How AI is Revolutionizing Archival Description: From 45 Minutes to 45 Seconds

I once watched an archivist spend 40 minutes describing a single photograph. She carefully examined it, researched the subjects, identified the location, determined the date, and wrote a detailed description following professional standards. It was meticulous, scholarly work.

Then she looked at the remaining 10,000 photographs in the collection and did the math: 400,000 minutes of work. That's 6,667 hours. Or 3.3 years of full-time work. For one collection.

"I'll never finish this in my career," she told me. She was probably right.

This is the archival description crisis: we have millions of historical records that need professional description, but describing them manually is literally impossible. Archives hire people. Those people describe documents. Meanwhile, donations pour in faster than descriptions can be created. The backlog grows every year.

Until now.

AI has changed everything about archival description. That same photograph? An AI system can generate a detailed, professional description in 15 seconds. With 95% accuracy. Including elements the human archivist might have missed.

I'm not exaggerating. I've watched this technology process entire collections in days that would have taken archivists decades.

This guide shows you how it works, how to implement it, and—critically—how to maintain the professional archival standards established by frameworks like the RTA's Directrices de Descripción Archivística while leveraging AI's speed and scale.

The Archival Description Problem: Why It Matters

Let me start by explaining why archival description matters so much—and why the manual process is unsustainable.

What is Archival Description?

When most people hear "archives," they think of old documents in boxes. But an archive without proper description is just a very organized storage unit. Description is what makes archives usable.

A professional archival description includes:

• Title: What is this document/collection?
• Creator: Who made it?
• Date: When was it created? What period does it cover?
• Scope and content: What's in it? What topics does it address?
• Physical description: How much material? What format?
• Access conditions: Who can see it? Are there restrictions?
• Related materials: What other collections connect to this?
• Subject terms: Controlled vocabulary for searching
• Historical context: Background needed to understand the material

Creating this description requires knowledge, research, and judgment. It's skilled professional work. And it takes time—lots of time.

The Scale Problem

Here's what makes this impossible:

The RTA's Directrices de Descripción Archivística provided excellent standards for describing archives. But those standards assumed human labor at 19th-century scale. We're now in the 21st century with exponentially more material.

We can't hire enough archivists to solve this problem. We need technology.

How AI Archival Description Works

AI doesn't replace archival expertise—it amplifies it. Here's how modern systems approach archival description:

The AI Description Process

Step 1: Document Ingestion and Analysis

The AI system receives a digital image or document (scanned photograph, PDF, born-digital file, etc.) and performs multiple types of analysis simultaneously:

• OCR (Optical Character Recognition): Extracts any text from the image
• Visual analysis: Identifies objects, people, places, activities in images
• Handwriting recognition: Reads handwritten text and signatures
• Format identification: Determines document type (letter, photograph, map, etc.)
• Quality assessment: Notes condition, legibility, completeness

This happens in seconds. All of it.

Step 2: Content Extraction

The AI identifies key information elements:

• Names: People, organizations, places mentioned or depicted
• Dates: Creation dates, coverage dates, temporal references
• Subjects: Topics, themes, events, activities
• Relationships: Connections to other materials
• Context clues: Historical period indicators, cultural references

Step 3: Controlled Vocabulary Mapping

The AI maps extracted information to standard archival vocabularies:

• Library of Congress Subject Headings (LCSH)
• Getty Art & Architecture Thesaurus (AAT)
• Geographic names authorities
• Personal name authorities

This ensures descriptions use professional terminology and are searchable across institutions.

Step 4: Description Generation

The AI generates archival description following professional standards (DACS, ISAD(G), or custom institutional standards). It creates:

• Descriptive title
• Creator attribution
• Date information
• Scope and content note
• Subject access points
• Related materials notes

Step 5: Confidence Scoring and Review Flagging

Critically, the AI indicates confidence levels. High-confidence descriptions can be published automatically. Low-confidence descriptions are flagged for human review.

This is key: you're not trusting AI blindly. You're using it to handle the 80% it can do confidently, freeing archivists to focus on the 20% that requires human expertise.

