As companies adopt AI, token costs become a significant, opaque expense. This creates a need for new financial tools that provide granular analytics to track, understand, and optimize AI spend across models, use cases, employees, and even per customer.

Most enterprise managers evaluate AI through cost savings and productivity gains rather than top-line revenue generation. However, companies that directly tie AI spending to concrete revenue growth anticipate increasing their investments, whereas companies failing to show direct business returns risk pulling back amid usage-based pricing.

True ROI of AI isn't found in usage metrics like token counts. It's measured by identifying entire, expensive projects (e.g., a $4M manual document conversion) and using AI to make the problem 'vanish,' completing the work in hours instead of months.

Superhuman's CEO advises against simply tracking AI costs, a practice he calls 'token maxing'. Instead, they evaluate the ROI of internal AI tools by measuring developer productivity metrics like feature delivery pace. This output-focused approach has doubled engineering velocity, justifying the AI spend.

The AI market has cleared its first ROI hurdle: model revenue has justified massive infrastructure investment. Now it faces a second, harder test. Enterprises spending billions on AI tokens must demonstrate tangible financial benefits, like higher margins or revenue, to sustain the flywheel.

According to Mike Cannon-Brookes, advanced enterprises are not tracking AI success by counting tokens. Instead, they are asking harder questions about overall output, such as engineering productivity and quality. They understand that high token usage doesn't always correlate with high productivity, shifting focus from raw usage to tangible business outcomes.

As companies spend billions on tokens, they will demand justification, similar to how law firms use the billable hour. Vertical AI startups can win by demonstrating the specific ROI of every token used for a business task, answering the question: 'Where's my ROI?'

When selling an AI platform to a CFO, go beyond abstract productivity gains. Calculate the direct cost savings from reducing token consumption on other, less efficient LLMs. This creates a powerful, easily quantifiable business case based on reducing existing AI spend, which resonates strongly with financial leaders.

The era of unmonitored enterprise AI experimentation is closing. While companies previously celebrated employees with the highest token consumption, Apter now audits heavy token users to demand demonstrable return on investment. CFOs must enforce governance and training around AI tooling to ensure spend drives measurable commercial returns rather than wasteful costs.