Find your metadata gaps before AI does
Most DAMs have gaps that AI search will expose. This scorecard gives you a clear, honest picture of where your metadata stands — and where to focus first.
What you’ll learn:
- How consistent your metadata schema really is across teams and asset types
- Whether your taxonomy is structured for how users search — or just how assets are filed
- Where descriptive metadata gaps will cause AI search to return poor results
- How to prioritise fixes that will have the biggest impact on findability
FAQs
What is the Papirfly DAM metadata scorecard about?
The Papirfly metadata health scorecard is a self-assessment tool for DAM managers who want to understand how well their current metadata setup supports AI-powered and natural language search. It covers five dimensions — schema consistency, taxonomy quality, descriptive metadata, governance, and AI readiness — and gives you a scored view of where your DAM stands today.
Why does metadata quality matter so much for AI search?
AI search doesn’t just match keywords — it interprets meaning. If your metadata is inconsistent, vague, or stored in unstructured fields, AI models have nothing reliable to reason from. Papirfly’s scorecard helps you identify exactly which gaps are most likely to undermine search relevance before you invest in AI enrichment or a new search experience.
Who should use the Papirfly metadata health scorecard?
This scorecard is designed for DAM managers, marketing operations leads, and anyone responsible for how digital assets are structured, tagged, and found. It’s especially useful if your organisation is evaluating AI search tools, planning a DAM migration, or noticing that users are struggling to find the right assets — and you want to understand whether metadata is the root cause.