Tokens – the base units for measuring and pricing AI usage – have quickly become one of the most important metrics for corporations. Enterprise AI model pricing has shifted from static subscriptions to dynamic usage-based pricing, and now rising AI consumption has turned a productivity experiment into a potential source of margin pressure. CEOs are caught in a balancing act.
AI is not the kind of software tool that can be turned on and off at will. Over the last year, large language models have become deeply embedded across business processes. All signs indicate that reliance is here to stay. Indiscriminately restricting AI use now – like many executive teams are considering – will not only slow down the growth and efficiencies companies have gained, it will leave them less prepared to capitalize on the next generation of AI capabilities as experimentation becomes discouraged.
The risk is that executives fall into a trap of whipsawing their AI spend to balance the next quarterly budget, and in doing so miss out on the underlying transformation of their business that AI will create when governed systematically, not reactively.
We have entered the second phase of AI adoption, where the mandate has changed from rapidly demonstrating competency and progress, to now demonstrating companies can extract the greatest possible value out of AI without threatening their bottom line. This is the sustainable adoption phase, and it will soon expose a divide with long-lasting impacts in the corporate landscape between organizations that can manage and scale AI economically, and those that cannot. Token costs are simply the first visible symptom of a broader governance problem.
