AI adoption is accelerating across the enterprise, but reliability is lagging. In a new research report, The State of AI Reliability: Perspectives from Data & AI Leaders, CDO Magazine, in partnership with Monte Carlo, explores how organizations are approaching AI readiness, trust, and scale. The findings highlight a clear gap between ambition and operational reality.

Key insights:

• 58% of organizations expect significant AI growth in the next 12 months
• Only 18% have scaled AI across multiple teams
• 61% report experiencing “silent failures” in their data systems
• Just 29% have a clear definition of AI readiness

“Organizations aren’t struggling to adopt AI; they are struggling to trust it at scale. Reliability is quickly becoming the defining factor between experimentation and real enterprise impact.”

While AI investment continues to grow, the infrastructure required to support it (data quality, observability, and governance) remains fragmented. This disconnect is creating real risk.

Many organizations still rely on manual validation, despite positioning AI as a driver of efficiency. At the same time, gaps in data visibility and system monitoring make it difficult to detect issues before they impact business outcomes.

However, leading organizations are taking a different approach. They are prioritizing reliability as a foundation: investing in data observability, aligning governance across systems, and building the infrastructure needed to scale AI with confidence.

As AI adoption accelerates, the ability to ensure trust in data and outputs will define which organizations can move beyond experimentation and achieve enterprise-wide impact.

Download the full report to explore how leading organizations are building trusted AI systems at scale.