Foundations

Enterprise AI, from first principles.

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1. What Makes AI Different From Conventional Software

Conventional software follows rules a person wrote and fails predictably. AI learns patterns from data and fails in ways nobody anticipated.

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2. AI Models: What They Are and How Quickly They Date

A model is a trained system that maps inputs to outputs. It is also the fastest-depreciating choice a vendor makes, which is why it tells you little.

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3. Where an AI System Gets Its Answers From

Retrieval-augmented generation lets a system answer from documents you can open, rather than from patterns absorbed in training. The difference is auditability.

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4. Agentic AI: What Changes When Software Acts

Reactive systems inform. Agentic systems pursue a goal and act inside your workflows. The hard part is the mandate, not the autonomy.

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5. What Happens to Your Data Inside an AI System

Most enterprise AI deployments do not train on your data. They supply it as context at the moment a question is asked, and the model itself does not change.

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6. Where Enterprise AI Creates Value

Returns pool where three conditions coincide: documents plus tacit judgement, senior bandwidth as the bottleneck, and knowledge loss that is expensive.