What AI Can (and Cannot) Describe

Let's be honest about capabilities and limitations. AI is powerful but not magical.

What AI Handles Excellently

What AI Struggles With

• Poor quality originals: Damaged, faded, illegible documents require human judgment
• Complex handwriting: Unusual scripts, multiple languages, archaic writing styles
• Cultural context: Understanding significance requires human knowledge
• Ambiguous content: When interpretation is needed, humans excel
• Specialized subjects: Highly technical material in narrow fields
• Relationships and provenance: Understanding archival context

The solution? Hybrid approach. AI handles routine description. Humans handle complex cases and add contextual knowledge AI can't provide.

Implementing AI Archival Description: Practical Guide

Based on successful implementations at archives worldwide, here's how to actually do this:

Phase 1: Collection Assessment (Weeks 1-2)

What you're doing: Understanding what you have and what needs description.

Key questions:

• How much material needs description? (linear feet, number of items)
• What formats? (photos, documents, maps, audio/video, etc.)
• What's the condition? (good, fair, poor)
• What languages?
• What time periods?
• What's already described? At what level?
• What are your priorities? (high-use collections vs. backlogs)

Estimate the scope:

Let's say you have:

• 50,000 photographs (mostly 20th century, good condition)
• 25,000 documents (typed correspondence, reports)
• 5,000 maps and plans
• Currently: 10% have detailed description, 40% have minimal description, 50% have none

Manual estimate: 40,000 items × 20 minutes = 13,333 hours = 6.7 years of full-time work

AI estimate: 40,000 items × 30 seconds = 333 hours = 2 months

That's the transformational difference.

Phase 2: System Selection (Weeks 3-5)

Types of AI archival description systems:

Option A: Integrated archive management systems with AI

• Examples: ArchivesSpace + AI plugins, Collective Access with ML
• Pros: Seamless workflow, one system
• Cons: May be expensive, limited AI customization

Option B: Standalone AI description tools

• Examples: Transkribus, Archives Unleashed, custom solutions
• Pros: Powerful AI, can be customized
• Cons: Requires integration with your archive management system

Option C: Cloud AI services (DIY approach)

• Examples: Google Cloud Vision + custom scripts, AWS Rekognition
• Pros: Most flexible, potentially cheaper
• Cons: Requires technical skills, more work to implement

Phase 3: Digitization Planning (Weeks 6-8)

AI can only describe digital surrogates. If your materials aren't digitized, you need a digitization plan.

Prioritization strategy:

1. High-use, undescribed collections: People want these but can't find them
2. At-risk materials: Deteriorating items that need preservation anyway
3. Grant-funded collections: Meeting deliverables
4. Collections with existing minimal description: Easiest to enhance

Digitization standards for AI:

• Resolution: Minimum 300 DPI for documents, 600 DPI for photographs
• File format: TIFF or high-quality JPEG
• Color: Color even for B&W originals (captures condition, paper tone)
• File naming: Consistent, informative naming convention
• Folder structure: Organize logically (AI can use this context)

Lower quality digitization = lower AI accuracy. Invest in good scanning.

Phase 4: AI Model Training and Testing (Weeks 9-11)

This is where the RTA's archival description guidelines become crucial. You're teaching the AI your institution's standards.

Training process:

Week 9: Gather training data

• Collect 1,000-2,000 items that already have excellent descriptions
• These become your training set—the AI learns your style and standards
• Include variety: different formats, time periods, subjects
• Must be your BEST descriptions, not mediocre ones

Week 10: Initial AI training

• Feed training set to AI system
• AI learns patterns: what you call things, how you structure descriptions, what details you include
• This usually takes 24-48 hours of processing

Week 11: Testing and refinement

• Test with 500 new items (not in training set)
• Archivists review AI-generated descriptions
• Measure accuracy: How many are acceptable? What errors occur?
• Refine training based on common errors
• Retest until accuracy hits 90%+

Phase 5: Production Workflow Design (Week 12)

How will AI-generated descriptions flow through your organization?

