Observed Signal · Apr 9, 2026 · Strategy / Best Practice · Source: https://martech.org/feed/ · Impact: 3/5 · Sentiment: Positive
Use Context Graphs to Ground Enterprise AI
The article argues that enterprises should shift from prompt engineering to 'context engineering' by building a Context Graph — a living knowledge layer that connects customers, products, content and services with relationships, decisions, rules and outcomes. It explains that LLMs are context‑blind when isolated and that grounding models in a context graph improves factuality, explainability and decision quality. The piece outlines a seven‑step approach: define entities, capture decision intelligence, architect an AI‑ready stack, connect and unify systems (CMS, CDP, PIM, CRM), enable relationship‑aware retrieval and reasoning, build memory and continuous learning loops, and embed governance. It also highlights the Model Context Protocol (MCP) as a standard for interoperable model access and recommends graph‑based retrieval and policy layers to reduce hallucinations and operational risk.
Provides a practical, enterprise‑level architecture (context graphs, decision memory, MCP) that influences how organizations integrate LLMs into marketing, content and customer systems — relevant to MarTech and adtech teams building production AI workflows.
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Key Takeaways & Evidence Grounding
- Large language models (LLMs) are described as context‑blind and prone to fill missing enterprise context with generalized assumptions.
- A Context Graph connects entities (customers, products, locations, content, services) with relationships, decisions, rules and outcomes and preserves decision traces.
- The article presents a seven‑step approach to build a Context Graph: define entities; capture decision intelligence; architect an AI‑ready stack; connect systems; enable contextual retrieval and reasoning; build memory and continuous learning loops; embed governance and control.
- Model Context Protocol (MCP) is cited as a standard for securely connecting models to external databases, CMS platforms and APIs without custom integrations.
- Graph‑based retrieval and a decision memory layer are recommended to enable multi‑step, relationship‑aware AI reasoning and to reduce hallucinations.
Connected Companies & Entities
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Related Market Signals & Shifts
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Empowering AI: The Need for Marketing Decision Infrastructure
The article argues that marketing lacks the structured, machine-readable decision language found in engineering, which limits AI from meaningfully participating beyond assistive tasks. It proposes building a decision infrastructure—centered on "context graphs"—that captures decision logic, policies, approvals, inputs, exceptions and precedents alongside outcomes. Context graphs would connect entities (customers, campaigns, products, markets) to the rules and reasoning that shaped decisions, functioning as a complementary system of record to transactional systems. By preserving decision traces and making reasoning queryable, the author contends AI can better navigate brand nuance, regulatory constraints and trade-offs, accelerating collaboration and raising baseline quality without replacing human judgment. The article cites a Foundation Capital piece and notes Glean as a close practical example.
Designing AI Products for Context Management
The article argues that failures in large language model (LLM) outputs are often due to missing or poorly managed context rather than model capability. It describes a shift from prompt engineering to context design, where systems must store, scope, select, and update relevant context across interactions. The piece identifies three emerging design patterns implemented across major AI chat products: context containers (persistent project/notebook scopes), selective referencing (choosing which sources to include), and instructions (project- or system-level behavioral guidance). Examples cited include ChatGPT Projects, Claude Projects, Gemini NotebookLM, Copilot Notebooks, NotebookLM checkboxes, and Claude connectors. The author emphasizes that context must be curated and maintained over time, and that product and UX design play a central role in enabling more reliable, valuable LLM-driven workflows.
Context Memory Graph: Giving AI actionable marketing context
The article explains Context Memory Graphs (CMGs) as an architectural layer that supplies AI with business context for marketing decisions. A CMG links products, locations, content, customers, brand rules, live signals and decision outcomes so AI can detect what’s true today, recommend next-best actions, and remember why past decisions were made. The piece contrasts CMGs with schema, entities and traditional knowledge graphs, and outlines benefits across the marketing funnel — from campaign planning to personalization and conversion optimization — as well as governance, automation, and continuous learning. It positions the CMG as a connective intelligence layer that augments existing CRM, CDP, analytics and content systems without replacing them.
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