Unlocking the Value of Archives

How do Archivists add value?

Through the primary activities of the archival profession, archivists add value by following a logical chain of processes:

• Transfer & Accessioning: Undertaking sentencing decisions via Retention & Disposal, overseeing the physical transfer of records, and creating a basic accession list for previously unlisted records.
• Arrangement & Description: Accessioned records are removed and collated into a new or existing groupings (by series, provenance, subject/theme etc.); metadata is added to the minimum required for an accession.
• Database Listing: Creating basic listings of accessions and groupings of physical records; location tracking is enabled.
• Digitisation: Creating high-quality reproductions according to best-practice and standards; completing follow-up steps such as, Optical Character Recognition (OCR), metadata extraction and enrichment, transcription, creation of digital preservation formats, etc.
• Adding to Open Access Database: Exposing records to the web and conducting Search Engine Optimisation (SEO); adding to linked data & semantic tagging; applying Creative Commons (CC) licenses to records.
• Outreach: Communicate with stakeholders and organisations through various channels on archival holdings; keep GLAM sector and public informed on developments and project achievement

• Standardisation: Applying consistent metadata inputs via an adopted schema (such as the Metadata Object Description Schema or Dublin Core) enables AI and search engines to reliable and effectively retrieve information.
• Digitisation & OCR for Data Extraction: OCR can make historical texts machine-readable. Data extraction enhances discoverability and interoperability with other databases.
• Linked Data & Interoperability: Connecting archival records with national and global-level heritage organisations datasets leads to broader research applications.
• Data Integrity & Reliability: Ensures that AI consumes verified, high-quality information rather than unstructured or misleading data.

Reliable Data, Reduced Bias, Smarter Insights

As the born-digital era continues to rapidly expand, and AI continually shapes how we access and interpret information, the role of information management professionals has never been more critical. A well-maintained archive provides structured, authoritative sources that enhance the reliability of AI outputs. When archival records are digitised and added to databases, they offer historical accuracy, context, and provenance to bolster our current digital records.

One of the biggest challenges in AI development is reducing bias and misinformation. A notable example (amongst many) is Microsoft’s AI chatbot, Tay, which began expressing offensive remarks on social media due to learning from poor-quality data. Utilising untested or unreliable digital sources can distort AI-generated insights, reinforcing inaccuracies instead of correcting them. So too can incorporating only digital-born records into an LLM. AI output for an organisation with access to only the past 15 years’ worth of digital information would completely gloss over an organisation’s 100+ year history.

Wow, you just saved us hundreds of thousands of dollars in invasive inspection and remediation!

The Future of Archives in an AI-Driven World

• Archival Processing: AI is another tool we can use to help us in our jobs. Increasing AI adoption and upskilling staff would lead to enormous productivity gains by supporting archival processes, especially digitisation, transcription, and metadata creation (with the caveat that human oversight and quality control remains crucial).
• Government & Private Sector Collaboration: Archivists already play an active role in increasing the openness and transparency of information. By partnering with other organisations to link entire collections and datasets, we can vastly expand the amount of information available to our AI platforms to output useful, useable information.
• Empowering Users: By leveraging the metadata, OCR, and linked data that archivists create, AI enhances discoverability and contextual understanding, making databases more intuitive more valuable and powerful. The quicker our users can search, analyse and retrieve information, the more efficient (and happier) they will be. This also further reduces requests and workloads on information management professionals.