I still remember visiting a government archive in 2018. The archivists were drowning in work—thousands of boxes waiting to be processed, years-long backlogs, and citizen requests they couldn’t fulfill because they literally didn’t know what documents they had. They had the RTA’s archive management model printed and sitting on a shelf. “It’s excellent guidance,” the director told me, “but we just don’t have the staff to implement it fully.”
Seven years later, I visited the same archive. The backlog was gone. Citizens could search and request materials online. The archivists looked… happy? “AI changed everything,” the director explained. “We’re finally implementing the RTA model the way it was meant to work.”
That transformation—from manual archives drowning in backlogs to AI-powered institutions serving citizens efficiently—is happening across government archives worldwide. And it’s all built on the foundation the RTA established over 15 years ago.
The RTA Archive Management Model: A Foundation That Still Matters
From 2010 to 2025, the Red de Transparencia y Acceso a la Información (RTA) developed comprehensive frameworks for government institutions across Latin America. While much of their work focused on records management and active transparency, they also created detailed guidance specifically for archival institutions.
The Modelo de Gestión de Archivos (Archive Management Model) wasn’t just theory—it was practical guidance developed by archivists for archivists, addressing real challenges government archives face:
What the RTA Archive Model Covered
1. Archival Description Standards
The RTA emphasized proper archival description following international standards (ISAD(G), ISAAR, ISDF). They understood that an undescribed collection is essentially invisible—researchers can’t find materials, staff can’t provide reference service, and the archive’s value to society remains unrealized.
Their guidelines covered:
Creating finding aids at collection, series, and item levels
Writing scope and content notes that help researchers
Establishing access points (names, subjects, places)
Documenting provenance and custodial history
Maintaining relationships between related materials
2. Appraisal and Acquisition
Not everything deserves permanent preservation. The RTA model helped archives develop systematic appraisal processes:
Identifying materials with permanent historical value
Evaluating records for research significance
Balancing preservation costs against historical importance
Establishing acquisition policies
Documenting appraisal decisions
3. Arrangement and Organization
The model promoted the archival principles of provenance and original order:
Maintaining records in their original organizational context
Respecting the creating organization’s structure
Physically organizing materials for preservation and access
Creating intellectual control through description
4. Preservation and Conservation
Government archives hold materials spanning centuries. The RTA provided guidance on:
Environmental controls (temperature, humidity, light)
Appropriate storage materials
Handling procedures
Disaster preparedness
Conservation treatments
Digital preservation for electronic records
5. Access and Reference Services
Archives exist to be used. The RTA model emphasized:
Providing equitable access to materials
Balancing preservation with accessibility
Developing reference services
Educating users about archival research
Managing restrictions appropriately
6. Outreach and Advocacy
The best archives actively engage their communities:
Exhibitions and public programs
Educational partnerships
Online access to materials
Social media presence
Demonstrating archives’ value to stakeholders
Why It Worked
The RTA archive model succeeded because it was:
- Practical: Built for real government archives with real constraints
- International: Drew from global best practices while respecting Latin American contexts
- Comprehensive: Covered all aspects of archival work
- Flexible: Adaptable to archives of different sizes and types
- Collaborative: Developed with input from 42 institutions
Archives across Latin America used this model to professionalize their operations, train staff, and improve services to researchers and citizens.
View archived RTA archive management content →
The Challenge: Excellent Framework, Insufficient Resources
Here’s the uncomfortable reality the RTA acknowledged but couldn’t solve: proper archival work is incredibly labor-intensive.
Let’s look at the numbers for a mid-sized government archive:
Typical holding: 5,000 linear feet of records (roughly 1.5 million pages)
Proper processing (per the RTA model):
Survey and inventory: 5 minutes per linear foot = 417 hours
Arrangement: 15 minutes per linear foot = 1,250 hours
Description (finding aids): 45 minutes per linear foot = 3,750 hours
Cataloging: 10 minutes per linear foot = 833 hours
Quality review: 5 minutes per linear foot = 417 hours
Total time: 6,667 hours = 3.3 full-time archivists working for one year
And that’s just for existing materials. New records arrive constantly.
