Measuring Success of AI Team
Can replicate similar style dashboards to measure effectiveness of different teams / organizations
Performance Trends - All Agents (Average)
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Portfolio Summary
Portfolio Highlights:
- ✓Statistical Integrity: 96.75%
- ✓Explainability: 96.75%
Focus Area:
⚠Adoption Index: 83.5/100 (targeting ≥90)
Trajectory:
↗ Improving (+3% from prior month)
Total Impact:
7.4 FTE-weeks/month average capacity freed across portfolio
Biometrics capacity redirected to higher-value analysis and regulatory activities
AI Strategy Team Performance
Development Velocity Metrics
Tracks the AI Strategy Team's ability to prototype, validate, and deploy biometrics-focused AI agents. Measures innovation speed, deployment acceleration, internal tool adoption, and production quality.
6-Month Performance Summary
POC throughput grew from 4 to 17/month (+325%), while time-to-production decreased from 52 to 23 days (56% faster), and production bugs reduced by 80%.
Team Upskilling & Engagement
Workforce AI Capability Development
Measures how biometrics staff (biostatisticians, clinical programmers, data managers) develop AI/ML skills and contribute to agent development—transitioning from AI consumers to AI builders.
Organizational Learning Growth
Active contributors grew from 11 to 37 biometrics staff (+236%), with prototype submissions up 5.1x and working groups expanding from 3 to 8 teams.
Biometrics Stakeholder Satisfaction
Continuous feedback from oncology biostatisticians, clinical programmers, and study teams on AI agent usability, accuracy, and workflow impact.
Survey Details
Quarterly pulse surveys sent to all biometrics staff who have used AI agents in their workflows, plus post-pilot UAT feedback sessions.
Total Responses: 182
Response Rate: 78%
NPS Score: +71
Last Updated: June 2025