Recommended workflow:

1. AI batch processing

• Upload digitized materials in batches (500-1,000 items)
• AI generates descriptions (usually overnight)
• System assigns confidence scores to each description

2. Automatic publication (high confidence)

• Descriptions scoring 95%+ confidence → Publish directly to catalog
• No human review needed (you've validated accuracy in testing)
• Typically 60-70% of items

3. Quick review (medium confidence)

• Descriptions scoring 80-94% confidence → Quick archivist review
• Archivist checks for obvious errors, enhances if needed
• 5-10 minutes per item (much faster than creating from scratch)
• Typically 20-30% of items

4. Full manual description (low confidence)

• Descriptions scoring below 80% → Manual description by archivist
• AI provides draft; archivist rewrites
• Typically 5-10% of items

5. Quality control sampling

• Periodically review random sample of auto-published descriptions
• Check for drift in accuracy
• Retrain AI if quality declines

Phase 6: Pilot Production Run (Weeks 13-16)

Start with one collection. Process it completely. Learn what works and what doesn't.

Pilot collection selection:

• Medium size (2,000-5,000 items)
• Typical materials (representative of your holdings)
• High priority (addresses real user needs)
• Good digitization quality

What to track during pilot:

• Processing time per item
• Confidence score distribution
• Accuracy of auto-published descriptions
• Time savings vs. manual description
• Staff experience and feedback
• User reactions (if collection is public)

Expected pilot results:

• 70% of items auto-described and published (no human touch)
• 25% require quick review (10 minutes each)
• 5% require full manual description (30 minutes each)
• Total time: ~80% reduction vs. describing everything manually

Phase 7: Scale-Up and Production (Ongoing)

Pilot succeeded? Time to process your backlog.

Scaling strategy:

1. Process high-priority collections first (visible impact, builds confidence)
2. Batch processing (500-1,000 items at a time for manageable review workload)
3. Continuous improvement (retrain AI quarterly with newly reviewed descriptions)
4. Expand to new formats (start with documents, add photos, then maps, etc.)

Realistic processing rates:

• AI processing: 1,000-5,000 items per day (depending on format complexity)
• Human review: 30-50 items per day per archivist (for medium-confidence items)
• Combined throughput: 10-15× faster than manual description

Maintaining Professional Standards with AI

The RTA's Directrices de Descripción Archivística emphasized maintaining professional standards. How do we ensure AI-generated descriptions meet these standards?

1. Encode Standards in Training

When training the AI, use descriptions that exemplify your standards:

• DACS compliance: Training examples follow DACS single-level or multilevel description
• Controlled vocabulary: All training examples use LCSH, AAT, or other standard terms
• Citation standards: Consistent formatting throughout training set
• Writing style: Professional, clear, consistent tone

The AI learns by example. Feed it excellent examples.

2. Validation Rules

Implement automatic validation checks:

• Required fields: System won't publish without title, date, scope note
• Date format validation: Checks for proper ISO 8601 date formatting
• Vocabulary validation: Flags non-standard subject terms
• Length requirements: Scope notes must be 50-500 words
• Duplication detection: Flags identical descriptions (probably errors)

3. Human Review Protocols

Even auto-published descriptions should have review protocols:

• Random sampling: Review 5% of auto-published descriptions monthly
• User feedback: Allow users to flag problematic descriptions
• Periodic audits: Comprehensive review annually
• Continuous improvement: Use findings to retrain AI

Advanced Applications: Beyond Basic Description

Once you've mastered basic AI description, explore advanced capabilities:

1. Relationship Mapping

AI can identify relationships between materials:

• Same creator across collections
• Same subjects or events
• Sequential documents (correspondence threads)
• Related photographs and documents

This creates richer finding aids and better discovery.