Typical staffing: 2-3 archivists (if the archive is lucky)
The math doesn’t work. Even excellent frameworks can’t overcome fundamental resource constraints.
The result across Latin American government archives:
Processing backlogs measured in decades
Collections that are accessioned but undescribed (meaning inaccessible)
Limited or no online access
Inadequate reference services
Frustrated researchers
Archivists who love their profession but feel defeated
Sound familiar? The RTA couldn’t fix this problem with better methodology. The methodology was already excellent. What was needed was a way to implement that methodology at scale without doubling or tripling staff.
Enter Artificial Intelligence: Automating What Can Be Automated
Here’s what changed between 2018 and 2025: artificial intelligence became genuinely capable of handling many archival tasks that previously required human expertise.
I’m not talking about replacing archivists. I’m talking about automating the mechanical parts of archival work so archivists can focus on the intellectual and interpretive work only humans can do.
Let me show you what’s now possible.
AI-Powered Archival Description
The RTA approach (2015): An archivist examines a box of correspondence, reads enough to understand the content, writes a scope and content note, identifies subjects and names for access points, estimates dates, notes the volume, and creates a finding aid entry.
Time per box: 45-90 minutes
The AI approach (2025): AI scans or OCRs documents, analyzes content using natural language processing, generates draft description including scope note and access points, estimates dates from content analysis, flags items needing human review.
Archivist reviews and refines AI output.
Time per box: 10-15 minutes
The difference: AI handles initial content analysis and draft description. Human archivists provide expertise, context, and quality assurance.
Real Example: State Historical Society
A state historical society in the US Midwest had a 30-year backlog of undescribed collections—literally, boxes that had been sitting since the 1990s.
Traditional processing estimate: 15 years with current staff
What they did:
Implemented AI-powered description tools
AI generated draft finding aids for entire collections
Archivists reviewed, corrected, and enhanced AI output
Added contextual notes and specialized knowledge
Published finding aids online as they were completed
Result: Entire backlog processed in 18 months. Not perfect description, but good enough to make materials discoverable and usable.
Key insight: The RTA’s archival description standards remained the same. AI simply made it possible to apply those standards at scale.
Automated Name and Subject Recognition
One of the most time-consuming parts of archival description is identifying names, places, and subjects for access points.
AI capabilities:
Named entity recognition identifies people, organizations, places automatically
Subject analysis determines topics from content
Relationship mapping connects related entities
Authority control links to standardized forms
Example: AI analyzing 1920s municipal correspondence automatically identifies:
Mayor’s name (links to authority file)
City council members (cross-references to other collections)
Topics discussed (budget, infrastructure, public health)
Related organizations (county government, state agencies)
Geographic locations mentioned
An archivist would take 20-30 minutes per folder. AI does it in seconds—not perfectly, but well enough that human review takes only 2-3 minutes.
Intelligent Digitization and OCR
The RTA archive model emphasized making materials accessible. Today, that means digital access.
Traditional digitization workflow:
Physically handle each document
Scan at appropriate resolution
Apply OCR (optical character recognition)
Review OCR accuracy
Create metadata
Organize files
Upload to access system
AI-enhanced workflow:
Automated scanning (high-speed scanners with AI quality control)
Advanced OCR that handles handwriting, poor quality documents, multiple languages
AI-generated metadata from content
Automated file organization
Integrated upload to access systems
Speed improvement: 5-10x faster with better OCR accuracy
The Paraguay National Archive Example:
Paraguay’s national archive had extensive collections of historical correspondence, much of it handwritten in fading ink on deteriorating paper.
