AI and the Archivist: Partnership, Not Replacement

The question comes up at every conference, in every team meeting, and across every archival institution that is beginning to grapple with artificial intelligence: is this going to replace us?

It is the wrong question. After years of watching AI tools being deployed across libraries and archives, the pattern is clear. The organisations that get the best results are not the ones that hand everything to an algorithm. They are the ones that treat AI as a capable, tireless assistant — and keep the archivist firmly in charge.

This post looks honestly at what AI can and cannot do in archival work, how the human-AI workflow plays out in practice, and what archivists need to think about as they start using these tools.

Why AI is entering archival practice now

Archival collections have always contained more information than archivists have had time to surface. Backlogs are not a sign of failure — they are an almost universal feature of archival work. A county record office might hold thousands of handwritten ledgers that have never been properly catalogued. A museum archive might have boxes of correspondence that remain uncatalogued decades after acquisition. The material exists; the capacity to describe it systematically does not.

This is where AI changes the equation. Machine learning models — particularly those trained on handwritten text recognition — can now read 19th-century script with accuracy that would have seemed implausible five years ago. Tools like Transkribus have made automated transcription a practical part of archival workflows rather than an experimental curiosity. Alongside this, AI-powered systems can generate draft metadata, suggest subject terms, identify named entities such as people, places, and dates, and flag records that may need sensitivity review.

None of this replaces archival science. But it does mean that AI can take on the high-volume, repetitive stages of archival description — freeing archivists to concentrate on the work that genuinely requires human expertise.

What AI does well in archival workflows

To use AI effectively, archivists need a clear-eyed understanding of where it adds value and where it does not. The practical strengths of current AI systems in archival work fall into a few distinct areas.

Transcription of handwritten and printed material. Handwriting recognition has advanced significantly. AI-assisted transcription of handwritten text — from letters, registers, and estate papers — is now faster and, at scale, more consistent than purely manual transcription. This is one of the clearest wins for AI in archival collections.

Bulk metadata generation. AI can analyse the text of a document and suggest title, date range, creator, and subject terms. The suggestions are imperfect, but they provide a usable first draft that an archivist can review and correct far more quickly than starting from a blank record.

Entity extraction and enrichment. Processing large volumes of archival material to extract personal names, place names, corporate bodies, and dates is labour-intensive work. AI tools can do this at scale, producing data that makes finding aids richer and digital collections more accessible to researchers.

Surfacing connections across collections. AI can identify thematic or subject-matter links between records in different series or fonds — connections that might take a human archivist years of close reading to notice. This has real value for archival research and for helping researchers navigate complex holdings.

Supporting born-digital appraisal. As archives take in growing volumes of born-digital material, AI systems can assist with bulk appraisal by identifying file types, flagging potential duplicates, and clustering documents by subject — tasks that would otherwise represent an enormous manual burden.

Where the archivist’s judgement is irreplaceable

AI for archivists is not a wholesale handover. There are critical points in archival practice where human expertise is not just useful but essential.

Interpreting ambiguous or sensitive material. An AI tool can flag that a document contains personal information or references to a named individual. It cannot make the nuanced judgement about whether that record should be restricted, how it fits within the donor’s wishes, or what the community implications of release might be. That is archival judgement — built from training, experience, and an understanding of context that no AI system currently possesses.

Applying standards and institutional conventions. Archival description is not generic. Every institution has its own conventions, its own established subject vocabularies, its own approach to provenance and arrangement. AI-generated metadata needs to be checked against these standards by someone who knows them. The archivist is not just correcting errors — they are ensuring the description fits coherently within the wider archival system.

Maintaining provenance integrity. Provenance is a foundational principle of archival science. AI can process documents at speed; it cannot understand the archival significance of how and why records were created, by whom, and in what relationship to other records. Maintaining that intellectual control remains a human responsibility.

Making curatorial decisions about access and priority. Deciding which collections to digitise and describe first, how to balance researcher demand against conservation risk, and how to represent community collections fairly — these are curatorial decisions that reflect institutional values and relationships. AI can inform these decisions with data, but the judgement belongs to the archivist.

What remains human work

It is worth being explicit about the specific tasks that AI cannot responsibly handle — not as a general disclaimer, but as a practical guide for anyone designing an AI-assisted workflow.

Deciding hierarchy and arrangement. How a collection is structured — what constitutes a series, how items relate to each other, what the intellectual order of the archive should be — is a curatorial decision grounded in provenance, original order, and institutional convention. AI can process items individually; it cannot determine how they fit together as a coherent whole.

Resolving ambiguity. A letter that mentions “the meeting on Thursday” requires contextual knowledge to date. A photograph captioned “the old building” needs local knowledge to identify. AI will either guess (often wrongly) or leave the field blank. The archivist resolves these ambiguities by drawing on knowledge of the collection, the donor, the locality, and the period.

Applying contextual knowledge. An AI model does not know that a particular handwriting style belongs to a known local figure, or that a series of receipts relates to a building project documented elsewhere in the archive. This kind of cross-referencing and contextual enrichment is what makes archival description genuinely useful to researchers.

