Set clear, measurable definitions of AI quality—beyond just accuracy—to guide design, evaluation, and trust. Align your scoring criteria with business goals so everyone knows what “good” looks like from the beginning.
Build Shared Understanding Across Teams
Your AI quality standards are only effective if the whole organization understands and believes in them. Early and regular stakeholder involvement makes AI more trustworthy and aligned.
Thresholds and zones of safety determine when AI outputs are good enough to release—or risky enough to escalate. They make your evaluation actionable and help with continuous improvement.
Not all metrics matter equally. Weighted scorecards let you focus on the quality dimensions most important to your business goals, producing a reliable overall performance score.
AI outputs aren’t just about functioning—they need to be relevant, accurate, fair, and aligned with business goals. Defining quality metrics across dimensions like tone, fairness, and usability ensures your AI works in the real world.