How Organizations Can Measure Progress in Data & AI Literacy

Attendees joined us to explore key frameworks, practical tools and common pitfalls in measuring data and AI literacy—drawing on years of experience working with thousands of learners across a wide range of industries.

During the webinar, attendees learned how to: 

• Apply proven techniques, such as cohort-based learning and scheduling strategies, to increase training engagement and completion rates across teams
• Establish a measurable baseline of data and AI literacy within their organization by applying both objective (skills-based) and subjective (perception-based) assessment methods
• Identify specific, trackable indicators of training program success—including metrics for participation, learning outcomes and employee perception—to evaluate progress over time
• Recognize and address common pitfalls in data and AI literacy measurement—such as over-reliance on a single metric or lack of feedback loops—and learn how to implement timely course corrections