5 AI Metrics That Matter (and 3 That Don’t): The Essential KPI Cheat-Sheet (Copy)
Measuring AI’s business impact is a top priority across businesses—yet most teams still track the wrong metrics. Here’s a clear, copy-paste-ready guide for your Squarespace blog: which AI metrics actually move the needle, which ones don’t, and how to build a dashboard that showcases real value.
5 AI Metrics That Matter
1. Accuracy (or Error Rate)
Measures how often your AI delivers correct results.
High accuracy builds trust and drives adoption. Track accuracy for core tasks and monitor error rates to spot issues early.
2. User Engagement (Active Users, Session Duration, Retention)
Looks at how many people use your AI, how long they stay, and how often they come back.
These numbers reveal if your AI is actually helpful and sticky.
3. Business Impact (Automated Resolution Rate, Conversion, Cost Savings)
Focuses on outcomes: how many issues your AI resolves without human help, how many sales it drives, or how much money it saves.
These metrics prove ROI and justify investment.
4. Response Time (Latency)
Tracks how quickly your AI responds to user input.
Fast responses improve user experience and satisfaction.
5. Reliability & Robustness
Measures how well your AI performs across different scenarios and data types.
Robust models handle real-world variability, ensuring consistent results.
3 AI Metrics That Don’t Matter (as Much)
1. Model Size (Parameter Count)
Bigger isn’t always better. More parameters can mean higher costs and complexity without guaranteed performance gains.
2. Training Loss (Without Context)
Low training loss can mean overfitting. What matters is real-world performance on new data, not just fitting the training set.
3. Vanity Metrics (Page Views, Demo Runs)
High page views or demo runs look good, but don’t reflect true adoption or business value. Focus on metrics tied to outcomes.
KPI Dashboard Template
Dashboard Tips:
Visualize trends for each metric over time.
Segment by user type or use case.
Include business impact metrics alongside technical KPIs for executive reporting.
FAQ: Measuring AI Performance
Q: How do I know if my AI is helping my business?
Track business impact metrics like Automated Resolution Rate, conversion, or cost savings. High accuracy with low business impact means it’s time to investigate user experience or integration.
Q: What’s the best way to report AI KPIs to leadership?
Use a dashboard that combines technical (accuracy, latency) and business (ARR, cost savings) metrics, highlighting trends and actionable insights.
Q: Should I care about model size or training loss?
Only if they directly affect cost, speed, or production accuracy. Focus on real-world value and user outcomes.