Lanai Launches Token Tuner to Turn AI Token Spend Into Measurable Business Value

New capability tracks AI usage across all enterprise departments, showing spend, leverage gained, and efficiency scores by department and workflow

Lanai, the enterprise AI accountability company, today announced Token Tuner, a new feature that helps enterprises understand where AI spend is happening, which workflows are driving results, and where lower-cost models can reduce unnecessary token costs.

Tokenmaxxing is becoming a new enterprise AI problem: teams are burning more tokens, using more models, and generating more AI activity. CFOs can receive AI bills that are 30% higher than the previous month and still lack visibility into what drove the increase or what outcomes were achieved. Token Tuner fills the missing context for enterprises by mapping token spend to workflows, model choices, efficiency, and value created. The new feature ties each AI interaction to a measurable outcome and generates a productivity score based on how well each user matched token usage and model choice to the task. For example, an employee using Opus 4.7 for email responses is likely to receive a lower efficiency score than if they used a smaller model for the task.

“Tokenmaxxing is real, it’s expensive and it’s spreading beyond just a few engineers or companies,” said Lexi Reese, co-founder and CEO of Lanai. “It is a vanity metric that looks like a measure of efficiency or progress but says nothing about net value. ‘Outcome-maxxing’ is the solution enterprises need now in order to see which workflows are actually improving productivity, accelerating decisions, and driving measurable outcomes. That’s exactly what Token Tuner does for enterprises using AI at scale.”

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Early customer analysis has shown significant differences in value generated across workflows, with some users identifying $50,000 to $150,000 in monthly waste within their first week from high-volume, low-value workflows that could run on lower-cost models with similar output quality. In beta, one Lanai user delegated 4.2% of all AI leverage hours across the organization while using only 0.7% of tokens. Their efficiency score was 6.0, indicating they were matching tasks to the right models, while others were burning 10x as many tokens for half the efficiency.

“Enterprises are using AI across engineering, sales, marketing, finance, and operations, but not every use case should be treated the same,” said Mohit Mehta, Chief Product Officer at Lanai. “A complex customer sentiment analysis workflow across Snowflake, Salesforce, and multiple MCPs may justify a premium model. Using that same expensive model for simple formatting, search or email validation usually does not. Token Tuner helps leaders see the difference, so enterprises can invest in the workflows that create value and adjust the ones that are simply burning tokens.”

Key features include:

• Workflow-level value visibility: Shows which teams, workflows, and use cases are driving AI spend and whether that usage is tied to measurable business value.
• Productivity and efficiency measurement: Compares token spend against leverage gained by user, team, and workflow to show where AI is creating the most value per dollar.
• Spend optimization recommendations. Identifies runaway workflows, mismatched tasks, and premium model usage for work that lower-cost models could handle.

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