that are harder to detect, explain, and contain.
This shift poses new challenges for data protection and recovery strategies. If an AI agent modifies data, leaks sensitive information, triggers an incorrect workflow, or influences a business decision, recovery may require more than just restoring a virtual machine, database, or application environment. It becomes crucial to understand what data was used, what systems were accessed, what actions were taken, and what decisions were influenced.
Veeam’s survey found that only 22% of companies using AI could identify what data the system was using within minutes, 29% could see what systems were accessed, 25% could see what actions were taken, and 24% could determine what decisions were influenced. Only 40% of executives expressed high confidence in their ability to isolate and accurately reverse an AI agent error.
This insight directly ties the AI discourse to data resilience. Veeam emphasizes that as errors occur at machine speed, resilience must progress from comprehensive recovery to precision recovery, focusing on restoring only the affected components rather than resetting entire environments.
Small AI-Enabled Group Reports Measurable Results
For organizations looking to bridge the AI trust gap, the report outlines AI readiness using three essential building blocks: ambition, visibility, and governance. Companies must have clear objectives for data and AI, maintain a reliable overview of their data storage and location, and implement governance structures that ensure secure and compliant data usage.
Ultimately, while AI technologies continue to transform industries, the journey to trust and effective data management is paramount. Organizations must prioritize these aspects to fully leverage AI’s potential without increasing reputational and operational risks.
For more information, read the full report Here.
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