What it means
Success metrics are the measures an organisation uses to decide whether an AI initiative is achieving its intended outcomes, such as time saved, quality, user satisfaction, error rates or service improvements. Good metrics are defined before launch and include a baseline.
Real-world example
For an AI tool drafting responses to library enquiries, the team measures the average response time before and after, the share of drafts sent with minor or no edits, the rate of factual errors found in spot checks, and staff and user satisfaction. They also track an unintended effect: whether complex enquiries now wait longer.
Why it matters for AI adoption
Without agreed metrics, AI projects are judged by impressions, enthusiasm or cost alone. Good metrics combine efficiency, quality and people measures, and help decide whether to scale, adjust or stop. Measuring only speed can hide declining quality.
Questions to ask
- What is our baseline today, and what change would count as success?
- Which metrics would warn us that quality or fairness is getting worse?