Foundations · Enterprise AI, from first principles.
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.
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.
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.
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.
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.
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.