How to make AI work with context instead of precepts

Artificial intelligence is in a state of flux. In the past few years, businesses have rushed to use large-scale language models (LLMs), experimenting with commands, copy and chat areas. These early wins created excitement, but also revealed a deeper truth: while AI can produce impressive results, it still struggles to work reliably within business realities.

Large language models are context sensitive. They don’t understand your business, your customers, your policies, or the nuanced vision of a decision that delivers results. They don’t remember past workflows and when context isn’t there, they fill in the gaps with general guesses. This is why many AI pilots fail to scale. A model may work on its own, but it cannot work within an enterprise system.

Awareness is about interacting with AI. Design, or what is often called context engineering, is about shaping the environment in which AI works. It shifts the focus from writing better information to building a system that consistently provides the right information, at the right time, in the right format. Instead of improving outcomes, organizations begin to design the inputs that determine those outcomes.

What is a Content Graph?

Traditional business systems like CRM, ERP, analytics and content platforms are good at capturing what happened. They record transactions, interactions and events. But they rarely capture why decisions are made. Why are exceptions allowed? Why has there been a rise in the number of customers? Why was one campaign more successful than another? Those answers often reside in Slack threads, emails, unwritten workflows, or the minds of experienced users.

The context graph captures this missing layer. It connects entities such as customers, products, locations, content and services to relationships, decisions, rules and outcomes. More importantly, it preserves the traces of the decision: the thinking, the context and the differences behind the actions taken throughout the organization. Over time, this becomes a living system of institutional information that can use AI.

Context graphs help transform AI from a content generator to a decision engine. When AI is based on the context graph, it no longer depends only on standard training data. It works with the collective intelligence of your organization. It can think within the boundaries of your business, use presentations and respond in a more precise, explainable and actionable way.

How to Build a Content Graph: A Step-by-Step Method

Step 1: Define the business premise

The first step is clarity. Start by identifying the entities most important to your business: brands, products, locations, customers, services, teams and key goals. Then explain how these associations relate to each other.

This is the premise, because AI cannot think clearly in the face of ambiguity. If the business does not clearly define what the product is, how it differs from the service, or how the location connects to the product, the model will make assumptions. A strong business foundation gives AI the structure it needs to interpret meaning accurately. As you’ve written elsewhere, this is when business strategy becomes the backbone of AI visibility and business intelligence. The following four steps show how to create a business strategy.

Step 2: Capture the intelligence of the decision

Once the business foundation is ready, the next step is to capture how the business actually works. This means documenting not only the results, but also the thinking behind them. Why is the discount approved? Why is a policy exception made? Why did support raise the ticket? Why did one customer receive a different experience than another?

This decision stage is important because so much of a business lives with exceptions, judgment calls and operational fluctuations. Capturing these patterns turns everyday business behavior into organized memory. Over time, AI begins to learn from the actual history of decision-making rather than relying solely on abstract rules.

Step 3: Build an AI-ready stack

To make the context graph usable by AI, enterprises need an architecture that combines semantic meaning with operational intelligence.

At the core sits the data layer, where the knowledge graph models key associations and relationships. Above that is the decision memory layer, which captures decisions, reasoning and outcomes. The policy layer embeds business rules, compliance requirements and access controls. Then comes the agent layer, where AI systems think, discover and act against the graph. Finally, the integration layer connects this architecture with business systems such as CMS, CDP, PIM, CRM and workflow tools.

This structure is important because AI doesn’t just need data. It requires systematic definition, business logic and controlled access to act responsibly.

Step 4: Connect and integrate the systems

With the organization and decision logic defined, the next step is to connect the systems where this information resides. Content platforms, customer data platforms, CRMs, PIMs, DAMs, service platforms and internal information systems all hold pieces of the puzzle.

The goal is not to integrate everything into one monolithic environment, but to enable collaboration. AI needs a unified layer that can access signals, relationships and records across systems without losing meaning—allowing it to work more seamlessly across the enterprise than within isolated silos. To support this, organizations are increasingly adopting the Model Context Protocol (MCP), which acts as the ‘USB-C for AI,’ providing a standardized and secure way for models to communicate with external databases, CMS platforms and APIs without requiring custom integration for each system.”

Step 5: Enable situational recovery and reasoning

Traditional retrieval methods are insufficient for enterprise AI. Pulling classified pieces of text based on similarity may help answer simple questions, but misses relationships between words. This is why organizations are moving towards graph-based discovery and reasoning.

In this model, AI doesn’t just find a document. It understands how the customer relates to the product, how that product connects to a support problem and how that problem connects to an objective signal or business rule. This restoration of relational awareness enables critical, multi-step thinking and produces more appropriate and comprehensive responses.

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Step 6: Build memory and continuous learning loops

The context graph should not be stationary. It should be read continuously.

All interactions, decisions, adjustments and results must flow back into the system. This creates a living memory layer that gets richer over time. Instead of relying on people to constantly rewrite commands, the system evolves around the business. This is how organizations move from manual ordering to scalable, agent-based workflows.

Real time updates are important here. Streaming changes from systems like CMS, CRM, or commerce platforms help keep the graph up-to-date, so AI is always making the most recent state of the business, not yesterday’s snapshot.

Step 7: Embed governance and control

Sovereignty cannot be assumed. Brand rules, compliance requirements, permits and approvals should be built right into the architecture. If this layer is missing, AI fills the gap with standard internet information or consistent translation. This is where misperceptions, product drift and operational risk begin. When governance is encoded in the context graph, AI can operate within clear boundaries, respect access controls and represent the business accurately and consistently.

What makes a context graph work well

A useful context graph is not just connected; it is usable. It must be structured enough for the AI ​​to think about, present enough to reflect real-world changes and governed enough to be trusted. It should reduce ambiguity, capture institutional knowledge and improve with every communication. In other words, it should serve as the enterprise memory and intelligence layer on which AI depends.

Success in this new model is not measured solely by clicks, rankings, or quality of content. It is measured by whether the AI ​​is more accurate, more focused and more useful to the business.

The most important measures include retrieval accuracy, authenticity, decision quality, latency and business outcomes. Organizations must also track whether AI is improving over time, whether it’s using the right context and whether it’s reducing manual effort while maintaining trust and control. The efficiency of tokens and context pruning is also important because the right design should make the AI ​​smart and efficient.

Why this is important for business leaders

As models are commercialized, competitive advantage will not come from access to AI alone. It will come from the quality of the context the organization provides to that AI.

The winners will not be the companies with the smartest information. They will be the ones who create the richest, most organized, and ever-improving context layer. Their AI systems will be faster to adapt, better aligned with the business and harder for competitors to replicate.

That’s why the Context Graph is emerging as one of the strategic assets that a business can build.

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The transition from ordering to design is no small feat. It is a fundamental change in the way businesses use AI.

The future of AI will not be defined by who has the best model. It will be explained who owns and builds the context on which the model depends.

And that context, captured by a well-designed Content Graph, will be the intellectual layer that powers the next era of business growth.