AI Implementation Use Cases by Government Function

ByJacob Berry

From 2010 to 2025, this page described the RTA’s working groups—specialized teams of professionals from member organizations who collaborated on specific areas: archives, transparency and access, records management, and technology.

These working groups weren’t just discussion forums. They were communities of practice where government professionals tackled real challenges together: How do we process massive archival backlogs? What’s the best way to handle complex information requests? How can we implement retention schedules when staff is limited? How do we balance transparency with privacy?

The working groups developed frameworks, shared solutions, and supported each other through implementation challenges. They represented different professional specializations within government information management, each with unique needs and perspectives.

Today, this page serves a similar purpose—but instead of describing working groups, we’re showing how different government functions are using AI to address those same challenges. Think of these as use cases organized by professional role, just as the RTA working groups were organized by specialty.

If you’re a transparency officer, records manager, archivist, IT director, or legal counsel wondering “how would AI help MY work specifically?”, this page is for you.

Transparency & Access Officers {#transparency-officers}

RTA Working Group Legacy: The RTA’s transparency working group focused on implementing access-to-information laws, managing citizen requests, and promoting proactive disclosure.

Modern Challenge: Most transparency authorities are overwhelmed—thousands of requests annually, complex laws, tight deadlines, limited staff.

How AI Helps: Automates the mechanical work, letting officers focus on complex legal and policy judgments.

Use Case 1: Automating Request Intake and Classification

The Challenge: A state transparency commission receives 5,000 requests annually. Each request requires:


  • Reading and understanding what’s being requested

  • Determining which agency/department has responsive records

  • Classifying by subject matter for tracking

  • Assigning to appropriate staff

  • Setting deadline alerts

Manual process: 15-20 minutes per request = 1,250-1,667 hours annually

AI Solution:


  • Natural language processing reads request text

  • AI categorizes by subject, agency, and complexity

  • Automatically routes to appropriate staff

  • Sets deadlines based on legal requirements

  • Flags unusual or complex requests for senior review

AI process: 2-3 minutes of staff review per request = 167-250 hours annually

Time savings: 1,000-1,400 hours per year = reclaiming half a full-time position

Real Example – Chilean Municipality: Implemented AI request classification:


  • Average classification time: 18 minutes → 3 minutes

  • Staff freed up for complex requests requiring judgment

  • Accuracy improved (AI doesn’t miss categories when rushed)

  • Citizen satisfaction increased (faster acknowledgment)

Use Case 2: Intelligent Document Search for Requests

The Challenge: Request: “All correspondence between the mayor’s office and ABC Construction Company regarding the Smith Street Bridge project from 2020-2023.”

Manual search:


  • Email multiple departments

  • Search email systems, network drives, physical files

  • Try different keyword combinations

  • Chase non-responsive departments

  • Review everything found for relevance

  • Time: 8-15 hours

AI Solution: AI searches across all systems simultaneously:


  • Understands “correspondence” includes emails, letters, memos

  • Knows “ABC Construction” might appear as “ABC Const.” or “ABC Construction Co.”

  • Recognizes “Smith Street Bridge” might be called “Smith St. Bridge Project” or “Bridge Reconstruction – Smith Street”

  • Identifies relevant documents based on content, not just keywords

  • Ranks results by likely relevance

AI process: 30-60 minutes (mostly reviewing AI’s findings)

Time savings: 7-14 hours per request

At 500 requests/year: 3,500-7,000 hours saved = 2-3.5 full-time positions

Real Example – Mexican Transparency Authority: Implemented intelligent search:


  • Response time: 21 days → 7 days average

  • Completeness improved (AI finds documents humans miss)

  • Staff handle 40% more requests with same team

  • Backlog eliminated

Use Case 3: Automated Preliminary Redaction

The Challenge: Found 300 pages of responsive documents. Must review each page for:


  • Personal information (names, addresses, IDs, phone numbers)

  • Commercially sensitive information

  • Internal deliberations (if protected)

  • Security classifications

  • Other legally protected categories

Manual process: 2-3 minutes per page = 10-15 hours for 300 pages

AI Solution:


  • Scans all 300 pages

  • Identifies potential protected information

  • Categorizes by protection type

  • Flags for human review with suggested redactions

  • Creates redacted and unredacted versions

AI process: 1-2 hours (reviewing AI suggestions, making final decisions)

Time savings: 8-13 hours per 300-page request

Critical: Human makes final legal determination. AI just identifies candidates.

