The Context Problem

While the last generation of enterprise software perfected the system of record, context has emerged as a new opportunity. This shift toward the "systems of context" seeks to turn raw data from all of those siloed systems of record into the autonomous, decision-ready intelligence required for the age of agents.

A system of process defines how work gets done, encoding workflows, transactions and operating steps. These systems are essential for scaling repeatability and control. However, a system of context defines what's true and how it all connects. It gives AI a unified representation of the concepts, entities and policies that matter to the business. Raw data tells an AI that a record exists; context tells the AI what that record means, how it relates to others and what action is allowed.

Moving AI Out Of Pilot Purgatory

AI initiatives remain stuck in pilot purgatory because the context layer is missing. Enterprise data remains fragmented across hundreds of systems, with customer, product and supplier data siloed and labeled differently.

This gap becomes dangerous as we move from generative AI to agentic AI. Generative AI can tolerate some imperfection because a human is usually in the loop to review a draft. Agents, however, don't just generate content—they take action, trigger workflows and coordinate across systems. When an agent acts on incomplete or contradictory information, it becomes a business liability.

Beyond "AI-Ready" Data

Making data AI-ready—clean, deduplicated and well-governed—is a basic necessity. Context goes further: It's multisource and interconnected. It adds the relationships and rules that enable AI to reason and rationalize cause-and-effect across the business, rather than just query isolated facts. It makes the business understandable to AI, not merely accessible.

For years, master data management (MDM) was framed as the effort to create a single source of truth. That mission remains important. In fact, it's more important than ever. But in the era of agentic AI, a single source of truth is no longer the finish line. It's the foundation. The next step is to create a system of context: a living, connected, trusted representation of the business that AI can interpret and act on. That isn't a rejection of MDM. It's an elevation of it. First, enterprises had to unify data. Now they must complement it with unified meaning.

The Power Of Relationships

Business context doesn't live in flat tables—it lives in relationships: customer to household, supplier to product or contract to obligation. AI can only perform the kind of multihop reasoning enterprises expect when that context is represented in a form it can traverse, such as a graph-based approach.

When you combine a system of process with a system of context, enterprise AI becomes practical. A procurement agent shouldn't just read a supplier file—it should understand supplier risk and contractual constraints before recommending an alternative source. A change to customer data shouldn't remain trapped in one application—it should harmonize consistently across the business.

AI Needs To Understand Business Reality​​

Agents excel at crunching large volumes of information quickly, multitasking and taking orders on what to do next. They can learn and improve their own processes over time. The agentic promise for knowledge workers is immense—they'll transform how work gets done and at a scale and speed never before imagined.

They can’t, however, fulfill on those promises without a trusted roadmap of an enterprise or the full context of how it operates. In today’s large enterprises, that could mean traversing hundreds of data systems and thousands of different processes. That’s why organizations that build a singular system of context and a unified system of process will win the agentic era.

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