Enterprise leaders lacked a reliable view of how employees were using AI across departments.
Activity was scattered across approved tools, personal accounts, embedded AI features, and internal agents. They couldn’t clearly see which tasks were driving usage, where company data might be exposed, or which workflows warranted further investment. Our client, an enterprise AI company serving Fortune 100 organizations, needed the collection and reporting infrastructure to close that gap. The project focused on turning fragmented activity into consistent, useful information for business and security teams while limiting sensitive-content collection and supporting enterprise deployment requirements.
GenLift built the collection, AI classification and reporting layer behind an enterprise AI platform, gathering browser and endpoint signals from approved and unapproved AI tools and turning them into structured records of platform, department, task type and sensitive usage.
Unified fragmented AI activity into a shared view of tool usage, department-level demand, and potential data exposure.
Flagged sensitive AI usage for security review, with privacy controls limiting sensitive-content collection.
Structured real usage patterns to support testing AI tools against employees’ actual tasks and company policies.
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