AI Adoption Is Being Measured in Tokens, but the Metric Falls Short, Experts Say

A growing number of companies are using a unit called the token to measure how much their employees and workflows use artificial intelligence, according to The Wall Street Journal. Companies that now regularly use AI are starting to track their workers’ use of tokens, AI’s unit of measurement.

Tokens are the foundational unit through which AI models process all information. Tokens are tiny units of data that result from breaking larger chunks of information into smaller pieces. AI models process tokens to learn relationships between them and unlock capabilities such as prediction, generation and reasoning.

For large language models, short words may be represented by a single token, while longer words may be split into two or more tokens. The word “darkness,” for example, would be split into two tokens, “dark” and “ness,” with each token bearing a numerical representation as explained by Nvidia.

Every prompt a worker sends to an AI system and every response the system returns are measured and often billed in tokens. Every prompt and response consumes tokens and incurs charges.

That direct relationship between usage and cost is what makes tokens attractive as a management tool. Unlike the seat-based pricing that defined earlier generations of enterprise software, token consumption is granular, real-time and tied directly to behavior.

Tokens Replace Seats as Adoption Proxy

The shift from seat counts to token consumption mirrors how enterprise AI spending itself has changed. While the unit price of AI tokens is falling, overall enterprise spending on and scaling of AI systems is rising. The number of users, complexity of models, and intensity of workloads will likely drive greater token consumption and, consequently, higher costs.

OpenAI’s own data on its enterprise customer base illustrates how dramatically usage patterns have shifted. Average reasoning token consumption per organization has increased by approximately 320 times in the past 12 months, suggesting that more intelligent models are being systematically integrated into expanding products and services. That figure has become a headline metric in the company’s internal reporting on adoption progress.

Nvidia CEO Jensen Huang went further at the company’s GTC conference this week, framing tokens as a new form of corporate currency. “I could totally imagine in the future every single engineer in our company will need an annual token budget,” Huang said, estimating that employee token allocations could reach half of base salary in value.

Volume Without Value

The appeal of token metrics runs into a fundamental problem: tokens measure volume, not outcome. Generating through packaged software abstracts tokens almost entirely, while consuming through APIs makes tokens explicit, but this can bring transparency and also volatility, as costs rise based on workload design, prompt length, and hidden choices of infrastructure providers.

A poorly structured prompt that forces the model to iterate, rephrase or regenerate a response will consume more tokens than a concise, well-targeted query, yet both may or may not produce useful output.

If an AI agent saves a customer service representative 15 minutes of work, but costs $4 in inference tokens to run, the ROI is negative, as explained by AnalyticsWeek.

The kind unit-economics mismatch is more visible as companies move from pilots to production deployments. As companies move from experimental chatbots to thousands of autonomous “agentic” workflows running around the clock, the sheer volume of tokens consumed has created a massive budgetary leak.

The dynamic draws comparisons to earlier enterprise metrics that proved easier to game than to interpret. Click-through rates once served as a proxy for advertising effectiveness; hours logged once functioned as a proxy for productivity. Both created incentives that diverged from the outcomes they were meant to track.

If token consumption becomes a performance indicator tied to employee evaluations, workers may optimize for AI interaction frequency rather than task quality. Knowing that “AI spend is up 40%” is not enough. Organizations need a single pane of glass that links every workload, tenant and token to their owners or business outcomes.

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