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Sendbird tracks and ranks every employee's daily AI token usage on a public dashboard, categorizing them from 'AI Newbie' to 'AI God.' This gamified metric makes AI adoption a visible, shared company objective and identifies who needs enablement.

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By ranking engineers on AI token consumption, Meta is experiencing Goodhart's Law: "When a measure becomes a target, it ceases to be a good measure." Employees reportedly build bots to needlessly burn tokens for status, demonstrating how gamifying a proxy metric can backfire and disconnect from actual business impact.

To move beyond mandates, Salesforce provides leaders with a dashboard showing exactly which employees are using approved AI tools and how often. This data-driven approach allows managers to pinpoint adoption gaps and diagnose the root cause—such as skill versus will—for targeted intervention.

When companies measure AI adoption by counting tokens used, it creates a perverse incentive. Employees and their teams create agents to perform pointless tasks simply to boost their metrics, leading to fake productivity and problematic artifacts.

To make AI adoption tangible, Zapier built rubrics defining "AI fluency" for different roles and seniority levels. By making these skills a measurable part of performance reviews and rewards, you create clear incentives for employees to invest their time in developing them, as behavior follows what gets measured.

To ensure a return on massive AI investments, companies like Disney are gamifying employee usage with streaks, leaderboards, and badges. This creates "prompt pressure": a new form of workplace dynamic that strongly encourages, and implicitly requires, employees to integrate AI into their workflows to boost productivity.

A trend called "tokenmaxxing" is emerging in Silicon Valley, where companies like Meta use leaderboards to track employee AI token usage. This reflects a corporate bet that higher token consumption correlates with increased productivity, turning AI usage into a new, albeit gameable, performance metric for engineers.

Gamifying AI token consumption via internal leaderboards, as seen at Meta, creates perverse incentives. Employees may burn tokens to climb the ranks rather than to solve real business problems. This "tokenmaxxing" promotes conspicuous consumption of compute, a vanity metric that masks true productivity and ROI.

Some large companies are incentivizing employees to use the maximum amount of AI tokens, even ranking them on usage. This seemingly inefficient strategy is a deliberate investment to accelerate adoption. The goal is to retrain employee thinking to be "AI native" before optimizing for cost and efficiency.

Sendbird created an internal platform where employees post 'quests' for AI tools. This marketplace connects needs with builders (engineers or AI-enabled staff) and even AI agents, bypassing slow prioritization processes and fostering a building culture.

To accelerate its internal AI transformation, Meta is now grading employees on their use of company-provided AI tools as part of their performance reviews. This tactic moves AI from an optional productivity enhancer to a mandatory part of the job, creating powerful incentives for adoption and cultural change across the organization.