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.

Two assumptions led to this inflection point. The first was an appealing but not wholly accurate thesis on what AI would allow businesses to do: swap human labor costs for model costs and capture the efficiency delta as profit. That tradeoff has not been as straightforward. Many organizations that leaned into it, reducing headcount in anticipation of AI-driven productivity gains, have found they shed institutional knowledge and critical engineering talent needed to effectively integrate and refine AI systems over time.

That realization is hitting many companies that conducted large-scale layoffs. In a recent high-profile example, Ford said it must rehire hundreds of engineers to address quality control issues with newly implemented AI tools. Executives directly cited the lack of veteran expertise negatively impacting product development and limiting efficiency gains from autonomous systems.

The other assumption was a misalignment between investors and management teams. In Teneo’s most recent annual CEO and investor survey, 53 percent of investors expected return on investment from AI within six months, while only 16 percent of large-cap CEOs believed they could deliver on that timeline. That deadline has now arrived.

The window to build disciplined AI governance is open, but it will not stay open indefinitely. Between our two firms, we engage with thousands of CEOs and boards globally to address these challenges. Here is what we are advising them. 

First, reframe the AI conversation in the boardroom. Stop asking how much you are spending on AI. Start asking where AI investment is creating durable competitive advantages, and where it is generating consumption without compounding value. 

Second, treat AI spend as a capital allocation decision, not an IT budget line. Usage that drives new revenue, creates differentiated customer experiences, or builds proprietary capabilities is a growth investment. Usage that automates low-value processes is an operating expense. 

Third, establish governance mechanisms that match workloads to the least expensive model capable of performing them reliably. Employees will naturally gravitate toward the latest and greatest models, even when older generations can produce the desired output. One implementation may be a software layer that can intake prompts and automatically route them to the appropriate model.

Fourth, reshape incentives around AI use. Employees should not be rewarded for using AI the most, which was an early instinct among companies eager to show investors how advanced their adoption was. Nor should they be penalized with blunt usage caps, because that can inadvertently stifle innovation. Instead, organizations should reward efficient AI use: achieving better business outcomes with the appropriate level of AI consumption. The goal is not maximum usage, but maximum value per token.

Fifth, distribute AI governance throughout the organization. AI governance cannot be delegated to a single role like a Chief AI Officer. Managers across functions need to be accountable for guiding sustainable AI adoption within their teams. Employees, in turn, need practical support both to understand how their AI use is evaluated and to develop the skills required to meaningfully and efficiently contribute with AI in the long-term.

The companies that win this phase of AI adoption will not be those that use it the most or those that spend the least. They will be the ones that govern it best, consistently converting AI consumption into lasting economic advantage. It is a difficult balance, especially with a technology evolving this fast. Yet, getting that balance wrong may soon be existential.

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