You've heard the term. But what does a Context CoE actually do, what does it look like in practice, and why does it matter for your AI strategy? Here's a clear answer.
The Definition
Think of a Context CoE as the cross-organizational function that manages and curates your organization's knowledge for AI and human use alike. It's both an infrastructure layer and a team of experts ensuring that the context feeding your AI is complete, current, and correct.
In essence, it exists to make your institutional knowledge portable, composable, and governable across the enterprise. That means anyone human or AI can reliably reuse expertise under the right conditions.
The Gap It Fills
The Context CoE operates in the space between your data governance team, your knowledge management function, and your AI team, filling the gap that none of those groups fully covers today, especially not when it comes to AI.
Data governance owns data quality, schemas, and access controls. Knowledge management owns documents, storage, and retention. Your AI team owns models and implementations. None of them own what the AI actually knows or whether that knowledge is accurate, current, and structured for AI consumption.
The Context CoE is the committed team that makes data meaningful, content usable by AI, and AI behavior reliable. It's an internal capability your organization builds, not an external consultant you pay indefinitely. The goal is to build quality in from the start through disciplined context curation, rather than constantly play catch-up fixing AI outputs after the fact.
Without it, context left uncollected and unorganized causes AI to struggle to understand your organization and act inconsistently. Left unmanaged, context fragments, drifts, and contradicts itself, causing AI systems to become unpredictable and unsafe.
The Quality Principle
There are only two ways to produce high-quality solutions. Build the solution and then rework the defects. Or build quality in from the beginning. The first approach is expensive, slow, and demoralizing. The second requires discipline, specifically the discipline to industrialize language and remove variation before AI development begins.
AI success starts before AI development. And continues after development.The Context CoE exists to ensure this discipline is systematic, not heroic.
How it Works In Practice
Standing up a Context CoE can sound abstract. Here's the concrete framework developed at The Kendall Project, four key tools that work in concert to keep context in control.
Context 360°
Diagnose firstA structured discovery process run at the start of every AI initiative. Short, focused sessions, called Context Sprints, surface who knows what, where knowledge lives, and where definitions diverge across teams. If three departments describe the same process differently, you catch it before it gets baked into the AI. Misalignment is identified and resolved before any development begins.
Context Blocks
Modular knowledgeThink of these as the LEGO® bricks of enterprise knowledge. Each Context Block is a standardized, version-controlled, bite-sized piece of information, a policy, a role definition, a procedure with a clear owner responsible for its accuracy. Knowledge becomes composable: the right pieces can be assembled for different AI applications, and every block is traceable back to who provided it and when.
Context Warehouse
Governed repositoryAll Context Blocks live here, an enterprise knowledge hub built specifically for AI consumption. Unlike a typical document repository, the Context Warehouse enforces quality: only curated, approved blocks go in, tagged with security classifications, provenance info, and relationships to other blocks. Think of it as a constantly maintained single source of truth. Not a static wiki, a living data warehouse for tribal knowledge, with pipelines to keep content fresh.
AI Bill of Materials
Scoped by designJust as engineers create a Bill of Materials to build a product, an AI BoM defines exactly which Context Blocks a specific AI application is allowed to use. A customer service chatbot gets the refund policy, product catalog, and troubleshooting guides and nothing else. The AI isn't rummaging through everything; it operates within a defined, vetted context. AI governance by design, not by hope.
Continuous Oversight
The CoE doesn't just build the system and walk away. Context Curators embedded in each business unit are responsible for regularly reviewing and updating Context Blocks, improving how AI retrieves information, confirming source approvals, and tracking output quality metrics.
The CoE establishes dashboards and context KPIs, for example, what percentage of an AI's answers can be traced to an approved source, or how often users flag an answer as wrong. When a policy owner leaves the company, there's an established handoff process so nothing falls through the cracks.
It's not about adding red tape. It's about creating a fast lane for AI projects by removing the roadblocks, the unknowns and inconsistencies upfront.
The result: organizations with a Context CoE can launch new AI capabilities 40–60% faster, because context is already curated and ready rather than scrambled together each time. And they aren't stuck in endless review cycles because the traceability and context governance provide compliance assurance from day one.
Learn more: Download the white paper on the Context Center of Excellence Imperative