Artificial intelligence in archives and document management

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Artificial intelligence applied to cloud-based file and document management is fundamentally changing how we work with files, historical records, administrative documentation, and all types of records. Hundreds or thousands of documents are created every day in companies, universities, and public administrations, and without a good system, they can easily become a chaotic mess that's impossible to use.

What's interesting is that, thanks to AI, these documents cease to be mere stored files and become active sources of information for decision-making . Technologies such as optical character recognition, natural language processing, and machine learning allow for better content location, the automation of tedious tasks, and access to files for a much wider audience.

Archival science in the age of artificial intelligence

Archival science, understood as the discipline that deals with the complete lifecycle management of documents (creation, organization, long-term preservation, and access), has progressively incorporated computer science and is now experiencing a qualitative leap with AI. It's not just about digitizing, but about truly leveraging the content of the documents.

In recent years, systematic studies have been published reviewing the impact of AI on archives , analyzing academic literature in databases such as Scopus, SciELO, LISA, and Google Scholar using methodologies like PICOC and PRISMA. These studies reveal that, although there is still some lack of awareness among many professionals, the practical applications are already very tangible.

Among the clearest contributions of artificial intelligence to archival science are assisted digital preservation, automatic metadata generation, and handwritten text recognition (HTR) . These are complemented by machine learning and natural language processing applications that improve information searches and retrieval, fostering more democratic access to archival collections.

Although AI is advancing rapidly, not all institutions are yet fully leveraging its potential . Many archives continue to use highly manual procedures, either due to a lack of resources, a lack of awareness of available tools, or a reluctance to change their processes and comply with ISO 27000 standards.

Artificial Intelligence Observatory in Archives

Within the Spanish landscape, the Observatory of Artificial Intelligence in Archives stands out , a working group made up of professionals from different autonomous communities and organizations that closely monitor how AI is being applied in this field and promote good practices.

The Observatory is composed of specialists from the Region of Murcia, the University of Santiago de Compostela, the Valencian Community, the Balearic Islands, the Sant Cugat del Vallès City Council, the Community of Madrid, the Chartered Community of Navarre, Catalonia, the Basque Country, Castile and León, the General Directorate of State Archives, and other entities . This territorial and organizational diversity allows for a very broad perspective on needs and projects.

The group is coordinated by Désirée Domínguez Pallas, director of the Archive of the University of Santiago de Compostela , who promotes the exchange of experiences and reflection on the technical, ethical and organizational challenges associated with the use of intelligent systems in the management of archives.

These types of initiatives encourage the adoption of AI not in an isolated and disorganized manner, but rather the sharing of technical criteria, real-world examples, and common lines of work , which is fundamental for public archives to move forward coherently.

Key AI concepts in document management

When we talk about artificial intelligence applied to documents and files, several technologies come into play that, combined, allow us to read, understand, classify, and utilize the information contained in the documents . The main ones are OCR, natural language processing, and machine learning, as well as search engines like Elasticsearch.

Optical Character Recognition (OCR) converts images of documents (scans, photographs, image-generated PDFs) into editable and processable digital text. Current OCR engines are capable of working with complex fonts and even certain handwritten scripts, opening the door to processing large historical collections.

Natural Language Processing (NLP) allows machines to analyze and understand human language. With these techniques, it's possible to improve searches, extract entities (people, places, dates), summarize documents, translate texts, and generate automatic descriptions, among many other functions.

Machine learning, on the other hand, is based on models that learn from examples. In the archival field, it allows for the training of systems that distinguish document types, detect patterns in data, or predict which series a document belongs to based on its content, learning from collections already classified by professionals and stored in different types of databases.

Many solutions also incorporate computer vision , which analyzes images beyond just the text. This is especially useful for managing historical photographs, plans, technical drawings, or old documents scanned with low quality, where it's necessary to identify areas of interest before applying other techniques.

How an AI-powered document management system works

In day-to-day operations, a modern document management system with artificial intelligence typically begins with a digitization and capture phase . Although much of today's document production is born digital, there is still a significant amount of paper and scanned material that needs to be processed by advanced OCR.

Printed documents or PDF images are sent to OCR tools that recognize the text and convert it into indexable content . Many modern copiers already incorporate this type of engine, and there are also specific solutions for high-volume scanning.

Once the content is transformed into text, NLP and machine learning models come into play . These analyze the document, identify its subject, extract key data (e.g., names, file numbers, dates, amounts), and classify it within the organization's classification scheme or document management system.

