Pipeline thinking
Capture → OCR/transcription → QA → metadata → preservation store → access layer. AI belongs in defined stages with sampling checks. Do not skip QA to “finish on time.” A fast, wrong index is an expensive gift to future confusion.
Privacy and access before bulk processing
Historical does not always mean public. Personal data and restricted fonds need access rules before you run bulk models. Coordinate with archivists and privacy officers. Align sensitive classes with secure AI practices.
Where models help
- OCR post-correction suggestions
- Entity hints for indexing (always reviewed)
- Clustering similar document types
- Draft descriptions for human archivists to accept or rewrite
Preservation first
Choose open formats and durable storage. AI features should not trap masters in a proprietary-only system without export. Models change; catalogues and bitstreams must endure. Budget for ongoing description work—the unglamorous half of “digital transformation.”