Agentic KPI Scorecard for Chaos Workflows

Category: Academy · Stage: Optimisation

By Max Beech, Head of Content

Updated 15 August 2025 · Expert review: [PLACEHOLDER: Head of Analytics]

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.

      - Which KPIs belong on an agentic scorecard?

      - How do you build the scorecard in Chaos?

      - How do you run reviews?

    

  
  
    

TL;DR

      - Track adoption, accuracy, time saved, and sentiment for each Chaos automation.

      - Visualise KPIs with tables that link to [experiment reviews](/blog/ai-experiment-review-template).

      - Hold monthly scorecard reviews, recording decisions in the [decision log](/blog/decision-log-workflow).

    

  
  
    
      
        
          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
        
      
    
    Sample scorecard highlights adoption, accuracy and impact.
  
  

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.

    

  
  
    

Next steps

      - List every Chaos automation and define owners plus success metrics.

      - Populate the scorecard with baseline data.

      - Schedule monthly reviews and report wins to leadership.

    

  
  
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