Over the past two years, many enterprises have taken a maximalist approach to AI adoption — launching more use cases, onboarding more employees, deploying more agents, and consuming more tokens. For a new technology, this was rational. Enterprises did not yet know which workflows would be transformed, which models were reliable enough or which employees would adopt the tool. The fastest way to find out was to deploy as many agents and run as many pilots as possible to figure out what worked.
But this year, the conversation changed. In nearly every executive review, the question is no longer “How do we get more AI?” or “Which model is smartest?” It’s “How do I generate impactful outcomes, while keeping AI costs under control?” Different companies have different answers. Uber tightened internal AI spending after consumption ran far ahead of plan. Anthropic, OpenAI and Google have aggressively cut input token prices and rolled out prompt caching. And Cursor, with Composer 2.5, now considers cost an important factor in model selection — not just performance.
