Archivists
Scrub through 247years of this role's history, from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.
The tools that defined the work
Select an era to see how it reshaped the work.
Physical custody: ledgers, acid-laden paper folders, wooden boxes
The earliest American record keepers worked entirely by hand and physical organization. Records were stored in whatever came to hand: wooden boxes, leather portfolios, and later bound ledgers. The primary tool of the archivist was the hand-written inventory or calendar -- a list of documents in a collection with brief descriptions, produced by reading each item in turn. The labor was entirely physical and intellectual: no mechanical device assisted in organization, description, or preservation. The main threat to collections was fire, water, rats, and acid-containing paper that slowly destroyed itself. The keeper of records had no professional standards, no formal training, and no community of practice beyond correspondence with fellow historians.
Ledger workPaper recordkeeping Typewriter and card catalog (archival description enters the machine age)
The typewriter, commercially available from the late 1870s, transformed the archival finding aid from a manuscript curiosity into a reproducible document. Card catalog systems, borrowed from librarianship, gave archives their first systematic subject and name access tools -- a typed card per folder or item, filed alphabetically, physically searchable. Repositories that adopted the typewriter could produce finding aids legible to any researcher; those that did not remained dependent on a single person's handwriting and memory. The technology did not speed the work dramatically but it standardized the output and made archives conceptually shareable across institutions for the first time.
Work toolChanging equipment Federal Records Acts and microfilm (preservation at scale)
The founding of the National Archives in 1934 and the Federal Records Act of 1950 created the first formal institutional and legal framework for archivists. More transformatively, microfilm -- commercially mature by the late 1930s and widely adopted through the 1950s -- gave archivists their first tool for creating a preservation surrogate: a document could be filmed and the original could be managed (or destroyed under schedule) without losing access. The Eisenhower-era federal government microfilmed millions of pages of historical records. For the archivist, microfilm changed the job from mere physical custody toward active reformatting and preservation planning, the intellectual core of what the profession would become. The negative: microfilm introduced a new type of professional error -- poor filming procedures and vinegar-syndrome acetate bases created a preservation problem that archivists are still addressing.
Effect on the workThe Federal Records Act of 1950 and the supporting infrastructure of the National Archives system significantly grew the ranks of government-employed archivists through the 1950s. The national survey of archival agencies in the late 1950s counted roughly 1,300 repositories, each requiring at least one trained records professional.
Work toolChanging equipment MARC and mainframe databases (machine-readable bibliographic records)
The Library of Congress's Machine-Readable Cataloging (MARC) format, developed in the late 1960s and widely adopted through the 1970s, gave archives their first chance to describe holdings in a computer-readable format. For archivists, the MARC-based RLIN and OCLC bibliographic systems (accessible via terminal by the mid-1970s) meant that finding a manuscript collection no longer required writing to each institution individually -- the records were increasingly discoverable online. The 1970s growth in social history -- oral history, labor archives, feminist archives, civil rights collections -- also drove a significant expansion of the archivist workforce as new repositories formed around newly valued historical communities. SAA membership grew substantially through this decade as college and university archives proliferated.
Effect on the workThe 1970s and early 1980s were a genuine growth period for archival employment: the rise of social history, new university archives, and state archival programs drove hiring that substantially expanded the occupation beyond its federal core.
Mainframe processingComputerized records EAD / internet-accessible finding aids (the World Wide Web transforms discovery)
Encoded Archival Description (EAD), developed by the Library of Congress and SAA beginning in 1993 and released as Version 1.0 in 1998, was the first standard for encoding archival finding aids in a format designed for the World Wide Web. Before EAD, finding aids were paper documents that researchers had to request by mail or consult in person. After EAD, finding aids became internet-searchable, and the Archives Online platform and similar consortia made holdings visible to researchers globally for the first time. The archivist's job changed: the audience for their descriptive work exploded from in-person researchers to anyone with a browser, and the quality and completeness of description suddenly mattered in a new way. DACS (Describing Archives: A Content Standard), published in 2004, formalized the content standards that EAD encoding assumed.
