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As access to AI models and tools becomes widespread, the differentiator won’t be the model or platform, it will be an organization’s context: the real way work gets done through repeatable judgment calls, coordination patterns, escalation triggers, and trade-offs. That’s the authors’ core argument in this new article. They define context as “demonstrated execution – the workflows teams actually follow across systems, the signals they respond to, the order in which roles get involved, the exceptions that trigger action, and the judgment calls that repeat across real work.” Because AI models are general by design, they can flatten nuance and standardize behavior unless they’re grounded in this operating logic. The authors show how context calibrates decisions in the moment: the same subtle cues can warrant escalation in a speed-oriented culture but be routine noise in a risk-first one. Their recommendation is “context engineering”, building an enterprise context layer that captures how work unfolds and delivers the right context to AI at the moment of decision. They suggest four moves for doing so. One takeaway: don’t just deploy AI, spend the time building the context capability that turns the technical power of AI into differentiated competitive advantage. Bonus read: Why AI Adoption Stalls, According to Industry Data, which also addresses some of the contextual factors related to AI.
