This session introduces practical metrics and KPIs for tracking AI time-to-proficiency across teams, moving beyond attendance numbers to measures that reflect genuine, observable capability. It covers how to define what proficiency actually looks like for different roles within your organization, since a reasonable bar for a casual user differs meaningfully from what is expected of someone using AI daily in a specialized function. Attendees will look at lightweight assessment methods designed not to burden already-stretched managers, alongside ways to use the resulting data to target follow-up training precisely where it is needed. The session closes with an approach for reporting this progress to leadership in terms that connect training activity to measurable outcomes.
Without a way to measure prompt competence, you have no reliable way of knowing whether your AI training investment actually worked, or whether your team is still quietly guessing at what makes a prompt effective. Leadership conversations about AI training ROI become difficult to have credibly when the only available data is attendance, not capability. Organizations that skip this step often discover the gap only when output quality across the team turns out to be far more inconsistent than expected, well after the training budget has already been spent. This session gives you a way to catch that gap early, using measures that reflect actual skill rather than attendance alone.
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