Treat AI as a Portfolio
The next AI discipline is deciding what gets funded, scaled — and killed.
For the last two years, most companies have treated AI as a collection of experiments. Try a model. Add a copilot. Build an agent. See what happens.
That phase is ending.
As AI moves from answering questions to executing work, every implementation starts consuming something more important than tokens: organizational resources. It needs access to systems, data, supervision, integration, maintenance and increasingly its own operating budget.
The useful unit of AI strategy is therefore becoming the portfolio, not the model.
Treat every significant AI implementation as an investment. Give it an owner. Define the process it improves. Measure its cost. Establish the outcome that justifies keeping it. Decide beforehand what would make you expand it, change it or shut it down.
This sounds obvious, but experimentation culture tends to produce the opposite. Successful pilots accumulate because nobody wants to kill them. Similar tools appear in different departments. Temporary integrations become infrastructure. Token consumption grows quietly. Nobody can quite say which systems are producing value and which are merely producing activity.
A simple discipline changes this.
