Making AI an asset, not an expense
Making AI an asset, not an expense
摘要: Sponsored
Artificial intelligence
Making AI an asset, not an expense
As AI moves from pilots to production, enterprises must decide when consumptio
正文
Sponsored
Artificial intelligence
Making AI an asset, not an expense
As AI moves from pilots to production, enterprises must decide when consumption pricing still fits—and when AI infrastructure should be treated as a productive asset.
Cheri Williams
archive page
September 29, 2026
Provided by
When customers talk about AI costs, the conversation usually starts with token prices and ends with access to the latest, most capable model in the cloud. Do they always need that level of capability? Not necessarily. But that is often where the conversation goes.
As AI moves from experimentation to production, model choice is only part of the equation. When demand becomes steady and business-critical, a consumption-only approach can turn AI spending into a variable monthly line item that is difficult to forecast as usage, workloads, and model requirements change.
At that point, the question is no longer simply which model to consume, or which provider offers the lowest token price: It is how to run AI economically, predictably, and at sustained scale.
AI is moving from isolated pilots into production portfolios: assistants, retrieval-and-knowledge systems, and agentic applications. Customer-service, IT, research, and business-process agents can execute multi-step workflows across enterprise systems, creating recurring demand across models, data, and tools.
This is already starting to happen. Deloitte’s 2026
State of AI in the Enterprise
reflects what many leaders are seeing: worker access to AI rose 5% in 2025, and the share of companies with at least 40% of their AI projects in production is expected to double within six months.
When AI becomes a portfolio of always-on workloads, not a collection of experiments, the economics change. Consumption pricing gives teams flexibility and limits commitment. But when usage becomes steady, predictable, and large enough to keep capacity productive, leaders need to ask a different question: Does it still make eco