Making AI an asset, not an expense
2026-09-29 · MIT Technology Review
Making AI an Asset, Not an Expense
The Economic Shift from Experimentation to Production
When customers discuss AI, the conversation typically begins with token costs and ends with accessing the latest, most capable cloud models. However, as AI transitions 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. At that stage, the question is no longer simply which model to consume or which provider offers the lowest token price, but how to run AI economically, predictably, and at a sustained scale.
AI is moving from isolated pilots into production portfolios, including assistants, retrieval-and-knowledge systems, and agentic applications. Customer-service, IT research, and business-process agents can execute multi-step workflows across enterprise systems, driving recurring demand across models, data, and tools. Deloitte’s 2025 State of AI in the Enterprise report reflects what many leaders are experiencing: worker access to AI rose by 5%, and the share of companies with at least 40% of their AI projects in production is expected to double in six months.
When AI becomes a collection of always-on workloads rather than experiments, the economics change. Consumption pricing offers flexibility, but when usage becomes steady and large enough to maintain capacity, leaders must ask if it still makes economic sense to buy AI one request at a time, or if it is time to invest in capacity they can optimize and control.
A Workload-Driven Business Decision
This is not an abstract cloud-versus-on-premises debate; it is a workload-by-workload business decision. Over the next 12 to 18 months, enterprises must evaluate their expected AI demand and capacity utilization consistency. When multiple workloads share capacity, the enterprise can spread fixed costs across more productive use, improving the economics of ownership.
The question is how much you run. Ownership is not inherently the lower-cost option; it only makes sense when an enterprise can keep capacity utilized. Every organization has a crossover point—the level of sustained use at which owning capacity becomes more economical than buying per request. There is no universal number. It depends on the models used, the balance of input and output tokens, performance requirements, design, energy costs, and the supporting operating model.
For instance, a knowledge-heavy system processing vast context will have a different cost profile than a simple assistant. Agentic workflows also differ: a single task might involve multiple reasoning steps, retrievals, model calls, and tool uses. Consequently, generic benchmarks are insufficient. Enterprises must model their actual workloads, understand the demand, and size capacity accordingly. At the right utilization level, the benefits include not only a lower effective cost but also greater predictability—treating AI capacity as a strategic infrastructure investment rather than watching monthly expenses fluctuate.
The Operating Model and Value Realization
The capital decision is only half the equation. Even when economics support ownership, capacity creates value only when workloads are moved into production quickly and kept running. This requires more than infrastructure; it demands an operating model that connects technology to adoption and business outcomes. This means onboarding users and workloads, governing AI usage, monitoring utilization, and continually identifying the next high-value use case.
The goal is to create value early and build on it. This involves measuring usage, identifying underutilized capacity, and bringing additional high-value workloads to the platform. Without this discipline, the business may never realize the economic value that justified the investment. With it, AI capacity becomes a productive asset the business can optimize, expand, and use to create measurable value.
Three Questions for Leaders
Before investing capital, leaders should ask three questions:
1. Is demand becoming steady, predictable, and large enough to justify dedicated capacity?
2. At what level of usage does ownership make economic sense?
3. Can we keep that capacity utilized through adoption, governance, and continued case expansion?
Conclusion
As AI moves into production, organizations that create the most value will look beyond token prices and the latest models. They will recognize when recurring demand calls for a different economic model and possess the operating discipline to make that capacity productive. That is when AI stops being an expense and becomes an asset.