Effect on the workEAD did not directly expand archivist headcount, but it substantially raised the standard of what "good" archival description meant. The internet-visibility of holdings also revealed the scale of backlogs -- collections received but never described, physically safe but intellectually inaccessible -- that would eventually drive investment in digital-processing staff.
Work toolChanging equipment Born-digital processing: BitCurator, Archivematica, digital forensics
By the mid-2000s, archives were receiving born-digital accessions -- hard drives, email exports, floppy disks, optical media -- alongside paper records, and had almost no tools to process them. BitCurator, a suite of digital forensics tools adapted from law enforcement by the University of North Carolina and Maryland (2011-2014), gave archivists their first professional-grade toolkit for acquiring, characterizing, and processing born-digital materials. Archivematica (2013) automated the preservation workflow end to end. These tools did not replace archivists; they created a new category of archivist, the "digital archivist," and a new body of technical knowledge required for the job. Institutions that could not hire digital archivists began to accumulate born-digital backlogs that, by the 2020s, ran to petabytes at major universities and federal agencies.
Effect on the workDigital archiving skills became the most in-demand specialization in archivist job postings by the early 2010s, driving salary premiums and creating a supply gap the profession's graduate programs struggled to fill. By 2020, "Digital Archivist" was one of the fastest-growing job titles within the profession.
Work toolChanging equipment Transkribus AI handwritten text recognition (HTR)
Transkribus, developed from the EU-funded tranScriptorium project (2013-2015) and the READ project (2016-2019), gave archivists their first practical AI tool for the most labor-intensive task in the manual-records world: transcribing historical handwriting. The platform grew from 2,200 registered users in 2015 to 45,000 in 2020, a twenty-fold increase in five years, with major institutions including the British Library, the National Archives of Estonia and Norway, and hundreds of university archives adopting it. For archivists, the impact was to convert transcription from a years-long project requiring armies of volunteers into a weeks-long supervised AI workflow: a custom HTR model trained on 75-150 pages of a specific hand can achieve 85-95% character accuracy on legible 19th-20th century cursive. The archivist's role shifted from transcriber to model trainer, quality controller, and exception-handler. Collections previously inaccessible due to transcription backlogs became discoverable for the first time.
Effect on the workNo archivist job loss has been documented as a result of Transkribus -- rather, the tool enabled archivists to tackle backlogs that would otherwise have required decades of unfunded labor. The profession's consensus is that HTR tools have expanded what one archivist can make discoverable rather than reducing headcount.
Work toolChanging equipment Generative AI tools: Whisper, LLM-assisted description, ArchivesSpace AI integrations
The 2022-2025 generative AI wave arrived in the archives profession through several converging channels. OpenAI Whisper (September 2022) automated oral history and AV transcription with word error rates under 5% for clearly recorded English speech, turning multi-year AV backlogs into weeks of supervised review. ArchivesSpace, used by 500+ repositories, began integrating LLM-assisted drafting of scope notes and subject heading suggestions by 2024-25. ChatGPT and similar tools are being used by archivists to draft biographical histories, administrative histories, and grant narratives from structured inputs, though professional consensus holds that all LLM output requires rigorous verification before publication. The SAA formed an AI task force in 2023-24 to develop guidance. The net effect so far: augmentation, not displacement. The tasks that define the archivist's unique value -- appraisal authority, provenance judgment, access restriction decisions, physical authentication -- are precisely the tasks AI cannot reliably perform, and the profession's ethical framework makes human accountability for those decisions non-negotiable.
AI audit toolsPattern detection
What credible sources project
Scrub the slider past now to anchor each scenario on the scrubber. The spread is the range of futures credible sources project for this role.
What's shifting in the work right now
The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.
What's changing in your day
Three parts of your work where AI is already doing real lifting, and what stays yours.
AI is sitting alongside you hereUse Transkribus AI handwritten text recognition (HTR) to transcribe historical manuscript collections at scale — training custom HTR models on an institution's specific handwriting styles, reviewing and correcting model output, and publishing corrected transcriptions that make collections discoverable for the first time without the years of volunteer labor previously required.
