The early stages of AI adoption focused primarily on getting organizations to adopt AI-enabled solutions such as copilots, agents, assistants and AI-powered workflows. The priority was demonstrating that AI had the potential to deliver value, not scrutinizing every dollar spent along the way. At the time, this made sense. There were relatively few people using AI, budgets were often funded through innovation initiatives and the cost of experimentation was low compared to the potential upside. Organizations treated AI spending as a learning experience, so finance teams had little reason to scrutinize every model call or workflow because the priority was learning what AI could do, not optimizing what it cost.

Full opinion : AI bills are growing fast, but many companies still can’t tell which workflows are driving costs or whether they’re worth it.