Making AI an Asset, Not an Expense

The conversation around AI costs is evolving beyond token pricing and cloud access as the technology moves from isolated pilots into production portfolios. Enterprises are now deploying AI for customer service, IT, and business-process agents that execute complex, multi-step workflows, creating a recurring and steady demand for compute resources. While consumption-based pricing offers flexibility for experimentation, it can lead to unpredictable monthly expenses as usage scales. According to Deloitte’s 2026 State of AI in the Enterprise report, worker access to AI increased by 5% in 2025, and the number of companies with at least 40% of their AI projects in production is expected to double within the next six months. As AI workloads become 'always-on' assets rather than temporary experiments, the underlying economics change. Business leaders are now questioning whether it remains economically viable to purchase AI on a per-request basis or if it is time to invest in dedicated capacity that offers better optimization and cost control. This shift represents a strategic business decision rather than a simple infrastructure debate. Companies must now forecast their AI demand over the next 12 to 18 months to determine how to run these systems economically and predictably at a sustained scale.