Use Transkribus AI handwritten text recognition (HTR) to transcribe historical manuscript collections at scale — training custom HTR models on an institution's specific handwriting styles, reviewing and correcting model output, and publishing corrected transcriptions that make collections discoverable for the first time without the years of volunteer labor previously required.[6],[7],[5]
Transkribus HTR models trained on an institution's handwriting can achieve 85-95% character accuracy on 19th-20th century cursive and substantially higher on clearer hands — compressing what was once years of volunteer transcription effort into weeks of supervised AI processing. Your role shifts from transcriber to model trainer, quality controller, and exception-handler for difficult hands, damaged documents, and multilingual materials the model cannot handle. Learning to train custom Transkribus models and setting transcription quality thresholds is now a core archivist competency for any institution with significant manuscript holdings.
AI is sitting alongside you hereGenerate transcripts of oral history recordings and archival audio-visual materials using OpenAI Whisper or cloud-based AV transcription services — reviewing speaker-diarized output, correcting proper nouns and technical terminology, and time-stamping corrections for integration into AV access systems.
Generate transcripts of oral history recordings and archival audio-visual materials using OpenAI Whisper or cloud-based AV transcription services — reviewing speaker-diarized output, correcting proper nouns and technical terminology, and time-stamping corrections for integration into AV access systems.[8],[2]
Whisper-large-v3 produces very high accuracy transcripts for clearly recorded audio in major languages (word error rates under 5% for standard American English), turning AV transcription from a months-long volunteer project into an hours-long review task. The accuracy gaps that require archivist correction cluster predictably: speaker attribution ("he said / she said"), proper nouns (names, organizations, place names), technical jargon, non-English passages, heavy accents, and overlapping speech. Build a post-processing workflow for Whisper output — spellcheck against a controlled vocabulary of names relevant to your collection — and you can process AV backlogs that were previously untouchable.
AI is sitting alongside you hereUse Adobe Acrobat AI and OCR tools to convert scanned documents into searchable PDFs — reviewing recognition accuracy for historical typefaces, correcting degraded-scan errors, and integrating corrected text into the repository's full-text search layer so researchers can discover collection contents that were previously invisible.
Use Adobe Acrobat AI and OCR tools to convert scanned documents into searchable PDFs — reviewing recognition accuracy for historical typefaces, correcting degraded-scan errors, and integrating corrected text into the repository's full-text search layer so researchers can discover collection contents that were previously invisible.[9],[2]
Adobe Acrobat's AI-assisted OCR handles clean 20th-century typescript at very high accuracy rates and is increasingly effective on older typefaces, but degrades sharply on carbon copies, mimeograph duplicates, water-damaged documents, and non-Latin scripts. Your role is quality-sampling the OCR output at statistically meaningful intervals, building a remediation workflow for problem document types, and ensuring that OCR errors in finding aids and full-text layers don't systematically hide important materials from researchers. Train yourself on ALTO XML and HOCR formats so you can troubleshoot the text layer in your repository system.
Where this role is heading
Natural next steps for someone with your foundation: not exits, evolutions.
Education Administrators, Postsecondary
Education Administrators, Postsecondary (SOC 11-9033.00) — including university archivist director roles, head of special collections, and dean-of-libraries positions — represent the natural leadership escalation for experienced academic archivists. The pivot moves from processing collections to managing teams, budgets, and institutional strategy, with substantially stronger CRI because administrative authority and community trust are highly resistant to AI displacement. University archivists who step into director or dean roles are increasingly positioned as institutional AI policy advisors — deciding how AI-generated university records are retained, what HTR investments to make, and how to govern researcher access to sensitive digital collections. The barrier is administrative experience and a demonstrated leadership portfolio.
- · Academic governance: faculty senate engagement, curriculum committee participation, accreditation self-study preparation
- · Budget management and grant writing (NEH, IMLS, state humanities councils, private foundations)
- · Personnel management and performance review in a unionized higher education context
- · Strategic planning for library and archives programs: benchmarking against peer institutions, space planning, digitization roadmap development
- · Institutional AI policy development: AI records governance, AI use policy for special collections, privacy framework for AI-assisted research tools
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