The Evidence Map for Reskilling That Holds

Peer-reviewed training-transfer meta-analyses define success as generalization of learned behavior to the job plus maintenance over time. Certificates, satisfaction, and test scores show exposure or near-term learning; they do not establish workplace use or performance. No lens in the research disputed this; the argument is over how to create transfer, not whether completion proves it.

Meta-analytic sustainment correlations run .51 for supervisor support, .48 for peer support, and .32 for organizational support, with overlapping intervals and no reliable winner among support types. A second meta-analysis confirms supervisor support relates to transfer while showing that same-source measurement inflates the estimates. The safe conclusion: support, opportunity, and reinforcement matter. The evidence does not prove a weekly supervisor check-in causes AI fluency.

In a peer-reviewed study of 5,172 support agents, AI assistance raised resolved chats per hour 15 percent overall and about 30 percent for less-skilled workers, while the highest-skill and highest-tenure groups saw little gain and small declines in resolution rate and satisfaction. A survey of nearly 6,000 executives found 69 percent of firms using AI and 89 percent reporting no labor-productivity impact over three years. These are heterogeneity results, not a universal score, but they corroborate the direction: more access or use cannot stand in for better work.

The AI Fluency Framework supplies four competencies operationalized as 24 behaviors. The published index baseline examined 9,830 substantive conversations and only the 11 conversation-visible behaviors, on one vendor platform, correlationally. Independent peer-reviewed work partly corroborates objective skill measurement: a validated literacy assessment predicted AI-supported task performance where self-rated proficiency did not. That supports measuring skill objectively; it does not validate this specific index.

Numerator: eligible trained workers who, 90 days on, pass at least two independently sampled live cases through the approved workflow at preset quality and policy thresholds, equal-or-better than baseline, without excess rework or risk exceptions. Denominator: trained workers with a fair opportunity to perform. Keep exposure, observed behavior, workflow outcome, and net economics in separate lanes. This is a synthesis consistent with NIST measurement principles, not a validated standard: publish the rubric and test its predictive value.

Before training begins, capture the role, the recurring task, the approved AI contribution, the human decision owner, and current quality, cycle time, rework, escalation, and risk rates. Without the baseline, every later number is unanchored.

Require a safe practice case and a knowledge check, then move immediately into live work. Completion stays a leading indicator and access prerequisite; it never becomes the success metric.

Weekly for the first month, then biweekly: select one case, inspect the output and its evidence, discuss the human edit, remove one blocker, set the next attempt. Track manager review minutes per learner so the coaching cost stays visible instead of disappearing into the ROI story.

Track eligible opportunities, use, and abandonment for diagnosis. Separately score sampled work for accuracy, completeness, evidence, policy, decision quality, rework, and escalation. Never convert prompt counts into a fluency or performance rank, and score sampled work products with notice and aggregation before any individual scoring: measurement that reads as surveillance costs candor and participation.

Report 30-day transfer, 60-day sustainment, and 90-day sustainment separately so decay is visible, and keep opportunity-to-perform in the denominator so the metric cannot be gamed by shrinking eligibility.

Hold tool, task, and access constant while comparing classroom-only enablement against workflow practice with manager reinforcement. This is the cleanest enterprise test of whether the program, rather than the tool or the participant mix, produced the result.