Making AI an asset, not an expense

As artificial intelligence transitions from experimental pilots to production-grade enterprise portfolios, businesses are facing a shift in cost dynamics. MIT Technology Review explores the limitations of consumption-based pricing models, which often lead to unpredictable monthly expenses as AI usage scales. The article argues that for organizations with steady, high-volume workloads, shifting from a pay-per-request model to owning and optimizing dedicated infrastructure can offer better economic predictability. However, this transition requires a strategic approach to capacity planning, as ownership only becomes cost-effective once a specific utilization threshold is met. Beyond the hardware and infrastructure investment, the author emphasizes that long-term value depends on a robust operating model that governs AI usage, ensures high utilization, and aligns technology deployment with specific business outcomes. Ultimately, the piece encourages leaders to treat AI as a core strategic infrastructure investment rather than a variable operational cost.