AI Agents Are Advancing Faster Than Enterprise Data Is Ready for Them

Interim findings from the third annual Modern Data Survey show nearly 6 in 10 organizations are piloting or running AI agents, while fewer than 1 in 10 say the data feeding their AI is production-ready.

SAN JOSE, Calif.--(BUSINESS WIRE)--The Modern Data Company today released “5 Emerging Trends in Enterprise Data & AI,” an interim look at findings from the third annual Modern Data Survey, an ongoing research initiative conducted through the Modern Data 101 community. While the survey is still underway, more than 540 qualified responses have been collected to date, and several notable findings are already emerging.

The early results show enterprise AI moving into operations faster than the data foundations required to support it. They also point to meaningful shifts since the previous Modern Data Report, published in early 2026 based on survey data collected at the end of 2025, as organizations move beyond AI-assisted analytics toward AI agents working across enterprise data, systems, and workflows.

Among enterprise data leaders and practitioners across 66 countries, 57.3% report they are now piloting or running AI agents in data and analytics workflows, including 23.5% in production and 33.8% in pilots. Another 15.6% expect to begin within six months. Only 4.0% have no plans.

The significance of that shift is in what AI is being asked to do. As agents begin working across enterprise systems and workflows, they increasingly make determinations that previously remained with people, raising the stakes for the data, context and governance behind them.

Trust Has Not Kept Pace

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%. The explanations the market usually gives ranked last: a skills gap at 25.9% and immature tooling at 19.5%.

“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, former data executive at Citi, Deutsche Bank, Voya Financial and FINRA, and a member of the Modern Data 101 community. “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.”

Business Context Is Becoming Essential

The survey identifies business context, meaning definitions, relationships, lineage and policy, as 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. One in four organizations has no formal context layer.

Given one investment to make their data and AI work more effectively, respondents chose a better context layer over better tools by roughly six to one. Even among organizations that describe themselves as AI-first, where AI is the default consideration in any new initiative, less than half (38.5%) have engineered their context layer.

Compared with organizations interested in agents but not yet using them, those already running agents in production are consistently further along. They are nearly four times as likely to have intentionally developed a context layer, almost three times as likely to trust the data underpinning their AI. The same pattern holds for proving value: organizations with an engineered context layer are roughly five times as likely to say they can draw validated causal links between their data work and business outcomes.

Because the survey provides a snapshot in time, it shows correlation, not causation. Organizations running AI agents are more likely to have engineered context layers, but the findings cannot determine whether agents drive that investment or whether those foundations enable agent deployment.

Consolidation Advances, With Carve-Outs

Nearly half of organizations (47.0%) are actively consolidating toward fewer platforms, and 17.2% are evaluating it. A fifth have no coherent position, with teams pursuing different strategies or none.

Even organizations actively consolidating retain exceptions, with nearly 90% still relying on a best-of-breed point solution somewhere in the stack.

Governance Requirements Have Outrun Governance Practice

While 65.1% say AI-enabled decisions must be explainable, traceable and defensible under scrutiny, far fewer maintain the mechanics to deliver it. 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. Just 17.7% have a clear, documented AI accountability framework. A further 25.4% call accountability shared but unclear, 19.1% poorly defined, and 11.0% say no one is formally accountable.

Organizations still need to know where data came from, who owns it and who can access it. As AI begins working across enterprise systems, they also need to determine what information AI can use, what actions it can take, which policies apply, and who answers for the outcome. The more AI can do, the more governance has to cover.

The Modern Data Interim Report: 5 Emerging Trends in Enterprise Data & AI can be found here.

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. For more information, visit www.themoderndatacompany.com.