Handling sensitivity. Records involving personal data, community histories, traumatic events, or legally restricted material require human judgement about access, redaction, and publication. AI can flag potential sensitivity — but the decision about what to do with that flag belongs to the archivist, operating within institutional policy and legal frameworks.

Signing off output. Every AI-generated record that enters a public catalogue should be reviewed and approved by a person who takes responsibility for its accuracy. The archivist is not just a quality-check step — they are the accountable author of the archival record.

What a working AI workflow actually looks like

In practical terms, introducing AI into archival workflows does not mean switching a system on and walking away. It means redesigning the process so that AI handles specific stages while the archivist retains oversight at the key decision points.

Here is a model workflow for a handwritten correspondence collection, showing where AI acts and where the archivist decides:

1. Digitise. The collection is scanned at appropriate resolution. Image files are organised by box or series.
2. AI transcription. Images are passed through a handwriting recognition system to produce draft transcriptions. AI handles the bulk reading; obvious errors are expected at this stage.
3. AI metadata generation. The system processes transcriptions to generate draft metadata records — title, date, correspondent names, subject terms, and a draft scope and content note.
4. Archivist sample review. The archivist reviews a sample of the AI output to assess accuracy and calibrate their review threshold. If accuracy is low on a particular material type, the workflow may need adjusting.
5. Archivist full review. The archivist works through the records, correcting transcription errors, enriching descriptions where the AI has missed context, resolving ambiguities, and applying institutional standards and controlled vocabularies.
6. Archivist approval. Each record is approved for publication. Sensitive items are flagged and handled according to policy.
7. Export. Approved records are exported in the required format — EAD3, BagIt, CSV — for deposit, publication, or integration with a collections management system.

The workflow is faster than purely manual methods. The archivist is central to it at every decision point. The archival record is more complete than it would have been without AI assistance — and every published record carries the authority of professional review.

AI preparedness for archival institutions

Organisations considering adopting AI tools should think about a few things before they begin. The Society of American Archivists and equivalent bodies in the UK have begun developing AI preparedness guidelines for archivists, and these are worth engaging with. Some of the practical questions to work through include:

• What are your institutional policies on data privacy and third-party processing? AI tools that process archival material using cloud-based services may raise questions about where data goes and how it is handled.
• How will you quality-check AI output? Establishing review thresholds and sampling protocols is important before scaling up.
• How will you document AI involvement in archival description? Transparency about how records were described matters for the integrity of the archival system.
• What training do your staff need? Using AI in archival work is a skill — it requires understanding both the capabilities and the failure modes of the tools involved.

None of these are reasons not to use AI. They are reasons to use it thoughtfully, which is exactly what good archival practice demands.

Generative AI and its place in archival work

Generative AI — the technology behind large language models — is a separate conversation from the structured AI tools used in transcription and metadata generation, though the two are increasingly overlapping.

Generative AI has potential in archival work for drafting scope and content notes, producing accessible summaries of complex collections, and generating context notes for finding aids. It can also support archival research by helping researchers formulate queries or navigate large finding aids in natural language.

The caution here is well-founded. Generative AI can produce confident-sounding descriptions that are factually wrong. It requires close review, and archivists need to be alert to errors that sound plausible but are not grounded in the actual archival material. Used carefully, with review built into the process, it can add real value. Used carelessly, it can introduce inaccuracies into archival records that are difficult to trace and correct.

The principle is the same as with any other AI tool: the archivist reviews, the archivist decides, the archivist is responsible for the archival record.

Cultural heritage collections and the access imperative

There is a bigger picture here that is worth naming. Archives hold irreplaceable evidence of cultural heritage — records that document communities, events, and lives that would otherwise be invisible. The access gap is real: millions of items in archives across the UK and beyond are effectively undiscoverable because they have never been described, or described only at the most basic level.

AI does not solve this problem on its own. But it genuinely shifts what is possible. A local history society with a small team and limited funding can now digitise and catalogue a collection that would previously have sat in boxes for another generation. A museum archive can make its digital collections more accessible to researchers who need rich metadata to find what they are looking for. That matters — for scholarship, for communities, and for the long-term argument for archives as a public good.

Working with AI tools as an archivist

If you are an archivist beginning to explore AI tools, the practical starting point is not to look for a system that does everything. It is to identify the specific bottleneck in your current workflow — whether that is transcription, bulk cataloguing, metadata enrichment, or something else — and find a tool that addresses that problem.

The Archiver is designed specifically for archival work, not adapted from a general-purpose AI platform. It handles automated cataloguing, metadata generation, and description at scale, with a workflow built around the archivist’s review and approval. If you work with physical collections that need digitising and describing, it is worth looking at what it can do for your specific situation.

To see how AI-assisted cataloguing stacks up against spreadsheets, manual methods, and traditional CMS platforms, our comparison page has a detailed breakdown.

There is also a dedicated page for professional archivists with more detail on how the tool fits into established archival workflows and the standards it supports.

For a broader overview of how AI is being applied across the heritage sector, the post on AI for archives is a good starting point.

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The question of AI and archivists is not really about replacement. It is about capacity. Archives need to describe more, make more accessible, and do more with fewer resources. AI tools, used well, make that possible without compromising the expertise and judgement that archives depend on.

If you want to see what that looks like in practice, request early access to The Archiver to try it on your own collection.