Context Engineering: How Context Makes or Breaks Your AI Success

AI initiatives often fail not because of inadequate models but because organizations lack accessible business context, with surveys indicating that few enterprises consider their data fully AI-ready and many struggle with data searchability and reuse. The discussion distinguishes between “locked library” knowledge in data silos, which can be made discoverable through connectors, cleaning, federation, and centralized catalogs, and the more difficult “unwritten book” of institutional knowledge that resides in employees’ experience and undocumented practices. Large language models can interpret information that is available to them but cannot reliably resolve conflicting definitions or create missing business meaning without human guidance, increasing the risk of arbitrary or inaccurate outputs. Context engineering therefore involves identifying gaps exposed by AI systems, eliciting knowledge from subject-matter experts, documenting it, and incorporating it into a semantic layer that provides consistent business definitions between raw data and AI applications. Combining federated access to data where it resides with ongoing human processes for capturing and maintaining institutional knowledge can help organizations build more reliable and trustworthy AI agents.

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