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From Intent to Sovereignty: Designing the AI-Native Enterprise Operating Model
From Intent to Action: Designing the Executable Operating Logic of the AI-Native Enterprise
When AI Acts: Designing Authority, Accountability, and Failure in Agentic Systems
Grounded Intelligence: Enterprise Architecture for the AI Era
From Intent to Sovereignty: Designing the AI-Native Enterprise Operating Model
Enterprise AI fails not from weak models, but from ambiguous intent. This piece explores how executable logic, grounded truth, and earned authority transform AI from experimentation into sovereign, controllable action.
From Intent to Action: Designing the Executable Operating Logic of the AI-Native Enterprise
Scaling AI safely isn’t about better agents, it’s about sequencing truth, logic, and authority. This post introduces Executable Operating Logic as the missing layer between intent and action.
When AI Acts: Designing Authority, Accountability, and Failure in Agentic Systems
The moment an AI system can execute a transaction, it becomes an organizational actor. This article explores how to design authority, failure recovery, and accountability into agentic systems, before autonomy outpaces governance.
Grounded Intelligence: Enterprise Architecture for the AI Era
The future enterprise is neither fully automated nor manually controlled. It is architected, where deterministic systems preserve truth, probabilistic AI proposes action, and humans arbitrate when confidence and risk diverge.
Compression Is Not Cognition
Large Language Models achieve fluency by predicting the next word, but fluency is not understanding. Language is evidence of intelligence, not its source, a compressed projection of cognition. Trained on text alone, models learn patterns of expression, not the causal structure of the world.