Veeam CEO: Organisations have an 'AI Trust Problem'

Businesses faced a clear decision when AI entered the mainstream. Adopt the technology or risk falling behind competitors. Yet translating that ambition into operational reality has proved more complex than many leadership teams anticipated.

Research from Veeam Software, unveiled at VeeamON London, shows that whilst enterprises are implementing AI systems at speed, most lack the foundational infrastructure required to deploy them safely.

According to Veeam Software's Data and AI Trust Gap report, which surveyed 600 senior executives across financial services, healthcare, manufacturing, retail and technology, 88% of organisations are already using or piloting AI agents.

However, only 7% qualify as truly AI-ready when measured against all three pillars of AI implementation: ambition, visibility and governance.

According to Veeam Software, 95% of respondents say data-related challenges have already slowed the progress of their AI initiatives. This means the barrier to AI adoption is not technological capability but data management maturity.

"Most organisations don't have an AI adoption problem," says Anand Eswaran, CEO of Veeam.

"They have an AI trust problem. 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."

Leadership disconnect on readiness

According to Veeam Software, a disconnect exists between executive confidence in AI capabilities and the operational reality reported by technical teams. The research found that 65% of CEOs believe their organisation maintains a complete inventory of AI systems. In contrast, only 48% of technical leaders share that view. This gap extends to data strategy ownership, where 52% of CEOs believe they actively lead on data strategy, yet only 41% of CIOs and 38% of CISOs agree.

This misalignment creates challenges for AI governance. When leadership teams hold differing views on the state of AI readiness, establishing unified governance frameworks becomes more complex. Organisations need to align executive perception with technical reality before scaling AI deployments.

Fragmented accountability for AI

According to Veeam Software, fragmented ownership of data, AI and governance responsibilities continues to undermine progress across organisations. Multiple teams often share responsibility for these functions without clear delineation of authority.

"When 'everyone owns it,' no one can decisively set policy, enforce controls or prove outcomes," the company highlights.

The research shows that organisations with clearly defined ownership structures achieve better outcomes. Where Chief Information Security Officers own agentic AI risks, organisations are 24% more likely to detect rogue AI behaviour. Conversely, organisations with shared ownership are 47% less likely to detect such issues.

According to Veeam Software, 95% of organisations now report unauthorised AI usage within their workforce. This phenomenon, known as shadow AI, represents employees deploying AI tools without IT department approval or oversight.

Whilst 93% view shadow AI as a substantial risk, only 25% provide employees with approved alternatives. This gap explains why unauthorised usage persists despite widespread recognition of the risks involved.

The research found that only 40% of leaders expressed strong confidence in their ability to isolate and reverse an AI-related failure with precision. Regulatory pressure is intensifying alongside these operational challenges. According to Veeam Software, more than six in ten organisations say the EU AI Act has already influenced their investment decisions over the past year.

Revenue growth through data

According to Veeam Software, organisations achieving success with AI are those building strong foundations before accelerating deployment. The research found that nearly half of CEOs believe trusted and compliant data could unlock more than 25% revenue growth.

However, many organisations admit their data needs to be more accurate, accessible and up to date before those benefits can materialise.

"The findings here leave no room for doubt. When 95% of executives say data challenges are already slowing their AI progress, the bottleneck isn't the model – it's trusted, governed, recoverable data," adds Anand.

"Veeam is building the Data and AI Trust layer to give enterprises the visibility, control and precision recovery needed to scale AI safely and deliver real business value."

The link between data quality and revenue growth means organisations need to prioritise data governance investments before expanding AI capabilities. For business leaders focused on growth strategies, the message is that competitive advantage in AI will come not from speed of adoption but from the ability to deploy systems that stakeholders can trust.