Real Example – Colombian Government Archive: Implemented automated redaction:


  • Request processing time: 15-20 hours → 2-3 hours

  • Consistency improved (AI doesn’t miss SSNs when tired)

  • Quality increased (more thorough review possible in less time)

  • Staff morale improved (less tedious work)

Use Case 4: Proactive Disclosure Automation

The Challenge: Law requires proactively publishing certain document types (contracts, budgets, meeting minutes, etc.) within specific timeframes.

Manual process:


  • Someone must identify disclosure-required documents

  • Manually redact sensitive information

  • Create metadata

  • Upload to transparency portal

  • Track compliance

Result: Many agencies only publish 20-30% of required documents

AI Solution:


  • Monitors document repositories

  • Identifies disclosure-required documents automatically

  • Applies appropriate redactions

  • Generates metadata

  • Schedules publication

  • Tracks compliance

Impact:


  • Compliance rate: 20-30% → 80-90%

  • Staff time: 20 hours/week → 4 hours/week

  • Reduces citizen requests (information already available)

  • Demonstrates transparency commitment

Real Example – Brazilian Municipal Government:


  • Automated proactive disclosure for contracts, budgets, ordinances

  • Publication rate increased from 25% to 85%

  • Citizen information requests decreased 30% (answers already published)

  • Won transparency award from oversight body

Use Case 5: Response Quality Assurance

The Challenge: Before sending responses to citizens, must verify:


  • All responsive documents included?

  • Redactions appropriate and consistent?

  • Response addresses all parts of request?

  • Compliance with legal requirements?

Manual QA: Senior officer reviews everything = bottleneck

AI Solution:


  • AI performs preliminary QA check

  • Compares response to original request

  • Flags potential issues (incomplete response, inconsistent redactions, missed deadlines)

  • Highlights areas needing human attention

  • Senior officer focuses review on flagged issues

Impact:


  • QA time: 30 minutes per request → 10 minutes per request

  • Quality improves (AI catches things humans miss)

  • Senior officers can review 3x more responses

  • Faster turnaround

Records Managers {#records-managers}

RTA Working Group Legacy: The RTA’s records management working group developed classification schemes, retention schedules, and governance frameworks.

Modern Challenge: Implementing those frameworks manually across millions of documents is impossible with typical staffing.

How AI Helps: Applies the RTA’s proven methodologies systematically to every document automatically.

Use Case 6: Automated Document Classification

The Challenge: Agency creates 50,000 documents/year. Each needs classification according to functional classification scheme (per RTA framework).

Manual classification: 5 minutes per document = 4,167 hours annually = 2+ FTE

AI Solution:


  • AI reads document content

  • Understands function and activity

  • Assigns appropriate classification code

  • Applies metadata automatically

  • Flags uncertain classifications for human review

AI process: 30 seconds per document + 1 minute human review for flagged items = 500-700 hours annually

Time savings: 3,500-3,700 hours = nearly 2 full-time positions

Accuracy: 93-96% correct classification (exceeds typical human accuracy of 88-92%)

Real Example – Government Ministry: Implemented AI classification:


  • Classification backlog eliminated in 6 months

  • Ongoing classification happens in real-time

  • Staff focus on complex documents requiring expert judgment

  • Findability improved dramatically (consistent classification)

Use Case 7: Retention Schedule Automation

The Challenge: After classifying documents, must:


  • Apply appropriate retention rule

  • Calculate destruction date

  • Track legal holds

  • Manage disposition process

  • Maintain audit trails

Manual process: Complex, error-prone, often not done consistently

AI Solution:


  • Linked to classification (AI knows retention rules for each class)

