Making AI an asset, not an expense

AI is moving from small-scale experiments to large-scale, business-critical applications in companies. This shift is reflected in Deloitte’s 2026 State of AI in the Enterprise report, which shows that worker access to AI rose by 5% in 2025. The report also predicts that the share of companies with at least 40% of their AI projects in production will double within six months. This transition means AI is no longer just a tool for testing but a core part of business operations, including customer service, IT, research, and business processes. As AI becomes a portfolio of always-on workloads, its economic impact changes, requiring companies to rethink how they manage and pay for it.

When companies use AI, they often pay for each interaction, known as token pricing, where costs are based on the number of words processed. While this consumption-based pricing offers flexibility, it can become unpredictable and expensive for steady, large-scale use. The article suggests that companies should consider shifting from this pay-as-you-go model to owning capacity, where they invest in dedicated infrastructure. This change makes sense when AI demand is steady and large enough to justify the upfront cost. The crossover point—where owning becomes cheaper than renting—varies by workload, model type, and usage patterns. For example, a simple AI assistant may have different costs than a complex system that retrieves large amounts of data for each query.

The choice between renting AI capacity or owning it depends on the specific workload. Some AI applications, like customer-service agents or research tools, may require continuous, high-volume use, making ownership more economical. Others, like occasional pilots, may still benefit from consumption-based pricing. The article emphasizes that this is not a simple debate between cloud (renting) and on-premises (owning) solutions. Instead, it’s a decision based on workload needs, expected demand, and the ability to keep capacity productive. Companies must model their actual workloads to determine the most cost-effective approach.

Owning AI infrastructure can reduce costs and provide more predictability in spending, but only if the capacity is fully utilized. The article highlights that ownership alone does not guarantee savings—it must be paired with an effective operating model. This includes bringing users and workloads into production quickly, governing AI use, and continuously identifying new high-value applications. Without this discipline, companies may fail to realize the economic benefits of their investment. The goal is to measure usage, identify underutilized capacity, and expand high-value workloads over time to maximize return on investment.

Before committing to owning AI infrastructure, leaders should ask three critical questions. First, is AI demand becoming steady, predictable, and large enough to justify dedicated capacity? Second, at what level of usage does ownership become more economical than renting? Third, can the company keep the capacity productive through adoption, governance, and expansion of use cases? These questions help determine whether shifting from a consumption-based model to ownership is the right move. The answers depend on factors like expected demand, workload type, and the company’s ability to manage and optimize AI resources effectively.