Traditional digitization estimate: 15 years
What they did:
Partnered with an AI digitization service
AI-powered scanners with quality control
Advanced OCR trained on historical Spanish handwriting
Automated metadata extraction
Archivist review of AI output for historically significant items
Result:
3 million pages digitized in 2 years
85% OCR accuracy on handwritten materials (vs. 60% with traditional OCR)
Searchable online database launched
Researcher usage increased 400%
Automatic Redaction for Restricted Materials
Government archives hold materials subject to access restrictions—personal information, security classifications, attorney-client privilege, etc.
The RTA model emphasized appropriate access controls while maximizing availability. But manually redacting restricted information from thousands of pages is time-consuming.
AI solution:
Identifies potentially restricted information (SSNs, addresses, medical info, etc.)
Flags for human review
Applies redactions according to policies
Generates access and restricted-access versions
Tracks restriction periods
Example – Freedom of Information Archives:
A government archive in Colombia received frequent requests for executive correspondence. Processing each request required:
Locating relevant documents
Reading each page
Identifying information subject to legal restrictions
Manually redacting
Creating access copies
Average time: 15-20 hours per request
With AI:
AI searches digitized materials
Identifies potentially restricted content
Archivist reviews AI suggestions
Applies redactions
System generates access copy
Average time: 2-3 hours per request
Critical point: The archivist still makes the final legal determination. AI just handles the mechanical searching and flagging.
Predictive Appraisal
Appraisal—deciding what to keep permanently and what to destroy—requires deep expertise. But AI can help with the preliminary work.
AI appraisal assistance:
Analyzes record types and content
Compares to retention schedules
Identifies potential permanent value based on patterns
Flags records similar to items previously appraised as historically significant
Estimates research value based on content analysis
Municipal Records Example:
A city archive was drowning in routine administrative records. The RTA model’s appraisal guidelines were excellent, but applying them to 1,000 cubic feet of records per year wasn’t feasible.
What AI enabled:
Automated identification of routine records clearly eligible for destruction per schedule
Flagging of unusual or potentially significant materials for archivist review
Pattern matching to identify record types with historical precedent
Preliminary categorization by record type and function
Result: Archivists spent time on genuine appraisal decisions, not on categorizing obviously routine materials.
Real-World Implementations: AI + RTA Framework
Let me share three detailed examples of archives implementing AI tools while maintaining the RTA’s archival principles.
Case Study 1: Chilean University Archive
Institution: Major Chilean university with archives dating to 1890s
Challenge:
200 years of institutional records
10,000 linear feet of materials
1.5 archivists (yes, one full-time and one part-time)
90% of collection undescribed and inaccessible
Pressure to demonstrate value to university administration
RTA Framework Application: They had implemented the RTA model’s organizational structure, established acquisition policies, and created preservation standards. But actual processing was impossible at their staffing level.
AI Implementation: Phase 1 (Months 1-3): Digitization pilot
High-speed scanning with AI quality control
50,000 pages from highest-demand collections
AI-generated preliminary finding aids
Phase 2 (Months 4-9): Automated description
AI analysis of digitized materials
Draft finding aids for 100 collections
Archivist review and enhancement
Online publication
Phase 3 (Months 10-18): Scale and refine
Expanded to entire backlog
Continuous improvement of AI accuracy
Integration with university’s discovery system
Training for student assistants to review AI output
Results after 18 months:
85% of collection now described (up from 10%)
Online access to 500,000+ pages
Researcher visits increased 300%
Student research using primary sources increased 400%
Archive received budget increase based on demonstrated value
Key factors:
Maintained RTA archival principles throughout
AI handled mechanical tasks
Archivist focused on quality, context, and interpretation
Iterative improvement of AI accuracy
Built on existing RTA-based organizational structure
Cost: ~$45,000 total (digitization equipment, AI software subscription, student wages)
ROI: Accomplished in 18 months what would have taken 15+ years with traditional methods
Case Study 2: Mexican State Archive (Archivo General del Estado)
Institution: State-level government archive with constitutional mandate for preserving and providing access to government records
Challenge:
Receiving records from 150+ state agencies
Legal requirement to respond to citizen information requests
35-year backlog of unprocessed records
8 staff members
Budget cuts reducing staff further
RTA Framework Application: Strong implementation of RTA records transfer protocols, retention schedules, and preservation standards. But couldn’t keep pace with incoming materials or access requests.
