Why scaling AI requires a new economic strategy

The excitement over enterprise AI's potential has shifted to the harsh reality of production as companies grapple with deploying models. Organizations are finding out that the issue isn't AI itself, but the assumption that every task needs the most powerful models. This over-reliance, dubbed "tokenmaxxing," results in inflated infrastructure costs that outpace the output's value.

Enterprises are experiencing the financial consequences of this approach, with some burning through AI budgets quickly and canceling internal licenses. The core issue lies in comparing the success of isolated pilots to enterprise-wide usage, as the latter brings unpredictable costs due to the probabilistic nature of AI. Traditional software has fixed costs, while AI costs fluctuate, making it hard for finance departments to forecast expenses.

This unpredictability leads to 80% of enterprises missing AI cost forecasts by more than 25%, and over 95% of GenAI pilots failing to reach production. Instead of merely choosing the right model, organizations should focus on optimizing their workflows. Current cost optimization strategies rely on selecting the appropriate model for each task, but a deeper solution involves optimizing the work itself.

By decoupling the process from specific AI models, organizations can distribute computation across specialized steps, improving resource utilization and overall economics. This modular, process-driven architecture allows organizations to switch between different models as needed without rebuilding their infrastructure. As repetitive cognitive tasks are automated, employees can focus on high-judgment tasks, transforming AI from a capped experiment into a standard means of performing work.