Use AI to Analyze AI Adoption Patterns

Why It Matters

By turning raw usage data into cohort‑specific playbooks, the AI solution empowers leaders to drive deeper adoption, justify investments, and report concrete metrics to the board.

Key Takeaways

• AI tool auto‑segments users into natural adoption cohorts
• Generates CSV, dashboard, and reusable Python script instantly
• Visual dashboard highlights total lines, completions, and tier usage
• Provides cohort‑specific guidance to promote users to super‑users
• Facilitates board‑level reporting on engineer adoption of Cursor

Summary

The video demonstrates an AI‑driven workflow that automatically identifies natural user cohorts—light, moderate, active, power, and super‑users—based on their interaction data. By invoking a "build mode," the system produces a CSV export, a simple HTML dashboard, and a reusable Python script, all in real time.

Key insights include the generation of a visual dashboard that breaks down total lines, composer lines, tap completions, and tier usage, offering a quick snapshot of adoption patterns. The presenter highlights a sample user, Gabriel Diaz, who exemplifies high engagement, and asks the AI to create explicit guidance for each cohort to help users graduate to super‑user status, effectively turning raw data into a playbook.

Notable remarks underscore the business relevance: "This is the kind of stuff you get asked to put in a board meeting" and the recurring question, "What percentage of our engineers are using Cursor? Do we have power users?" These statements illustrate the demand for clear, data‑backed metrics at the executive level.

The implications are significant: product leaders can instantly quantify adoption, tailor coaching for different user segments, and present concise, actionable insights to stakeholders, thereby accelerating tool uptake and informing strategic decisions.

Original Description

Chintan used Cursor itself to analyze Cursor usage data, identifying natural user cohorts from inactive to super users. This meta-application of AI allowed him to create targeted guidance for each segment, helping engineers progress from light usage to power usage. The analysis revealed that agent-heavy users were 16x more productive with AI than other users, providing concrete data to drive further adoption.