With that information, the system can automatically assign metadata, propose document series, relate documents to each other and facilitate much richer searches , combining free terms, filters by dates, types, subjects or places.

The great added value is that these systems learn with use : as archival staff correct, validate or adjust the classification proposals, the models improve their accuracy and offer results more tailored to the context of each archive or institution.

Practical applications of AI in file management

In practice, AI can intervene in almost every phase of document management, helping to reduce repetitive work, minimize errors, and leverage documents as a strategic source of information . Some uses are particularly noteworthy in the public and private sectors.

One of the most widespread is the automatic classification of documents by content, type, or format. Instead of manually labeling each document, the system suggests or assigns categories, series, or files, greatly speeding up the process and facilitating regulatory compliance.

AI is also used to extract relevant information , such as people's names, addresses, dates, regulatory references, or file numbers. This information is then stored in databases or dashboards to facilitate further consultation and analysis.

Another clear application is the automatic generation of summaries and reports from large sets of documents. Language-based tools, such as some conversational assistants or ChatPDF-type services, can synthesize the key points of a file, contract, or set of reports almost instantly.

Advanced document retrieval is another area where the qualitative leap is clearly evident. Internal search engines powered by AI allow users to find information even if they don't remember the exact title of the document or use different words than those in the original text, thanks to semantic searches that are much closer to natural language.

In regulated sectors, many organizations use AI to strengthen regulatory compliance and detect sensitive information . Systems can identify personal data, confidential information, retention periods, or licenses nearing expiration, helping to prevent non-compliance and better manage risk.

In parallel, the idea of ​​the assisted intelligent archive emerges , where the system not only stores and organizes, but also helps to prepare documents, presentations or answers, automatically locating the most relevant information for each use case.

AI models and platforms for documents

The ecosystem of tools being used in document management ranges from highly specialized engines to state-of-the-art language models . Each one covers a part of the process, and together they form very powerful solutions.

In the field of OCR, one of the best-known engines is Tesseract , a widely adopted open-source project for converting images to text. Many commercial solutions integrate it or rely on similar technologies adapted for manuscripts and historical documents.

For natural language processing, models like BERT and the various GPT families have been a game-changer, enabling a contextual understanding of text that is much closer to how humans read. These models are used to classify documents, generate summaries, extract entities, and answer queries about the content.

Furthermore, many organizations choose to train custom machine learning models using their own documentation. This allows them to adapt to their terminology, organizational structure, and internal regulations, achieving a level of accuracy in classification or pattern detection that would not be possible with generic models.

This technological foundation is used to build integrated solutions that include document management systems, workflows, auditing, electronic signatures, and collaboration tools , all connected to analytical and artificial intelligence capabilities.

Document management solutions that incorporate AI

The range of options goes from technology giants to open-source software projects, and the proposals aim to improve the way organizations manage their files.

Companies like Google and IBM , with platforms such as Google Cloud and Watson, offer data classification, analysis, and extraction services that can be integrated into existing document management systems. Furthermore, companies focused on process automation, such as UiPath and Automation Anywhere, have created software robots capable of managing entire document workflows.

Among the solutions specifically geared towards document management , OpenKM stands out as an open-source document manager that incorporates intelligent classification, metadata extraction, and AI-powered semantic search. Its open-source nature allows for a high degree of customization and adaptation to the needs of each organization.

Another relevant tool is R2 Docuo , geared towards both document management and workflows. This platform promises to significantly reduce the effort involved in creating, controlling, circulating, and signing documents, automating everything from template generation to approval and final archiving.

In the realm of enterprise open-source software, Alfresco has established itself as a leading option for content and file management. It allows for process customization, the addition of modules, and integration with external AI services, making it a flexible foundation for building ambitious projects in large institutions.

Google Document AI and intelligent document processing

One of the clearest examples of a specialized platform is Google Document AI , a cloud-based service designed to automate the classification, extraction, and analysis of documents on a large scale. It is geared towards both private companies and public organizations that handle large volumes of forms, invoices, files, or contracts.

Document AI combines machine learning, NLP, and computer vision to identify relevant fields within each document, regardless of whether it comes from a scan, a PDF, or a screenshot. It can learn the common document formats in a given industry and reuse that knowledge for new applications.

Being available as a cloud service, this type of solution is especially interesting for organizations that need to quickly scale their processing capacity without having to invest in their own hardware or maintain complex infrastructures.

