“So when the super agents call subagents, a lot of them are small language models as well,” he says, “And we’re saving 90% of the cost.” Plus, AT&T negotiates price concessions with its vendors, he says. “We’re getting more mature on this.”

But most companies are lagging when it comes to AI cost control. McKinsey found that AI spending increases nearly four-fold as enterprises move from isolated use cases to widespread AI adoption, and a vast majority of organizations report exceeding their AI budgets.

The problem is that AI usage patterns aren’t predictable, governance is immature, teams don’t know which models or tools they should use, and new AI development tools allow employees to build new applications, workflows, and autonomous agents with little effort — and little oversight.

There are many enterprise processes where cutting-edge AI — or any AI at all for that matter — isn’t the solution.

“I don’t need a non-deterministic airline ticketing system or bank app experience,” says Nicholas Mattei, chair of the ACM special interest group on AI and professor at Tulane University. “There are places where agentic workflows are appropriate and mission-critical processes where they’re not. AI isn’t going to speed up your point of sale system.”