Enterprises can count every token their AI agents consume. Almost none can say what they got back. Three ideas change that.​

Effort shifts from maintaining thousands of individual forecasts and “like item” rules to deciding how the model understands the business.

These identities are dangerous because their authority comes from the relationships between systems.

A new generation of AI systems is beginning to interact with the physical world through robots capable of translating high-level goals into actions.

Sandbox thinking assumes the danger is code breaking out, but agents cause harm in other ways.

Most AI platforms are optimized to be frictionless, which is a problem because it feels the same whether the output is right or wrong.

When an agent picks your capability on a user’s behalf and runs it inside another company’s workflow, the integration is the product experience.

The real challenge for leaders is figuring out where AI investments create meaningful business outcomes and connecting AI spend to those results.

The move from co-pilots that answer questions to agents that plan work, call tools and more creates both opportunities for process redesign and new operational risk.

The phrase “customer data” hides several materially different classes of information.