AI metrics that matter: Developing effective evaluation systems

Measuring AI product success requires structured metric systems that capture both internal performance and customer value. Creating effective metrics frameworks helps organizations avoid the pitfalls of misaligned goals and provides clear direction for product improvement. By systematically tracking the right signals, teams can make data-driven decisions that enhance their AI products’ effectiveness and user satisfaction.

Key framework for developing AI product metrics:

1. Start with fundamental questions about your product’s performance

• Begin by identifying what you need to know about your AI product’s impact on customers and users.
• These questions should address basic functionality (did it work?), performance (how quickly?), and user satisfaction (was it useful?).
• Establishing these core questions creates alignment across teams and prevents conflicting metric interpretations.

2. Develop both leading and lagging indicators

• Output metrics (lagging indicators) measure events that have already occurred, like user adoption rates or completion percentages.
• Input metrics (leading indicators) help identify trends and predict future outcomes, serving as early warning signals.
• A balanced metric system includes both types to provide a complete picture of product performance.

3. Establish systematic data collection methods

• Most metrics require robust data engineering infrastructure to gather at scale.
• For ML products specifically, implement both manual and automated evaluation systems.
• Begin with manual evaluations to establish quality benchmarks before scaling to automated systems.

The big picture: Without clearly defined metrics, teams inevitably create their own versions of “success,” potentially working at cross-purposes despite having the same nominal goals.

• This divergence can lead to misaligned priorities and conflicting product decisions across functions.
• Developing standardized metrics creates a common language for discussing product performance.

Real-world applications: The framework can be applied across various AI products, with metrics tailored to specific use cases.

• For AI search features: Track coverage (% of searches yielding results), latency (time to display results), and user satisfaction (explicit feedback).
• For content generation tools: Measure output delivery rates, generation speed, and quality indicators like edit frequency.

Why this matters: Operating an AI product without performance metrics is like flying blind—making informed decisions becomes impossible without knowing what’s working and what isn’t.

• Metrics provide the necessary feedback loop to improve product performance over time.
• They also help communicate product value to stakeholders and justify continued investment.