AI Implementation:
Automated accessioning:
AI reads transfer documentation
Generates preliminary inventory
Flags issues for staff review
Creates accession records
Integrates with existing archival database
Intelligent search for access requests:
AI searches across collections (even unprocessed ones)
Identifies likely relevant materials
Generates suggested response
Staff reviews and refines
Batch description:
AI analyzes entire accessions at once
Creates collection-level descriptions
Identifies series and subseries
Generates access points
Staff reviews and publishes
Results after 2 years:
Backlog reduced by 60%
Information request response time: 21 days → 6 days
Staff morale improved (less mechanical work, more professional satisfaction)
Compliance with state access law improved
Positive media coverage
Unexpected benefit: AI analysis of access request patterns helped identify high-value collections for priority processing
Critical success factor: AI was configured to follow RTA-based retention schedules and description standards automatically
Case Study 3: Brazilian Municipal Historical Archive
Institution: City historical archive in northeastern Brazil with responsibility for both city records and community historical materials
Challenge:
Diverse collections (government records, family papers, photographs, maps)
Multilingual materials (Portuguese, German, Italian immigrant records)
Many handwritten historical documents
Limited local archival expertise
Strong community interest but low discoverability
RTA Framework Application: Basic arrangement and description but inconsistent standards and significant gaps in documentation.
AI Implementation:
Multilingual OCR and description:
AI trained on Portuguese, German, and Italian handwriting
Generates transcriptions of handwritten documents
Creates bilingual descriptions
Identifies language of materials automatically
Community-sourced enhancement:
AI-generated draft descriptions published online
Community members can suggest corrections and additions
System learns from community input
Archivists moderate and approve contributions
Automated translation:
AI provides English translations of descriptions
Expands researcher access internationally
Maintains original language descriptions
Results after 14 months:
200,000 pages transcribed (previously inaccessible due to handwriting)
Community contributed 1,500+ corrections and enhancements
International research requests increased 600%
Local schools now use archive materials in curriculum
Archive became point of community pride
Innovation: Combined AI with community knowledge—AI for mechanical work, community for local context, archivists for professional standards
Implementing AI in Your Archive: A Practical Roadmap
If you’re an archivist reading this and thinking “this sounds great but how do I actually do it?”, here’s a realistic implementation guide.
Phase 1: Assess and Plan (Months 1-2)
Step 1: Understand your current state
What percentage of your collection is described?
Where are your biggest backlogs?
What access requests take the most time?
What tasks consume the most staff hours?
Step 2: Identify high-value use cases
What AI applications would have the most impact?
Where would time savings make the biggest difference?
What would improve researcher/citizen service most?
Step 3: Evaluate RTA framework implementation
Are your existing policies and procedures aligned with RTA standards?
Do you have documentation AI can learn from?
Are your description practices consistent enough for AI training?
Pro tip: AI works best when you have consistent existing practices. If your description is chaotic, fix that first (using RTA guidelines!), then implement AI.
Phase 2: Pilot Project (Months 3-6)
Don’t try to solve everything at once. Start with one well-defined project.
Good pilot projects:
Describe one collection series using AI assistance
Automate description for one record type
Implement AI-assisted digitization for high-demand materials
Use AI for one type of reference request
What makes a good pilot:
Clear success criteria
Manageable scope
High visibility (so success builds support)
Represents broader challenges you face
Example pilot: “Use AI to generate preliminary finding aids for 50 boxes of routine correspondence, with archivist review and publication online within 4 months”
Phase 3: Tool Selection (Month 3)
Evaluate AI tools based on:
- Compatibility with RTA standards
Can it use your existing description templates?
