AI Training Impact Dashboard | Blue Edgewater
Artifact 07 · AI Workforce Enablement

AI Training Impact Dashboard

Post-training metrics across completion, learning, behavior transfer, and business results. Toggle between cohort scenarios to see how program design affects outcomes.

Cohort scenario
Week 1-4 results for a 24-person cohort with standard facilitation and no pre-work requirement.
79%
Program completion rate
Industry avg: 61%
+31pts
Avg pre-to-post score gain
44% to 75% avg
68%
30-day AI adoption rate
Target: 80%
2.1hrs
Avg weekly time saved
Per participant
Pre vs. Post Assessment Scores by Competency
Kirkpatrick Level 2 — learning measurement across all 7 competency areas
Weekly Completion and Engagement
Cumulative program completion rate and active engagement by week
Completion rate
Engagement score
Prompt Proficiency Distribution
Learner proficiency tiers after lab completion
Supervisor Validation Status
30-day post-training validation by department
Business Impact Summary — Kirkpatrick Level 4
Annualized estimates based on 24-participant cohort at 2.1 hours saved per week
2,620
Hours saved annually
across cohort
$78,600
Estimated labor cost
recovered (at $30/hr avg)
43%
Reduction in AI-related
errors at 60 days
L1 Reaction
Learner satisfaction
End-of-program survey across 5 dimensions: content relevance, facilitator quality, pacing, materials, and confidence gained.
Avg satisfaction: 4.2 / 5.0 · 91% would recommend to a colleague
L2 Learning
Knowledge and skill acquisition
Pre-to-post assessment comparison across 7 competency areas. Prompt proficiency scored via AI rubric across 5 lab exercises.
Avg score gain: +31 points · 79% achieved passing score of 75%+
L3 Behavior
On-the-job behavior transfer
Supervisor validation checklist completed at 30 days. Tracks 12 observable behaviors across AI use, prompt quality, validation habits, and responsible use.
68% active AI adoption at 30 days · 74% supervisor validation completed
L4 Results
Organizational impact
Weekly time-savings self-reported at 30 days, annualized across cohort. Error rate reduction measured via supervisor reports and workflow audit.
2,620 hours saved annually · $78,600 labor cost recovered · 43% error reduction

This dashboard demonstrates measurement-first program design. If you are building an AI enablement program that you need to prove works, let’s talk.

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