Can Agentic AI Bridge the Gap with Trusted Enterprise Data?

Enterprise Readiness for Agentic AI

Agentic AI is shifting from a tool for information delivery to a system capable of reasoning, decision-making, and taking action. This transformation introduces a new set of challenges for enterprises, particularly around data quality, governance, and trust. According to Reltio CEO Manish Sood, the gap between the ambition of agentic AI and the readiness of enterprise data is significant. While 94% of organizations are exploring or implementing agentic AI, only 15% believe their data foundation is truly prepared for this shift.

Data Fragmentation and Governance Challenges

One of the primary barriers to successful agentic AI deployment is the fragmentation of enterprise data. Data remains scattered across multiple systems, often siloed by function or department. This fragmentation is compounded by the lack of governance models that can support AI agents operating with autonomy. Traditional governance frameworks are not designed for environments where AI can initiate actions, making it difficult to ensure consistency, accuracy, and accountability.

Historically, humans have managed these inconsistencies by relying on institutional knowledge and judgment. However, AI agents lack this contextual awareness. If an agent operates on incomplete or conflicting data, it may make decisions that are misaligned with business goals or even harmful. This raises a critical question for enterprises: it is no longer enough to build an AI agent; the environment in which it operates must be trusted and reliable.

From Filing Cabinets to Connected Intelligence

Many enterprises still operate using a model Sood refers to as the “filing cabinet” approach, where data is stored in isolated systems. While digitization has improved access to information, it has not necessarily eliminated silos. The intelligence age demands a shift from storing information to connecting it. This connected foundation enables what Sood describes as “co-agency,” a collaborative relationship between humans and AI where both contribute to decision-making and action.

However, this collaboration requires that AI agents have access to accurate, contextualized data. Without this, co-agency becomes a conflict between human and machine perspectives. For example, if a human sees one version of a customer and an agent sees another, the system cannot function effectively. This highlights the need for data that is not only available but also trustworthy and aligned with business policies and history.

Operationalizing Trusted Context

For agentic AI to deliver value, enterprises must operationalize trusted context at the point of decision. This involves connecting critical enterprise data, contextualizing it with relationships, policies, and interactions, and governing how AI agents act. Organizations must define where agents can operate autonomously, where human intervention is required, and how decisions are traced and learned from.

The goal is not simply to provide more data to AI but to make the enterprise understandable through AI. This requires a fundamental rethinking of how data is managed, how decisions are made, and how AI is integrated into business processes. As Sood notes, the companies that succeed will be those that create environments where AI can operate with trusted context and act responsibly.

Source: SAP. Analysis based on publicly available information; no hands-on testing is claimed.