Does it support international archival standards?
Can it follow your retention schedules?
- Government-appropriate features
Security and privacy controls
Data residency (where is information stored?)
Compliance certifications
Access controls
- Practical considerations
Cost (one-time vs. subscription)
Training requirements
Technical support availability
Integration with existing systems
- Accuracy and reliability
Can you test with your actual materials?
What accuracy can you expect?
How much human review is needed?
Budget expectations:
Small archive (<5,000 linear feet): $5,000-15,000/year
Medium archive (5,000-20,000 linear feet): $15,000-40,000/year
Large archive (>20,000 linear feet): $40,000-100,000+/year
Many tools offer pilot pricing or government discounts. Some open-source options available but require technical expertise.
Phase 4: Implementation (Months 4-6)
Week 1-2: Setup and training
Install software/configure cloud service
Upload sample materials
Train AI on your existing descriptions
Staff training on using the tools
Week 3-4: Guided production
Begin processing pilot materials with AI
Document what works and what doesn’t
Adjust settings and parameters
Develop quality review procedures
Week 5-8: Full pilot production
Process all pilot materials
Measure time savings and accuracy
Compare AI-assisted descriptions to traditional descriptions
Gather staff feedback
Week 9-10: Evaluation and documentation
Analyze pilot results
Document procedures and best practices
Calculate ROI
Plan for scaling
Phase 5: Scale and Sustain (Months 7-12)
Expand systematically:
Add additional record types/collections
Increase volume processed
Add AI applications (if pilot succeeded)
Train additional staff
Continuously improve:
AI accuracy improves with more training
Refine procedures based on experience
Adjust review processes as needed
Share learnings with archival community
Measure and communicate success:
Track metrics (materials processed, access provided, time saved)
Share examples with stakeholders
Demonstrate value to funders
Contribute to professional literature
Common Questions and Concerns
“Will AI replace archivists?”
No. Here’s why:
What AI does well:
Pattern recognition
Mechanical tasks at scale
Preliminary analysis
Consistency
What AI does poorly:
Understanding context and nuance
Interpreting ambiguous information
Making judgment calls
Understanding user needs
Building relationships with record creators
Advocating for archives
What archivists do that AI cannot:
Provide reference and research assistance
Make complex appraisal decisions
Understand organizational and historical context
Develop institutional relationships
Advocate for resources and support
Teach users to use archives effectively
Apply professional ethics
AI changes what archivists spend time on—less mechanical work, more professional expertise.
“Our materials are too unique/complex for AI”
Possibly. But probably not as unique as you think.
AI handles successfully:
Handwritten documents (multiple languages)
Poor quality materials
Fragmented collections
Unusual formats
Multilingual collections
Technical/specialized subjects
Where AI struggles:
Highly visual materials (drawings, maps, photos without captions)
Materials with no text
Extremely poor condition materials
Very rare languages with little training data
Even if AI can’t handle everything, it can probably handle 60-80% of mechanical tasks, freeing staff for complex materials.
“We don’t have budget for this”
Fair concern. But consider:
Cost of not implementing AI:
Continued backlogs
Limited researcher access
Staff burnout
Difficulty demonstrating value to funders
Inability to meet legal access requirements
Cost of implementing AI:
Initial investment: $5,000-50,000 (depending on archive size)
Ongoing: $3,000-20,000/year
Staff time for implementation
ROI timeframe: Most archives recover costs within 12-18 months through time savings
Alternative funding:
Grant funding (many funders prioritize access projects)
Partnership with university IT departments
Shared services with other archives
Phased implementation (start small)
“What about errors? Won’t AI make mistakes?”
Yes. AI makes mistakes. So do humans.
Key principle: Human review is always required for archival work
But consider:
Human error rate on mechanical tasks: 5-10%
AI error rate (with current technology): 3-7%
AI + human review error rate: <1%
The optimal approach:
AI does preliminary work
Humans review and correct
System learns from corrections
Accuracy improves over time
This is faster AND more accurate than purely human work.
