Veeam Software, a data and AI trust company, announced the launch of its Data and AI Trust Maturity Model, a research-informed and customer-validated framework designed to help organizations assess, benchmark, and strengthen how effectively they govern and operationalize AI as it shifts from assistive tools to autonomous agents acting on enterprise data at machine speed.Across industries, most enterprises have already crossed the first threshold by deploying AI. However, a clear gap is emerging between confidence in AI readiness and the ability to operationalize and govern it effectively. Far fewer have implemented the controls needed to govern it.

As AI agents begin making autonomous decisions on enterprise data at speed and scale, that gap is becoming a material risk. Research conducted by Emerald Research Group on behalf of Veeam shows organizations have moved faster on adoption than on the identity frameworks, data foundations, and governance needed to justify those decisions to a board, auditor, or regulator. The challenge is no longer whether AI is being used, but whether its actions can be understood, controlled, and validated.

The Data and AI Trust Maturity Model is designed to address this gap, helping organizations close the disconnect between perceived readiness and real-world execution. It provides leaders with an independent view of where they stand today and where to focus first, helping move from experimentation to accountable, production‑ready AI.

The model evaluates AI maturity across 12 dimensions and maps progress across five stages, from ad hoc to leading. It enables organizations to identify where controls exist, where they break down under real‑world conditions, and what must be prioritized to strengthen trust, governance, and resilience.

“AI confidence is high, but confidence alone does not scale,” said Anand Eswaran, CEO, Veeam. “Our research shows that while most organizations believe they are ready to scale AI safely and responsibly, many struggle to demonstrate that readiness in a board, audit, or regulatory context. The Data and AI Trust Maturity Model provides leaders with a clear, objective way to understand where they truly stand, identify execution gaps, and prioritize the capabilities required to operationalize AI trust, not just aspire to it. This is critical in an agentic world.”

Research Highlights a Growing AI Trust Gap
The Data and AI Trust Maturity Model is informed by the opinions of 300 senior business and technology leaders, including C‑suite executives responsible for data, security, risk, and technology strategy. The research reveals a consistent gap between AI ambition, confidence, and operational readiness:

• AI is no longer experimental. Nearly seven in ten organizations report AI is embedded across multiple business functions or central to their operations, meaning AI systems and agents now touch sensitive production data, customer records, and decision-making workflows every day
• Executive confidence is high, with 80% of leaders saying they are confident in their ability to scale AI safely over the next two years
• Confidence often lacks evidence, as nearly half of executives acknowledge that their confidence is driven more by intuition than by demonstrable, audit-ready proof they could readily provide to external stakeholders
• Execution challenges are emerging as AI scales, with 52% of organizations reporting AI initiatives scaled back over the past 18 months, four in ten experiencing delays, and 28% discontinuing initiatives entirely
• Barriers to progress are operational rather than technological, led by gaps in AI and machine learning skills (43%), difficulty integrating AI into existing workflows and systems (33%), regulatory uncertainty (25%), data quality limitations (20%), and explainability concerns (19%)
• Governance maturity lags deployment, with nearly nine in ten organizations reporting formal AI governance policies exist in some form, but only about one in three saying they could produce comprehensive audit evidence immediately if required

Together, these findings show that while AI deployment is advancing rapidly, execution maturity is lagging, leaving organizations exposed as they scale AI into critical operations.

From Deployment to Demonstrable Trust
Rather than focusing on adoption alone, the Data and AI Trust Maturity Model evaluates how consistently AI‑related controls, accountability, and operating practices function in real‑world conditions. It organizes trust readiness into four core value pillars:

• Understood – Visibility and context across data and AI assets, lineage, and risk
• Secured – Identity and access governance, privacy, and data protection controls
• Resilient – Backup, recovery confidence, and operational continuity for critical data and AI‑dependent services
• Unleashed – Trusted data readiness to support responsible AI development and adoption

Benchmarking Confidence Against Reality
The model is applied through the Data and AI Trust Maturity Assessment, a consultative engagement delivered by Veeam’s data, security, and AI specialists and strategy leaders. The assessment produces:

• A scored maturity profile across the model’s 12 dimensions
• Peer benchmark comparison to establish fact-based urgency and context
• Prioritized recommendations and a pragmatic roadmap to strengthen trust over time
• Executive-ready insights to support board oversight, audit conversations, and measurable progress tracking