  • Calculates destruction dates automatically

  • Monitors for legal holds

  • Flags records eligible for destruction

  • Generates disposition documentation

  • Creates compliance reports

Impact:


  • Retention compliance: 60-70% → 95-98%

  • Audit preparation time: weeks → hours

  • Legal risk reduced (proper retention)

  • Storage costs reduced (systematic destruction)

Real Example – State Agency:


  • Implemented automated retention management

  • Reduced off-site storage costs by 40% (systematic destruction of eligible records)

  • Passed records audit for first time in 5 years

  • Litigation hold compliance improved dramatically

Use Case 8: Metadata Quality Enhancement

The Challenge: Documents have incomplete or inconsistent metadata:


  • Missing dates, authors, subjects

  • Inconsistent terminology

  • No relationships between related documents

  • Makes searching nearly impossible

AI Solution:


  • Extracts metadata from document content and properties

  • Standardizes terminology using controlled vocabularies

  • Identifies relationships between documents

  • Fills gaps in existing metadata

  • Maintains consistency across collections

Impact:


  • Metadata completeness: 40-50% → 90-95%

  • Search success rate: 60% → 95%

  • User satisfaction dramatically improved

  • Enables effective information governance

Real Example – University Administration:


  • Applied AI to 10 years of administrative records

  • Metadata completeness increased from 45% to 92%

  • Staff can now find documents reliably

  • Enabled effective records management program

Use Case 9: Email Records Management

The Challenge: Email is where most government business happens, but:


  • Employees don’t file emails consistently

  • Personal and business emails mixed

  • Compliance requirements unclear to users

  • Records lost when people leave

AI Solution:


  • Analyzes email content automatically

  • Identifies business records vs. personal messages

  • Classifies by function

  • Applies retention automatically

  • Preserves important records even if not filed manually

Impact:


  • Email records capture: 30-40% → 85-95%

  • User burden reduced (mostly automatic)

  • Compliance improved

  • Litigation risk reduced

Real Example – Municipal Government:


  • Implemented AI email management

  • Captured 8,000+ business emails previously going unmanaged

  • Employees report less burden (less manual filing)

  • Legal counsel confident in email retention compliance

Use Case 10: Vital Records Protection

The Challenge: Identifying and protecting vital records (essential for continuing operations during disaster) requires:


  • Knowing which records are vital

  • Ensuring proper protection

  • Maintaining currency as operations change

AI Solution:


  • Analyzes records to identify vital characteristics

  • Recommends vital records designation

  • Monitors protection compliance

  • Alerts when vital records aren’t properly secured

  • Updates as operations change

Impact:


  • Vital records identification: complete and current

  • Protection compliance: 100%

  • Disaster recovery capability: verified

  • Organizational resilience: improved

Archivists {#archivists}

RTA Working Group Legacy: The RTA’s archives working group developed archival description standards, preservation guidelines, and access policies.

Modern Challenge: Decades of unprocessed backlogs, limited staff, increasing digitization demands.

How AI Helps: Processes collections at scale while maintaining archival principles and standards.

Deep dive into archival AI →

Use Case 11: Backlog Processing at Scale

The Challenge: 30 years of accessioned but unprocessed collections:


  • 10,000 linear feet of materials

  • Traditional processing estimate: 12-15 years with current staff

  • Materials inaccessible to researchers

  • Unable to respond to reference requests

AI Solution:


  • Digitize priority collections (or work with born-digital materials)

  • AI generates preliminary finding aids

  • Creates collection-level descriptions

  • Identifies series and subseries

  • Generates access points

  • Archivists review, enhance, and publish

Implementation:


  • Year 1: AI processes 3,000 linear feet

  • Year 2: AI processes 4,000 linear feet

  • Year 3: Complete backlog processed

Result: 30-year backlog resolved in 3 years

Real Example – Chilean University Archive:


  • 90% of collection was undescribed

  • Implemented AI description tools

  • 85% now described and accessible online

  • Researcher usage increased 300%

  • Archive’s value demonstrated to administration

Use Case 12: Archival Description Enhancement

The Challenge: Existing finding aids are minimal:


