Why it matters: Without measurement, AI pilots stagnate. Accenture’s 2024 Pulse of Change report notes that 42% of executives lack KPIs for AI outcomes.[1] An agentic KPI scorecard shows whether Chaos automations deliver real leverage.
TL;DR
- Track adoption, accuracy, time saved, and sentiment for each Chaos automation.
- Visualise KPIs with tables that link to experiment reviews.
- Hold monthly scorecard reviews, recording decisions in the decision log.
| Automation | Adoption | Accuracy | Time saved | Sentiment |
|---|---|---|---|---|
| Meeting brief generator | 78% of target execs | 94% action items correct | 3.4h/week | 4.6/5 |
| Incident warmup reminders | 100% squads | 98% schedule adherence | 1.2h/week | 4.2/5 |
Which KPIs belong on an agentic scorecard?
Start with adoption (% of target users), accuracy (error rate), impact (hours reclaimed or revenue influenced) and sentiment (survey scores). Tie each metric back to the compliance roadmap to check for risk.
How do you build the scorecard in Chaos?
Create a dedicated collection with cards for each automation. Attach experiment data, connect to the demo storyboard, and sync to dashboards via CSV exports if finance needs deeper analysis.
How do you run reviews?
Hold a monthly 45-minute review. Walk through the scorecard, note red/yellow items, and capture next steps in the decision log. Gartner suggests quarterly recalibration of AI KPIs; monthly reviews keep you ahead of that guidance.[2]
Key takeaways
- Define KPIs that capture adoption, accuracy, impact, and sentiment.
- Use Chaos to centralise data, evidence, and decisions around automation performance.
- Review monthly so the scorecard guides investments, not just reports them.