Veeam Software has introduced a new Data and AI Trust Maturity Model aimed at helping organizations assess and strengthen their readiness for enterprise AI adoption and governance. The framework is designed to help businesses benchmark how effectively they operationalize, secure, and govern AI as organizations increasingly deploy autonomous AI agents across critical business operations.
The launch comes at a time when enterprises are rapidly adopting AI technologies but continue to face challenges in governance, accountability, and operational resilience. According to research commissioned by Veeam and conducted by Emerald Research Group, nearly 80% of business leaders believe they can scale AI safely over the next two years. However, only around one in three organizations can produce comprehensive audit-ready evidence to support those claims.
The research, based on insights from 300 senior business and technology executives, highlights a widening gap between AI deployment and governance maturity. While seven in ten organizations report that AI is already embedded across multiple business functions, many continue to struggle with identity frameworks, data governance, explainability, and operational controls required to manage AI-driven decisions at scale.
“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.” — Anand Eswaran
Anand Eswaran said organizations are moving quickly to adopt AI, but confidence alone is insufficient without measurable governance and accountability frameworks. He noted that the Data and AI Trust Maturity Model provides enterprises with an objective mechanism to identify execution gaps and prioritize the capabilities required to operationalize AI trust in an increasingly agentic environment.
The framework evaluates organizations across 12 dimensions and maps progress through five stages of maturity, ranging from ad hoc implementation to industry-leading practices. It focuses on four key pillars visibility and understanding of AI assets, security and access governance, operational resilience and recovery, and trusted data readiness for responsible AI deployment.
The study also revealed that execution challenges are beginning to affect AI initiatives, with more than half of organizations reporting project slowdowns or scale-backs over the past 18 months. Skills shortages, integration complexity, regulatory uncertainty, data quality concerns, and explainability issues were identified as the primary barriers to progress.
Veeam said the assessment will initially be delivered through its data, security, and AI specialists, with broader global availability and partner-led delivery planned later this year.
