Most AI product metrics are vanity metrics in disguise. Here's what you should actually be tracking.
The Problem with Standard Metrics
Walk into any AI product meeting and you'll hear teams obsess over model accuracy. "We hit 95% accuracy!" they celebrate.
But accuracy alone doesn't tell you if your product is succeeding.
Users don't care about accuracy scores. They care about whether your product solves their problem. The gap between model performance and user value is where most AI products fail.
Start with User Outcomes
Before you track anything else, define what success looks like for your users.
For a customer support bot, success might be "resolved user issue without human intervention." For a writing assistant, it might be "user published the content."
Your metrics should connect model behavior to these outcomes. Everything else is secondary. Learn more about building AI agents that focus on real user problems.
The Core AI Product Metrics
Here are the metrics that actually matter across most AI products.
Task Success Rate. What percentage of user tasks are successfully completed? This is your north star metric. It's outcome-focused and user-centric.
User Acceptance Rate. How often do users accept or apply your AI's suggestions? Low acceptance means your AI isn't providing value, regardless of technical accuracy.
Time to Value. How long does it take users to get value from your AI? Faster is almost always better. Track latency obsessively.
Retention and Frequency. Do users come back? Do they use your AI feature regularly? If not, your AI isn't solving a real problem.
Model-Specific Metrics
Technical metrics still matter, but they're means to an end.
Track precision and recall separately. The right balance depends on your use case. False positives hurt spam filters. False negatives hurt medical diagnosis.
Monitor confidence scores. Your model should know when it doesn't know. Low-confidence predictions need different handling than high-confidence ones.
Watch for distribution drift. Real-world data changes. If your model performance degrades over time, you need to catch it fast.
Key Insight
The best AI products track both technical metrics (model performance) and business metrics (user outcomes). Technical metrics help you diagnose issues, but business metrics tell you if you're actually succeeding.
Cost Metrics That CEOs Care About
AI products are expensive to run. Your metrics need to reflect this reality.
Cost per Inference. How much does each AI interaction cost? Factor in API calls, compute, and infrastructure. Make this visible and track it over time.
Cost per Successful Outcome. This combines cost and effectiveness. A cheaper model that achieves fewer successful outcomes might actually cost more per valuable result.
Value-to-Cost Ratio. What's the business value generated compared to AI costs? This is the metric that determines if your AI product is sustainable.
Quality Metrics for Generative AI
If you're building with LLMs, standard metrics don't capture quality.
Track hallucination rates. How often does your AI make up information? Sample outputs regularly and have humans check for factual accuracy.
Measure response relevance. Is the AI actually answering the question asked? Use both automated scoring and human evaluation. Explore prompt engineering best practices to improve response quality.
Monitor toxicity and safety. Your AI needs guardrails. Track how often safety filters trigger and whether they're catching real issues or over-filtering.
The Human-in-the-Loop Metric
Most successful AI products aren't fully autonomous. They augment humans. Your metrics should reflect this.
Track human override rate. How often do users or operators override AI decisions? Frequent overrides suggest your AI isn't trustworthy.
Measure time saved. If your AI augments human work, quantify the time savings. This is your value proposition made concrete.
Building Your Metrics Dashboard
Don't track everything. Focus on the few metrics that drive decisions.
Start with one primary metric that ties to user success. Add 2-3 secondary metrics that help diagnose issues. Include one cost metric so you're always aware of economics.
Make your dashboard real-time. AI products can degrade quickly. You need to catch problems fast.
The A/B Testing Mindset
AI product development is continuous experimentation.
Don't just track metrics. Use them to run experiments. Try different model versions, prompt strategies, and UX patterns. Measure the impact on your core metrics.
Sometimes a technically worse model creates better user outcomes. Let the data guide you.
Common Metric Mistakes
Here's what to avoid.
Don't optimize for model accuracy alone. I've seen teams hit 99% accuracy on test sets while user satisfaction dropped. Real-world data is different.
Don't ignore edge cases. Your AI will be judged by its failures. Track performance on difficult examples, not just averages.
Don't forget qualitative feedback. Numbers don't tell the whole story. Read user feedback. Watch session recordings. Talk to your users.
Making Metrics Actionable
Metrics are useless if they don't drive action.
Set clear thresholds. When does a metric trigger investigation? When does it trigger intervention? Define these upfront.
Connect metrics to team responsibilities. Every metric should have an owner who's accountable for it.
Review metrics regularly as a team. Make metric reviews a standing agenda item. Discuss trends, anomalies, and experiments.
Your Metrics Framework
Here's a simple framework to get started.
Layer 1: User outcome metrics. Did we solve the user's problem?
Layer 2: Product engagement metrics. Are users actually using the AI feature?
Layer 3: Model performance metrics. Is the AI technically sound?
Layer 4: Cost and efficiency metrics. Is this sustainable?
Start at layer 1. Work your way down only when you need to diagnose issues. Want to learn more? Check out our comprehensive AI PM curriculum that covers metrics and measurement in depth.
Good metrics turn AI products from black boxes into manageable, improvable systems. Choose them wisely.