Prompt Engineering Is Dead. Context Engineering Is Dying. What Comes Next Changes Everything.

ONE SENTENCE SUMMARY

Organizations must adopt intent engineering, encoding goals and values in AI infrastructure, because context alone yields speed without organizational value.

MAIN POINTS

1. CLA’s AI saved $60M but damaged customer relationships and reputational trust.
2. AI success at the wrong objective underscores need for organizational intent alignment.
3. Intent engineering encodes goals, values, and tradeoffs into machine-actionable parameters.
4. Context engineering is necessary but not sufficient without intent engineering.
5. Investment in AI is massive, yet tangible value remains uneven.
6. Microsoft Copilot adoption didn’t scale; majority failed to deploy broadly.
7. Three layers of the intent gap: unified context, coherent toolkit, and intent proper.
8. Layer One creates a unified context infrastructure to avoid shadow IT.
9. Layer Two demands transferable AI workflows and a coherent worker toolkit.
10. Layer Three defines machine-readable intent and explicit decision boundaries for agents.

TAKEAWAYS

1. Intent engineering aligns AI decisions with long-term organizational goals and values.
2. Context alone boosts speed but may misalign if organizational intent isn’t encoded.
3. Unified context infrastructure and shared workflows enable scalable, responsible AI adoption.
4. The shift from prompts to intent is an organizational governance challenge, not only a tech one.
5. OKRs are insufficient; machine-readable intent layers must govern agent behavior and trade-offs.