The cheap part is finished
For most of the last decade, the hard part of applied AI was intelligence: reading a document, classifying a case, predicting a number well enough to trust. That problem is now commoditised. A capable model is a line item every competitor can buy.
If the model is a commodity, a product whose entire value is access to a model is a commodity too. Durable advantage has moved somewhere else.
Where the value actually sits
Work is not made of documents. It is made of decisions, thousands of small repetitive ones, most currently mediated by a human copying context from one system into another.
The distance between a model producing a correct answer and a business having taken the correct action is where organisations lose the value. That distance is made of unglamorous things: permission, context, sequencing, side effects, reversibility, audit, and someone willing to be accountable for the outcome.
Closing that distance is an engineering and governance problem, not a modelling one. It does not commoditise, because it is specific to the decision, the data and the obligations of the organisation making it.
Why 'autonomy with evidence'
Once software acts, the question changes from 'is the answer good?' to 'should this have happened, and can we show why?' Systems that cannot answer that do not get to keep acting.
Evidence is the mechanism that lets autonomy expand, not a compliance tax bolted on at the end. Each proven loop earns the next increment of scope: understand, decide, act, prove, learn, in that order.
“An answer changes what someone knows. An action changes what the business has done. Only one needs governance, and only one creates value on its own.”