  • Collection-level descriptions only

  • Limited access points

  • Researchers can’t determine relevance without visiting

  • Reference staff struggle to provide guidance

AI Solution:


  • Analyzes existing materials

  • Enhances descriptions with content analysis

  • Generates additional access points

  • Creates folder-level descriptions

  • Identifies related materials across collections

Impact:


  • Description depth: collection-level → series and folder-level

  • Access points: 5-10 per collection → 50-100 per collection

  • Researcher satisfaction: dramatically improved

  • Online discovery: actually possible now

Real Example – State Historical Society:


  • Applied AI to 200 minimally-described collections

  • Generated folder-level descriptions

  • Added 15,000+ access points

  • Online searches increased 400%

  • Successful remote research now possible

Use Case 13: Multilingual Archives Access

The Challenge: Collections in multiple languages:


  • Portuguese, Spanish, German, Italian (immigration records)

  • Researchers need to search across languages

  • Description in original language only

  • Limited access for non-speakers

AI Solution:


  • Transcribes multilingual materials

  • Creates descriptions in multiple languages

  • Enables cross-language search

  • Maintains original language fidelity

  • Links related materials regardless of language

Impact:


  • International researcher access: dramatically expanded

  • Educational use: increased

  • Community engagement: improved

  • Cultural heritage: better preserved and accessible

Real Example – Brazilian Municipal Archive:


  • Applied AI to German and Italian immigration records

  • Generated Portuguese translations

  • Added English descriptions

  • International research requests increased 600%

  • Local schools using materials in curriculum

Use Case 14: Photograph and Visual Materials

The Challenge: Large photograph collections:


  • Minimal description (often just date and photographer)

  • Subjects unknown

  • Can’t identify people, places, events

  • Limited discoverability

AI Solution:


  • Visual recognition identifies subjects

  • Facial recognition suggests people (with appropriate controls)

  • Identifies locations

  • Recognizes objects and activities

  • Generates descriptive metadata

  • Community members can contribute identifications

Impact:


  • Photograph discoverability: transformed

  • Community engagement: high

  • Cultural heritage: preserved

  • Access: dramatically improved

Real Example – Regional Archive:


  • 50,000 photographs minimally described

  • AI generated preliminary descriptions

  • Community crowdsourcing added identifications

  • 80% of photographs now meaningfully described

  • Heavy educational and research use

Use Case 15: Preservation Planning

The Challenge: Limited conservation resources:


  • Can’t preserve everything

  • Need to prioritize high-value materials

  • Condition assessment time-consuming

  • Deterioration monitoring difficult

AI Solution:


  • Analyzes digitized images for condition

  • Identifies deterioration patterns

  • Predicts preservation needs

  • Recommends treatment priorities

  • Monitors condition over time

  • Optimizes preservation budget

Impact:


  • Data-driven preservation decisions

  • Early intervention for problems

  • Maximized preservation budget effectiveness

  • Important materials protected

IT Directors & Systems Teams {#it-teams}

RTA Working Group Legacy: The RTA’s technology working group focused on systems interoperability, digital preservation, and technical infrastructure.

Modern Challenge: Integrating AI tools with legacy systems, ensuring security, managing complex environments.

How AI Helps: Provides modern capabilities while integrating with existing investments.

Use Case 16: System Integration & Interoperability

The Challenge: Government IT environments are complex:


  • Email system (Microsoft 365 or Google Workspace)

  • Document management system (may be old)

  • Records management system

  • Financial system

  • HR system

  • Case management systems

  • Legacy databases

  • Network file shares

AI needs to work across all these without replacing everything.

