Preparing Your Archive for AI: A Practical Guide

The cultural heritage sector is undergoing a significant shift. Artificial intelligence has emerged as a practical tool for archivists — but the use of AI in archival work is not simply a matter of plugging in new software. It requires a clear understanding of what AI can do well, where it is weaker, and where human expertise remains essential.

If your institution is working through a backlog of uncatalogued material, or struggling to meet funder requirements for digital access, AI can meaningfully accelerate your workflow. But acceleration is not the same as automation, and understanding the difference is what separates effective adoption from disappointing results. This guide covers the current landscape of AI for archives, how to choose the right AI tools, and how to prepare your collections so that AI can work effectively on them.

Understanding what AI does — and where it stops

A useful way to think about AI in archival work is as a three-layer model:

1. Extraction — reading text from images (OCR, handwritten text recognition), identifying named entities, detecting dates, pulling structured data from unstructured documents. AI is strong here. It handles volume and repetition well, and modern models achieve high accuracy on a wide range of historical scripts and printed material.

2. Structuring — organising extracted information into metadata fields, mapping it to schemas like ISAD(G) or Dublin Core, classifying items by type or subject, and generating draft catalogue records. AI is capable here, though output quality depends heavily on the consistency of the input material. Draft records are useful starting points, not finished products.

3. Interpretation — deciding what a record means in context, resolving ambiguity, establishing provenance, determining sensitivity, applying institutional conventions, and making curatorial judgements about arrangement and access. AI is weakest here. These are tasks that require professional knowledge, contextual understanding, and accountability that current AI systems do not possess.

The practical implication is clear: AI can reduce the labour required to move from digitised material to structured, reviewable metadata. It does not remove the need for professional judgement. Organisations that expect AI to handle all three layers without human oversight will be disappointed — and may introduce errors into their archival records that are difficult to detect and costly to correct.

What your collection needs before AI can help

AI is not a remedy for disorganised source material. The quality of AI output depends directly on what you feed into it. Before introducing AI tools into your workflow, check these prerequisites:

• Scan quality. AI models trained on document images need legible input. Blurred, skewed, or heavily shadowed scans produce poor OCR and unreliable metadata suggestions. If your digitisation produced low-resolution or inconsistent images, improving scan quality should come before AI processing.
• Folder and file discipline. AI works best when material is organised into logical groupings — by series, by box, by collection. A single folder containing thousands of unrelated files from different collections will produce worse results than material that has been sorted, even roughly, before ingest.
• Rights awareness. If your collection contains sensitive personal information, restricted material, or items with unresolved copyright status, you need to know that before AI processes them. AI tools may generate descriptions or transcriptions of material that should not be published without review. Have a clear picture of where restrictions apply.
• Review capacity. AI generates draft output. Someone needs to review it. If your organisation does not have the staff time to check AI-generated records before they are published, the output will sit unreviewed — which is no better than an uncatalogued backlog. Plan for review as part of the workflow, not as an afterthought.

What AI for Archives Actually Means

The phrase “ai for archives” covers a broad range of technologies. At one end, you have general-purpose large language models — tools like ChatGPT — capable of drafting text and answering questions. At the other, you have bespoke AI systems trained specifically for archival description, handwritten text recognition (HTR), and digital collections management.

For most archival work, the distinction matters enormously. General AI models are impressive, but they hallucinate facts, amplify historical biases, and do not understand archival hierarchy. Task-specific AI, by contrast, is designed to perform discrete structural tasks: transcribe a handwritten ledger, extract named entities, populate a metadata schema. That precision is what makes AI genuinely useful in libraries and archives.

Handwritten Text Recognition and Transcription

One of the highest-impact uses of AI in archival collections is handwritten text recognition. HTR models use computer vision and natural language processing to read cursive historical documents — parish registers, estate accounts, military records — and produce machine-readable transcripts at scale.

Where traditional OCR struggles with historical scripts, modern HTR AI models trained on period-specific datasets perform with remarkable accuracy. For archivists managing large volumes of born-digital and digitised primary sources, this means a task that once required hundreds of volunteer hours can now be completed in a fraction of the time.

