The real problem is not the monthly AI bill. It is proving what the models produced, who reviewed it, and how to classify that work inside finance systems built before token-metered labor existed.
The newest pressure on enterprise AI is not only model quality, but the cost, power, and water profile of every query, retrieval step, and vendor choice.
The real risk in enterprise AI is not the hardest prompt, but the most repeated one - because token burn, workflow frequency, and user count can multiply into a budget problem faster than managers expect.
Lower token prices do not automatically shrink AI budgets - they can push total compute demand, infrastructure pressure, and organizational dependence even higher.
New spend controls and usage analytics turn enterprise AI into something closer to a governed utility, with budgets, visibility, and limits built into the admin layer.