A bank receives an instruction to move $4.8 million from a corporate treasury account. The request does not come directly from a human user. It comes from an AI agent operating inside the company’s finance stack.

When you walk into most enterprise AI conversations today, the discussion starts with the model. Is it smart enough? Is the demo impressive enough? Does the feature set cover enough ground?

One of the most overlooked questions in an AI SaaS evaluation is not “what features are included.” It is “what happens when we need this vendor to stop?”

As frontier models converge for a growing class of work, raw AI capability explains less of the difference between products. That is a narrower claim than it sounds.

A demo only has to impress once. An AI product has to work every day. The first version looks magical in a conference room: it answers the clean prompt, completes the happy path, and suggests that broader autonomy is one launch away.

The easier it gets to add AI, the more valuable it becomes to know where AI does not belong.

Most teams no longer struggle to access models that can summarize, draft, classify, route, recommend, answer, and act well enough to produce impressive demos.

Many companies are not building AI automation capabilities. They are rebuilding the same hidden machine in different departments.

Finance funds an invoice automation project. Legal buys contract AI.

When you work on AI in a regulated environment, you will hear the same question again and again: can the model say the same thing twice?

When you are responsible for putting AI agents into production, you will face a governance question that sounds like security but turns out to be about operating standards.

When you watch a customer support agent resolve tickets end to end in a demo, the workflow looks complete. It reads the complaint, checks the account, drafts the response, applies the credit, and closes the case.