Making AI an asset, not an expense
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…
When businesses consider the costs of AI, they often focus on token prices and accessing the most advanced models in the cloud. However, this approach may not always be the most economical. As AI transitions from experimental use to production, the decision around model selection becomes just one factor. For steady and critical demands, a consumption-only strategy can result in unpredictable, difficult-to-forecast AI expenses.
The true question shifts from choosing a model or provider with the lowest token price to managing AI economically, predictably, and at scale.
AI is progressing from isolated pilots to production portfolios, including assistants, retrieval-and-knowledge systems, and agentic applications. These systems execute multi-step workflows across enterprise systems, leading to recurring demand for various models, data, and tools. For instance, Deloitte's 2026 State of AI in the Enterprise indicates that worker access to AI rose by 5% in 2025, and the share of companies with at least 40% of their AI projects in production is projected to double within six months.
When AI becomes a portfolio of consistently running workloads, the economics change. While consumption pricing offers flexibility and limits commitment, when usage becomes steady and substantial, leaders must reconsider whether buying AI one request at a time remains cost-effective. This decision is not a cloud versus on-premises issue; it is a workload-by-workload business decision.
Over the next 12 to 18 months, enterprises should assess their expected AI demand and usage consistency. When multiple workloads share infrastructure, the enterprise can distribute fixed costs across more productive use, enhancing the economics of ownership.
Ownership is not inherently the lower-cost option. It only makes sense when an enterprise can maintain capacity productivity. Each organization has a specific crossover point— the level of sustained use where owning capacity becomes more economical than purchasing it request by request. This point varies depending on the models used, input and output token balance, performance requirements, system design, energy costs, and the operating model to support it.
Retrieval-heavy knowledge systems and agentic workflows, for example, may have different cost profiles due to the varying amount of context processed per interaction and the complexity of tasks involved.
Therefore, generic cost benchmarks are insufficient. Enterprises must model their actual workloads, understand expected demand, and size capacity accordingly. At the right utilization level, the benefits extend beyond lower effective costs. They include greater predictability, enabling AI capacity to be managed as a strategic infrastructure investment rather than a subject to fluctuating monthly spend lines due to varying model usage and workload demands.
The capital decision is only the first part of the equation. Even when the economics support ownership, capacity becomes valuable only when the business deploys workloads quickly and maintains their operation. This requires more than infrastructure installation; it demands an operating model that integrates the technology into adoption and business outcomes.
It involves engaging users and workloads, governing AI usage, monitoring utilization, and continuously identifying and integrating new high-value use cases. Without this discipline, the business may not realize the economic value justifying the initial investment. With it, AI capacity becomes a productive asset the business can optimize, expand, and leverage to generate measurable value.
Before committing capital, leaders should consider three key questions: Is demand becoming steady, predictable, and substantial enough to warrant dedicated capacity? At what usage level does ownership become economically advantageous? Can we ensure the productivity of this capacity through adoption, proper governance, and ongoing use-case expansion? By approaching this shift deliberately, organizations can transform AI from an expense into a strategic asset, driving measurable value for the business.
Written by urgent.news from MIT Technology Review's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