AI-generated transcriptions should always be reviewed by a human archivist before publication. AI use at this stage is about acceleration, not replacement. The archivist retains editorial control; the AI handles the laborious first pass.

Metadata, Finding Aids, and Archival Description

Metadata is the foundation of discovery and access. A scanned document without structured metadata is essentially invisible — it consumes storage but cannot be found by researchers, funders, or the public.

AI tools can now draft ISAD(G)-aligned metadata records from document content, extracting titles, dates, creators, and subjects automatically. For special collections and born-digital archives, this capability is transformative. Instead of manually cataloguing each item, an archivist reviews and approves AI-generated records, correcting errors and adding context that AI cannot infer.

This human-in-the-loop approach is central to the responsible use of AI in archival description. AI helps you work faster; it does not replace the intellectual judgement that makes archival work valuable. Finding aids produced with AI assistance are still authored by archivists — the AI simply reduces the data-entry burden.

For a deeper look at the digitisation process that precedes AI cataloguing, see our guide on how to digitise an archive.

Task-Specific AI vs General AI for Collections

A common mistake heritage organisations make is reaching for general-purpose AI tools when task-specific AI would serve them far better. General AI models are built for breadth; archival AI is built for precision.

Task-specific AI for archives is typically trained on archival materials — historical documents, catalogue records, finding aids, archival research datasets — so it understands the language and structure of the domain. It knows that a “creator” in an EAD record is not the same as an “author” in a library catalogue. It can apply controlled vocabularies, flag sensitive content for human review, and output records in formats like EAD3 XML or Dublin Core without needing to be prompted.

Generative AI and large language models have a role to play — particularly in drafting descriptive summaries and supporting archival research — but they should be used alongside specialist AI tools, not instead of them.

Responsible Use of AI in Archival Work

The ethical use of AI in archives is not an optional consideration. Archival collections often contain sensitive personal information, records of trauma, and community histories that deserve careful handling. AI-powered tools must be configured to flag potentially sensitive content before it is published, and human archivists must make the final decision on every record.

Responsible use also means transparency. Researchers using your digital archives deserve to know when AI has been involved in generating descriptions or transcriptions. Documenting AI use in your collection management system is good practice and increasingly expected by funding bodies and professional bodies alike.

The National Archives, digital humanities projects, and libraries and archives across the UK are developing guidance on the ethical use of AI. Staying engaged with these conversations — rather than waiting for formal policy — puts your institution in a stronger position.

Improving Discovery and Access with AI

When AI is implemented well, the result is a collection that researchers can actually find and use. AI-assisted cataloguing improves metadata completeness, which in turn improves search relevance, accessibility for users with disabilities, and interoperability with national aggregators such as the Library of Congress. For a broader look at the relationship between AI tools and professional archival practice, see our post on AI and the archivist.

Born-digital materials — emails, PDFs, spreadsheets, audio recordings — present particular challenges for discovery. AI tools capable of processing diverse digital collections make it possible to surface this material in finding aids and catalogue records, rather than leaving it sitting in unindexed folders.

For archives serious about making the most of AI, the starting point is not the technology — it is the data. Clean, consistent, well-structured metadata makes AI dramatically more effective. The two things reinforce each other. Our guide on how to make an archive searchable online covers the full chain from capture to public access.

Ready to Bring AI to Your Archive?

Integrating AI into your archival work does not require a large IT budget or a dedicated technical team. It requires the right tools — ones built for archivists, not generic users. See how it works in practice.

If you are weighing up different approaches to cataloguing — from spreadsheets to manual methods to specialist CMS platforms — our comparison page breaks down the trade-offs.

Archivers.ai is a platform designed specifically for heritage professionals. Our AI reads, classifies, and generates ISAD(G)-aligned metadata for documents, photographs, and audio recordings. You maintain full editorial control, and you can export your collection into funder-ready archival formats with a single click.

Explore what AI can do for your collections on our features page, or see how we work specifically for archivists. Ready to start? Request early access to try The Archiver on your own collection.