The Modern Data Company has released "5 Emerging Trends in Enterprise Data & AI," an interim look at findings from the third annual Modern Data Survey. The early results show enterprise AI moving into operations faster than the data foundations required to support it, with 57.3% of organizations now piloting or running AI agents in data and analytics workflows.
57.3% piloting or running AI agents; 23.5% in production, 33.8% in pilots.
Only 8.4% say data feeding AI is trustworthy enough for production.
Even among production users, only 21.7% are very confident in data trustworthiness.
Data quality and trust rank among top three barriers for 75.9%.
60.9% consider context layer necessary; only 16.0% have engineered one.
Just 10% maintain both audit trail and link from decisions back to data sources.
Just 8.4% of respondents say the data feeding their AI systems is trustworthy enough for production. Confidence improves for organizations further along in their agentic implementations, but it does not resolve the problem. Even among those already running agents in production, only 21.7% are very confident their data is trustworthy enough for production. Asked what stops agents from reaching production, respondents pointed to the data itself. Data quality and trust ranked among the top three barriers for 75.9%, well ahead of missing context and lineage at 63.5% and security concerns at 61.7%.
Business context is the widest gap between what organizations say they need and what they have built. A clear majority (60.9%) consider a reliable context layer a necessity for AI agents, yet only 16.0% deliberately design and engineer that layer as a product. While 65.1% say AI-enabled decisions must be explainable, traceable and defensible, just 39% maintain either an audit trail for AI inputs and outputs or a link from decisions back to data sources, and only 10% maintain both. Accountability is similarly unresolved, with just 17.7% having a clear, documented AI accountability framework.
"Enterprises have proven they can put AI agents to work. The harder question is whether those agents have the trusted data and business context they need to operate reliably," said Saurabh Gupta, president and CEO of The Modern Data Company. "The organizations further along with agents are also further along in building that data foundation. That is an important signal for every company trying to move AI into production."
"This research matches what I see in practice: AI is moving into production faster than the data supporting it is becoming trustworthy," said Julia Bardmesser, CEO of Data4Real. "The gap is familiar, but what has changed is its impact. In traditional analytics, questions about the reliability of the data could be addressed before someone acted on the result. With agents, that same data can lead directly to action. Data quality does not have to get worse for the consequences to grow considerably."
About The Modern Data Company
The Modern Data Company is redefining data management for the AI era. The company's flagship platform, DataOS, serves as the foundational analytics and AI-ready data layer for any data stack. This unified platform gives enterprises the ability to build and deploy data products, simplify data management, and optimize data costs. DataOS frees teams to focus on driving real value from data, accelerating the journey to becoming a truly data-driven and AI-enabled organization.