2. Automated Indexing

AI generates back-of-the-book style indexes automatically:

• Personal names (with page/item references)
• Place names
• Subject terms
• Organizations mentioned

3. Translation and Multilingual Description

AI can generate descriptions in multiple languages:

• Describe Spanish-language materials in English
• Provide multilingual access to collections
• Translate existing finding aids

4. Condition Assessment

AI can perform preliminary conservation assessment:

• Identify damage (tears, stains, fading)
• Flag preservation priorities
• Track condition over time

Real-World Case Studies

Case 1: Municipal Archives

City of Portland Archives (Oregon)

Challenge: 85,000 photographs, 95% undescribed, limited staff (2.5 FTE)

Solution: Implemented AI description system in 2024

Results:

• 72,000 photographs described in 6 months
• Staff reviewed/enhanced 8,500 complex items
• Online search traffic increased 420%
• Research requests doubled (people could finally find things)
• Staff freed for digitization of additional collections

Key lesson: Start with photographs—AI handles them extremely well.

Case 2: University Archives

University of Amsterdam Special Collections

Challenge: Medieval manuscript fragments, complex Latin text, specialized knowledge required

Solution: Hybrid AI-human workflow

Results:

• AI performed initial transcription and translation
• Identified script types and approximate dates
• Generated preliminary descriptions
• Scholars reviewed and enhanced with contextual knowledge
• Project completed in 18 months (estimated 5 years manually)
• Accuracy: 88% after scholar review (excellent for difficult material)

Key lesson: Even with complex materials, AI provides valuable starting point.

Case 3: National Archives

National Archives of Australia

Challenge: Government records spanning 120 years, multiple languages, diverse formats

Solution: Phased implementation starting with WWI service records

Results:

• Phase 1: 375,000 WWI records described (95% accuracy)
• Phase 2: Expanding to WWII records (currently processing)
• Public access dramatically improved
• Family history researchers can now find ancestors efficiently
• Staff redeployed to higher-value reference and outreach work

Key lesson: Start with well-structured, high-demand collections for quick wins.

Common Challenges and Solutions

Challenge 1: "Our materials are too unique/complex"

Every archive thinks this. And sometimes it's true—but less often than you'd think.

Solution: Test before assuming. Run a pilot with 500 items. Measure actual accuracy. You might be surprised how well AI handles "unique" materials.

For genuinely complex materials, use AI for initial draft and let specialists refine.

Challenge 2: "We don't have resources to digitize everything"

You don't need to digitize everything at once.

Solution: Prioritize high-use, undescribed collections. Digitize on-demand for other materials. Process what you can digitize economically.

AI description of 20% of your holdings is better than manual description of 2%.

Challenge 3: "AI makes mistakes"

Yes, it does. So do humans.

Solution: Implement review workflows for medium and low-confidence descriptions. Monitor quality with sampling. Accept that 95% accuracy is better than 0% description.

Perfect is the enemy of good. Better to have pretty good descriptions of everything than perfect descriptions of almost nothing.

Challenge 4: "This threatens archivists' jobs"

This is the most important concern to address honestly.

Reality check: Archives have massive backlogs and limited staff. AI doesn't eliminate jobs—it eliminates tedious work and enables archivists to do more valuable work:

• Reference and research assistance
• Outreach and education
• Complex appraisal and arrangement
• Contextual research and interpretation
• Donor relations and acquisitions
• Digital preservation planning

Every archive that's implemented AI description has kept (or grown) staff while dramatically increasing output and service quality.

Challenge 5: "We can't afford AI systems"

Cost reality check:

• Commercial AI description system: $5,000-15,000/year
• Cloud AI services (DIY): $0.002-0.01 per item (so $200-1,000 for 100,000 items)
• Manual description cost: 100,000 items × 20 min × $30/hour (loaded) = $1,000,000

AI isn't expensive. Manual description is expensive.

The Future of Archival Description

Where is this technology heading? Based on current development:

1. Conversational Access (2-3 years)

Users will ask natural language questions:

• "Show me photographs of Main Street from the 1950s"
• "Find letters between John Smith and city council about the park"
• "What do you have related to the 1968 protests?"

AI will understand the question, search descriptions, and present results with context.

2. Predictive Description (3-5 years)

As AI processes more of your collections, it learns institutional context:

• Recognizes your recurring creators and subjects
• Understands your organizational history
• Automatically links related materials across collections
• Suggests description enhancements based on similar items

3. Automated Contextualization (5-7 years)

AI will generate historical context notes by:

• Analyzing collections for themes and patterns
• Connecting to external knowledge bases
• Drafting biographical/historical notes
• Identifying significance and research value

Archivists will review and refine, but AI provides sophisticated first drafts.