The result is that many tasks that previously required hours of manual review can be resolved in minutes, with the human team dedicated to validation and higher value-added decisions instead of data entry.

Application of AI in Public Administration

Public administrations are finding an ally in artificial intelligence to manage documentation more efficiently and transparently . From city councils to ministries, a growing number of projects are incorporating automatic classification, file analysis, and advanced search systems.

Many local and regional governments are deploying AI-powered document management systems to organize electronic files, expedite procedures by digitally signing documents , and improve citizen access to public information . This reduces response times and facilitates internal control of processes.

A concrete example is the use of Alfresco at the Tomelloso City Council to manage and share internal documentation. Although its full potential is not yet being utilized, initiatives have been launched to share knowledge, such as storing training courses accessible to all staff, laying the foundation for a dynamic digital archive.

AI systems are also used to detect patterns in documentation that may suggest irregularities, inefficiencies, or the need for public policy review, helping to better guide the strategic decisions of administrations.

Furthermore, the public sector faces the challenge of reconciling these innovations with legal requirements for data protection, transparency, and long-term preservation , making the role of archivists and legal experts crucial in designing and implementing these solutions.

Carabela Project: AI for historical handwritten archives

In the field of historical heritage, AI is opening up possibilities that seemed like science fiction just a few years ago. A good example is the Carabela Project , which focuses on the probabilistic indexing of manuscript collections related to underwater historical heritage.

This project, promoted by the Andalusian Institute of Historical Heritage through its Center for Underwater Archaeology and led by the Research Center for Pattern Recognition and Human Language Technology (PRHLT) of the Polytechnic University of Valencia, applies advanced pattern and language recognition techniques to locate and investigate digitized handwritten documentation.

Thanks to AI, it is possible to identify mentions, routes, vessels, or events related to underwater heritage without having to manually read thousands of pages, multiplying the ability of research teams to discover relevant information.

The Carabela Project has been presented at specific workshops aimed at researchers and archival professionals , focusing on how these techniques can be scaled up to other collections of historical manuscripts and serve as a model for new projects in digital humanities.

The implementation of initiatives like Carabela demonstrates that artificial intelligence not only serves to streamline administrative procedures, but also promotes historical research and the preservation of heritage , opening up new questions and lines of study.

Training, courses and academic recognition

The incorporation of AI into archival science is also generating a growing supply of courses, conferences and academic publications that analyze its impact, share experiences and explore advanced uses in different contexts.

Training activities are organized that include modules on objectives, speakers, student profile, recognition of university credits, evaluation procedures and accommodation options , which indicates that these are not one-off workshops, but programs with a certain academic substance.

In parallel, specialized journals in library science, documentation and archival science are publishing articles that systematically review recent scientific literature on AI and archives, highlighting both the most established applications and the knowledge gaps that still need to be filled.

This combination of practical training and scientific production helps archivists, documentalists, and IT staff to speak a common language , which is essential for designing realistic and well-focused projects in each institution.

In addition, online courses focused on improving productivity with AI in the administrative field are being promoted , which explain in an applied way how these tools can be integrated into the day-to-day operations of offices, departments and archives.

Document management in the cloud and its impact on archives

The expansion of AI in archiving is closely linked to the growth of cloud-based document management . Many deep learning-based services require significant computing power, especially in terms of GPUs, which is difficult and expensive to maintain in traditional on-premises infrastructures.

With the Software as a Service (SaaS) model, document management applications and AI engines reside on external servers , allowing organizations to scale resources on demand, pay only for usage, and not worry about the physical maintenance of equipment.

It is true that there are reservations about storing documents on external servers, sometimes located outside the country, but the advantages in terms of scalability, security, backups and remote accessibility are clear, provided that providers are chosen that comply with the demanding European data protection regulations.

For IT departments, this cloud approach means less system administration burden , since it is not necessary to install or update software on each workstation, nor to size hardware for usage peaks that only occur occasionally.

For archives, the cloud opens up the possibility of making large volumes of digitized documentation available to the public , complemented by intelligent search engines, without the internal infrastructure becoming a bottleneck.

This entire ecosystem of observatories, research projects, commercial solutions, and cloud platforms is shaping a scenario in which artificial intelligence is becoming an everyday tool for archivists and records managers , not an isolated experiment. As professionals become more familiar with its possibilities and limitations, use cases are multiplying that improve productivity, expand access to information, and strengthen the preservation of documentary heritage for future generations. Furthermore, search engines and techniques for searching archives themselves help maximize the value of digital repositories.