AI STRATEGY
Monitor, Adapt, and Respond Responsibly
Visualize What’s Happening in Real Time
Dashboards translate raw interaction and quality data into insights your team can act on. They help everyone—from data scientists to executives—see how your AI is performing in the wild.
Why it's Important
Surfaces issues before they escalate
Creates alignment around product quality
Supports evidence-based decisions
Encourages proactive tuning and retraining
Enables transparency for non-technical stakeholders
How to Implement
Identify key metrics to track (e.g., satisfaction score, fallback rate, edit %)
Build dashboard views by feature, user type, and timeframe
Include model version history and quality score trends
Automate data refreshes and anomaly detection
Provide export/download options for reports
Set permission tiers for dashboard access
Available Workshops
Dashboard Wireframing Jam
Metric Prioritization Workshop
Stakeholder Metrics Wishlist Review
Alerting Rules Design Sprint
KPI-to-Chart Mapping Exercise
Dashboard Demo Roadshow
Deliverables
Live AI quality dashboard
Metric definitions doc
Data pipeline diagrams
Team usage and feedback report
Monthly dashboard insights summary
How to Measure
Dashboard load time and uptime
% of metrics with daily/weekly refresh
Number of teams actively using dashboards
Number of alerts triggered and resolved
Time from anomaly detection to action
Stakeholder satisfaction with insights access
Pro Tips
Include "why it matters" text below each chart
Add in latency data alongside feedback data
Record short videos walking through dashboards
Use dashboards in standups or sprint reviews
Link dashboards to decision-making docs (e.g., PRDs)
Schedule regular cleanup to avoid clutter
Get It Right
Design for humans, not just data folks
Tie charts to questions your team asks often
Use thresholds and color coding for clarity
Create layered views (overview vs. deep dive)
Update dashboard elements as product evolves
Don't Make These Mistakes
Showing vanity metrics that don’t drive action
Forgetting to log chart or metric definitions
Building dashboards no one uses
Hardcoding assumptions that don’t generalize
Ignoring dashboard feedback from end users