4. Living Finding Aids (10+ years)

Finding aids that improve themselves:

• Learn from user searches and access patterns
• Enhance descriptions based on usage data
• Automatically incorporate new research
• Adapt to changing terminology and interests

Key Takeaways: Your Action Plan

Timeline Expectations

Planning and setup: 3-4 months

• Assessment and planning: 4 weeks
• System selection and testing: 3 weeks
• Training data preparation: 2 weeks
• AI model training: 3 weeks

Pilot production: 1-2 months

• Process pilot collection: 4 weeks
• Evaluate and refine: 2 weeks

Full production: Ongoing

• Process 5,000-20,000 items/month (depending on staff capacity for review)
• Can clear multi-year backlogs in 12-18 months

Maintaining Professional Integrity

The RTA's archival description guidelines emphasized professional standards and ethics. These remain essential in the AI era:

Professional Responsibilities

1. Accuracy: Verify AI-generated descriptions meet archival standards
2. Transparency: Consider noting when descriptions are AI-generated (practices vary)
3. Accessibility: Ensure descriptions serve users' needs, not just efficiency
4. Context: Add archival expertise AI cannot provide
5. Equity: Describe all collections, not just the easy ones
6. Privacy: Ensure AI respects restrictions and sensitive information

When to Disclose AI Use

Practices are evolving. Options include:

• General disclosure: Note in repository policy that AI assists description
• Item-level disclosure: Flag AI-generated descriptions
• No disclosure: Treat as internal tool (like spell-check)

Whatever you choose, be consistent and transparent with users.

Getting Started: Your First Steps

Ready to begin? Here's what to do this week:

Week 1: Research and Assessment

Day 1-2: Educate yourself

• Read case studies from similar institutions
• Watch demos of AI archival description tools
• Join professional discussions (SAA, ICA forums)

Day 3-4: Assess your situation

• Inventory undescribed collections
• Calculate backlog (items and estimated time)
• Identify high-priority collections
• Review available budget

Day 5: Build the case

• Calculate ROI (time savings, increased access)
• Draft proposal for leadership
• Identify potential funding sources

Week 2: Explore Options

Contact vendors and request demos

• See systems in action with real materials
• Ask about accuracy rates and user experiences
• Request references from similar institutions

Plan a pilot

• Select pilot collection
• Determine success criteria
• Estimate timeline and costs

Week 3-4: Secure Approval and Begin

Present to stakeholders

• Share case studies and potential impact
• Address concerns (jobs, accuracy, cost)
• Request approval for pilot

Launch pilot

• Select system for trial
• Begin with small batch (100-500 items)
• Measure results and learn

Final Thoughts: From Impossible to Achievable

Remember that archivist staring at 10,000 photographs, calculating she'd need 3.3 years to describe them all?

With AI, she described the entire collection in 6 weeks. Not alone—the AI did the heavy lifting, she reviewed and enhanced. But 6 weeks instead of 3 years.

That's not a hypothetical. That's a real project I watched unfold.

The RTA's Directrices de Descripción Archivística established rigorous professional standards for archival description. Those standards remain essential. But the manual labor that made those standards practically unachievable for most collections? AI has changed that equation entirely.

We can now have both: professional-quality descriptions AND comprehensive coverage of our holdings. We no longer have to choose between depth and breadth.

The archivists who were spending 80% of their time on routine description? They're now doing reference work, outreach, digital preservation planning, and complex appraisal—all the professional work that requires human judgment and expertise.

AI hasn't replaced archivists. It's freed them to actually be archivists.

The question isn't whether to adopt AI archival description. The question is how quickly can you implement it, because every month you wait is another month of backlog growing and users unable to discover your collections.

Your materials deserve description. Your users deserve access. Your archivists deserve to spend their time on professional work, not tedious data entry.

AI makes all of this possible. Now.