Artificial intelligence has reached a pivotal moment. In recent years, enterprises have rapidly embraced large language models (LLMs), trying out prompts, copilots and chat-based interfaces. These early experiments delivered some quick wins and enthusiasm, but they also exposed a fundamental issue: while AI can produce striking results, it still has difficulty functioning consistently within the complex realities of an enterprise. By design, large language models lack built-in context. They don’t inherently know your business, your customers, your internal rules, or the nuanced decision logic that drives your operations. They don’t retain prior workflows, and when crucial context is absent, they compensate with broad, generic assumptions. This is a major reason many AI pilots never progress beyond experimentation. The model may perform well in a sandbox, yet fail to integrate into a real business system. Prompting focuses on how humans interact with AI. Architecting—often referred to now as context engineering—focuses on shaping the environment in which AI functions. It moves attention away from crafting ever-better prompts and toward building an infrastructure that reliably supplies the right information, at the right moment, in the right format. Rather than just tuning outputs, organizations start engineering the inputs that ultimately drive those outputs. What Is a Context Graph? Traditional enterprise platforms like CRM, ERP, analytics tools and content management systems excel at documenting what occurred. They log transactions, interactions and events. However, they rarely capture the reasoning behind those events. Why was an exception granted? What triggered a customer escalation? Why did one marketing campaign outperform another? The explanations are often buried in Slack conversations, email threads,…