Context Intelligence: The Foundation Every AI Agent Needs

What does Context Intelligence mean for the enterprise? 

Context Intelligence is the governed business meaning that makes enterprise data usable by both people and AI. It brings together master data, metadata, reference data, lineage, relationships, rules, provenance, and business knowledge so that data can be interpreted correctly and acted on with confidence. 

For AI agents, this is especially important because context cannot stay implicit. A human may know which customer record to trust, which hierarchy to apply, or which exception to ignore. An AI agent needs that same understanding made explicit, structured, and reusable. 

 

“Enterprise context management is basically about governing everything that allows your data to be interpreted correctly.”  — Alaa Hoblos, APGAR

 

Mehdi framed the same concept as the missing middle layer between enterprise systems and AI models: the machine-readable representation of customers, products, interactions, history, semantics, and relationships that allows agents to reason from trusted business reality. 

 

“Context intelligence is the bridge that connects your AI models with your enterprise data.” — Kash Mehdi, Reltio 

 

In short, Context Intelligence is what helps AI move from simply accessing data to understanding how the business works.

 

Can organizations build Context Intelligence from what they already own? 

The second major theme was whether Context Intelligence requires an entirely new technology layer, or whether organizations can build from the capabilities they already have. 

Both speakers emphasized that most organizations are not starting from zero. Many already have a long list of data management tools in place, from MDM to data catalogs to data governance tools. The opportunity is to connect and evolve those capabilities so they can support both human decision-making and AI-driven action. 

 

“The idea is not to replace. It is to add to what already exists.”— Alaa Hoblos, APGAR 

 

For example, a data catalog that once helped users understand definitions and lineage may now need to answer new questions: Is this data usable to train AI? Which data is best suited to a particular AI use case? What metadata is required to make a data product usable by an AI agent? 

MDM plays a particularly important role because AI agents need trusted representations of core business entities: customers, products, suppliers, materials, assets, and the relationships between them. But Mehdi cautioned that traditional approaches need to evolve. 

 

“If you are just doing classic MDM, that is not going to last in this new era.”— Kash Mehdi, Reltio 

 

The point is not that existing data management capabilities are obsolete. It is that they need to become more connected, reusable, and automation-ready. Governance also needs to move from being seen as a control function to becoming a foundation for reliable AI. 

 

Where should organizations begin? 

Lastly, one of the key questions was where organizations should start when their context capabilities are fragmented today. 

Hoblos emphasized the need to balance business use cases with a reusable enterprise framework. A use case gives the work focus: which entities matter, what context is needed, and where the value may come from. The framework ensures that context is not rebuilt from scratch every time. 

 

“You need to combine a specific use case with a common approach that can build something reusable for the future.”— Alaa Hoblos, APGAR 

 

Mehdi offered a practical first step for leaders: use the first 30, 60, or 90 days to identify and curate priority use cases. Those use cases should not simply be a list of business complaints. They should connect to enterprise strategy, business outcomes, and opportunities for automation. 

 

“Spend the first 30, 60, 90 days identifying and curating a list of use cases.”— Kash Mehdi, Reltio 

 

From there, organizations can define the operating model: who needs to be involved, how business and IT stakeholders collaborate, how use cases are prioritized, and how context is governed over time. 

The takeaway from the session was clear. Giving AI agents access to enterprise data is not enough. To act reliably, agents need trusted, connected, and reusable business context. Organizations with strong data management foundations have a head start, but those foundations now need to evolve for a world where AI is not just analyzing information, but increasingly acting on it.

 

Ready to Build Context Intelligence for AI?

Understanding the problem is only the beginning. During the webinar, Kash Mehdi and Alaa Hoblos also explored the practical questions organizations are now confronting as they move from AI experimentation toward agentic AI in production: 

• Where should organizations start when the data, governance, MDM, and metadata building blocks already exist—but remain fragmented? 

• How do you identify and prioritize the right AI agent use cases? 

• How can organizations build the underlying data foundation while still demonstrating near-term business value? 

• What operating model is needed to bring business, data, IT, and AI teams together? 

• How should organizations measure the value and ROI of Context Intelligence? 

• What can building an actual AI agent reveal about gaps in your enterprise data foundation? 

The discussion moves beyond the definition of Context Intelligence to the practical path forward: how to turn existing data management capabilities into a foundation that AI agents can actually use.