“Our existing systems are old. Won’t this be incompatible?”
Sometimes yes, sometimes no.
Most AI tools:
Work as standalone systems initially
Can export to standard formats (EAD, MARC, CSV)
Integration comes later if needed
Start standalone, integrate gradually.
Many archives run AI tools separately for 6-12 months, then integrate with existing systems once they’ve proven value.
The Future: What’s Coming Next
AI for archives is evolving rapidly. Here’s what to watch:
1. Proactive Discovery
Instead of waiting for researchers to request materials, AI will:
Identify potentially high-value materials automatically
Suggest connections between collections
Predict research interest based on patterns
Recommend priority processing
2. Conversational Search
Researchers will interact naturally:
“Show me correspondence about the 1976 drought”
“What do we have about indigenous land rights?”
“Find photographs from the 1920s showing [location]”
AI will understand intent and find relevant materials even if terminology doesn’t match exactly.
3. Automated Authority Control
AI will:
Link names across collections automatically
Suggest authority record creation
Identify variant forms
Maintain relationships
Update records as new information appears
4. Preservation Monitoring
AI-powered monitoring will:
Track environmental conditions
Predict deterioration
Recommend conservation priorities
Schedule preservation actions
Alert staff to problems
5. Cross-Institution Discovery
Imagine searching across all Latin American government archives simultaneously, with AI:
Understanding your question in any language
Searching materials in original languages
Identifying relevant materials across institutions
Providing translations as needed
This isn’t far off. The technology exists; implementation is happening now.
Conclusion: The RTA Legacy Continues
When the RTA developed their archive management model between 2010 and 2025, they created something enduring: a comprehensive framework for professional archival work grounded in international standards and adapted for Latin American contexts.
That framework didn’t become obsolete when AI emerged. Instead, AI made it possible to implement the RTA framework at a scale that was never feasible before.
The principles remain the same:
Proper arrangement and description
Appropriate preservation
Equitable access
Professional ethics
Service to society
What changed is our ability to apply those principles systematically across entire collections, at speed and scale, without sacrificing quality.
The archives I see succeeding today—truly serving researchers and citizens, managing backlogs, demonstrating value—are those combining the RTA’s solid framework with AI’s processing power. They maintain professional standards while leveraging technology to achieve what was previously impossible.
If you’re an archivist feeling overwhelmed by backlogs, impossible workloads, and limited resources, AI won’t solve everything. But it can make the RTA model’s vision achievable: professional archives that preserve history and provide meaningful access to all who seek it.
The RTA built the foundation. AI provides the tools. Together, they’re transforming government archives from institutions drowning in materials to institutions serving society effectively.
That transformation is happening now. The question isn’t whether to implement AI in your archive. It’s when and how.
Related Content:
Jacob Berry is an independent AI technology reviewer and digital privacy advocate with over 8 years of experience testing and analyzing emerging AI platforms. He has personally tested more than 500 AI-powered tools, specializing in comprehensive hands-on evaluation with a focus on user privacy, consumer protection, and ethical technology use.
Jacob’s review methodology emphasizes transparency and independence. Every platform is personally tested with real screenshots, detailed pricing analysis, and privacy assessment before recommendation. He holds certifications in AI Ethics & Responsible Innovation (University of Helsinki, 2023) and Data Privacy & Protection (IAPP, 2022).
Previously working in software quality assurance, privacy consulting, and technology journalism, Jacob now dedicates his efforts to providing honest, thorough AI platform reviews that prioritize reader value over affiliate commissions. All partnerships are clearly disclosed, and reviews are regularly updated as platforms evolve.
His work helps readers navigate the rapidly expanding AI marketplace safely and make informed decisions about which tools are worth their time and money.
Follow on Twitter: @Jacob8532
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