As AI adoption moves from experimentation to production, companies face rising costs tied to consumption-based pricing models, according to technologyreview.com. Deloitte’s 2026 State of AI in the Enterprise report highlights that worker access to AI increased by 5% in 2025, and the share of firms with at least 40% of AI projects in production is expected to double within six months. This shift is prompting enterprises to rethink AI economics beyond token prices and cloud model access.
The transition to steady, business-critical AI workloads means that consumption-only pricing can turn AI spending into a variable monthly expense that is difficult to forecast as usage and model requirements fluctuate. Enterprises now run AI as a portfolio of always-on workloads, including assistants, retrieval-and-knowledge systems, and agentic applications that execute multi-step workflows across systems. This recurring demand across models and data changes the cost dynamics significantly, moving the conversation from which model to consume to how to run AI economically and predictably at scale.
This evolution in AI deployment reflects broader trends in enterprise technology spending, where predictable costs and sustained scale are prioritized over flexible but unpredictable consumption pricing. As AI integrates into customer service, IT, research, and business processes, companies seek ways to manage AI as a strategic asset rather than a fluctuating expense. Deloitte’s findings underscore the growing maturity of AI adoption, with more projects moving into production and generating consistent demand.
Deloitte’s 2026 State of AI in the Enterprise report serves as a key benchmark, showing that the share of companies with at least 40% of AI projects in production will double within six months, signaling a significant shift in how enterprises budget and manage AI investments.