The trust-at-speed paradox: Most data governance wasn't built for AI
Agentic AI operates autonomously, exposing gaps in governance, data quality and accountability. Executives must address these limits to manage risk and move AI into production.
A fundamental tension is emerging in enterprise AI that few organizations are prepared to address. I call it the "trust-at-speed paradox," and it's quietly becoming the biggest obstacle between AI pilots and production deployments.
Organizations are racing to deploy agentic AI systems capable of autonomous decision-making. These systems promise unprecedented efficiency, operating at machine speed to analyze data, make decisions and take actions with minimal human intervention. But the faster these systems operate, the faster they propagate errors, act on compromised data or create cascading compliance violations.
When a human analyst makes a mistake, it's usually contained. They catch it, correct it and move on. When an AI agent acts on bad data, it can make hundreds of downstream decisions before anyone notices something is wrong. A single flaw in data lineage or quality doesn't create one problem; it triggers untraceable, cascading risk events that ripple through business processes at machine speed.
The governance gap
Traditional data governance was designed for a different era. It assumed human-paced decision-making, including approval workflows in which someone reviews access requests, quarterly or annual audits, and manual oversight of data quality issues as they arise.
Agentic AI breaks this model entirely. A human cannot approve every data access request when an agent is making thousands of decisions per hour. The math doesn't work, and the resulting latency would defeat the purpose. Without that oversight, organizations are effectively letting autonomous systems loose on their most sensitive data assets using governance frameworks that were never designed to monitor, control or audit machine-speed operations.
This isn't a theoretical concern. Many organizations have AI pilots successfully running in controlled environments, but far fewer have moved to production at scale. In most cases, progress stalls due to mistrust in data quality and data governance, which fail to meet compliance and data security requirements.
The c-suite blind spot
This governance gap is creating new risk exposure that boards and executives don't yet fully understand. When AI agents act autonomously, fundamental questions become surprisingly difficult to answer: Who is accountable when an agent makes a poor decision? How do you trace a business outcome back through an agent's decision chain? What data did it access, and was that data accurate at the moment of access?
Fragmented governance creates visibility gaps at the executive level precisely when they need more visibility, not less. The irony is that organizations are deploying AI to gain a competitive advantage through speed and efficiency while simultaneously creating blind spots that could result in regulatory penalties, reputational damage and operational failures.
Data quality is now existential
For traditional analytics, poor data quality led to inaccurate reports -- annoying, but manageable. For agentic AI, poor data quality leads to autonomous systems taking real-world actions based on flawed information. The stakes are fundamentally different.
Without reliable data, even the most advanced AI models will produce flawed and potentially harmful results. This is no longer about model accuracy. It's about agents executing transactions, making recommendations to customers or adjusting operational parameters based on data that may be incomplete, outdated or simply wrong.
The organizations that will successfully scale agentic AI aren't necessarily the ones with the most sophisticated models. They're the ones who recognize that governed autonomy requires a completely new approach to data governance, designed for machine speed from the ground up.
The good news is that this can be addressed by recognizing how AI operates on enterprise data and rethinking where and how governance is automated and applied.
Stephen Catanzano is a senior analyst at Omdia where he covers data management and analytics.
Omdia is a division of Informa TechTarget. Its analysts have business relationships with technology vendors.