AI Solution:


  • API-based connections to each system

  • Middleware for systems without APIs

  • Unified search across all systems

  • Centralized policy management

  • Single pane of glass for users

  • Maintains existing system investments

Implementation Approach:


  • Phase 1: Connect highest-value systems

  • Phase 2: Add additional systems incrementally

  • Phase 3: Retire systems strategically when appropriate

Real Example – State Government Department:


  • Connected 12 different systems

  • AI provides unified search and classification

  • Users access through single interface

  • Avoided expensive system replacement

  • Added capabilities without disruption

Use Case 17: Security & Compliance Management

The Challenge: Security and compliance in government require:


  • Access controls by classification level

  • Audit trails for everything

  • Data residency compliance

  • Encryption requirements

  • Incident response

  • Regular security assessments

AI Solution:


  • AI-powered security monitoring

  • Anomaly detection (unusual access patterns)

  • Automated compliance reporting

  • Policy enforcement

  • Incident detection and response

  • Audit trail automation

Impact:


  • Security posture: improved

  • Compliance reporting: automated (weeks → hours)

  • Incident detection: faster

  • Audit preparation: simplified

Real Example – Federal Agency:


  • Implemented AI security monitoring

  • Detected 3 security incidents that manual monitoring missed

  • Compliance reporting automated (quarterly reports: 40 hours → 3 hours)

  • Passed security audit with zero findings

Use Case 18: Performance Optimization

The Challenge: Information systems slow as volumes grow:


  • Search takes minutes

  • Users frustrated

  • Productivity impacted

  • Adding hardware expensive

AI Solution:


  • Intelligent caching

  • Predictive pre-loading

  • Optimized indexing

  • Load balancing

  • Performance monitoring and auto-tuning

Impact:


  • Search speed: minutes → seconds

  • User satisfaction: dramatically improved

  • Infrastructure costs: reduced (better optimization)

  • System capacity: increased without hardware

Use Case 19: Disaster Recovery & Business Continuity

The Challenge: Ensuring information availability during disasters:


  • Identifying critical information

  • Maintaining backups

  • Testing recovery procedures

  • Documenting dependencies

AI Solution:


  • Identifies critical information based on usage patterns

  • Monitors backup compliance

  • Predicts failure risks

  • Automates recovery testing

  • Maintains current documentation

Impact:


  • Recovery time objectives: met

  • Business continuity: assured

  • Disaster preparedness: verified

  • Risk: reduced

⚖️ Legal & Compliance Officers {#legal-compliance}

RTA Working Group Legacy: While not a formal working group, legal and compliance considerations were central to all RTA work.

Modern Challenge: Managing legal risk, ensuring compliance, responding to litigation holds.

How AI Helps: Systematic compliance, reduced risk, faster legal response.

Use Case 20: Litigation Hold Management

The Challenge: When litigation threatened:


  • Must identify all potentially relevant documents

  • Preserve across all systems

  • Prevent destruction even if scheduled

  • Track compliance

  • Report to legal counsel

Manual process: Labor-intensive, error-prone, slow

AI Solution:


  • Identifies relevant documents based on hold criteria

  • Places automatic preservation

  • Overrides routine destruction

  • Tracks preserved materials

  • Generates compliance reports

  • Alerts if preservation violated

Impact:


  • Hold implementation: hours instead of days

  • Coverage: comprehensive

  • Compliance: verified

  • Legal risk: reduced

  • Counsel confidence: high

Real Example – Municipal Government:


  • Litigation hold issued

  • AI identified 15,000 relevant documents across 8 systems

  • All preserved within 4 hours

  • Complete documentation for counsel

  • No spoliation risk

Use Case 21: Privacy Compliance (GDPR, LGPD, etc.)

The Challenge: Data protection laws require:


  • Knowing where personal data is stored

  • Responding to access/deletion requests

  • Minimizing retention

  • Documenting processing

  • Breach notification

Manual compliance: Nearly impossible at scale

AI Solution:


  • Discovers personal data across systems

  • Maps data flows

  • Responds to subject access requests automatically

  • Enforces retention limits

  • Monitors compliance

  • Generates required documentation

Impact:


  • Privacy compliance: systematic

  • Subject request response: days → hours

  • Breach risk: reduced

  • Regulatory confidence: improved

Real Example – Regional Authority:


  • LGPD compliance required (Brazil’s GDPR equivalent)

  • AI discovered personal data in 15 different systems

  • Automated subject access request responses

  • Reduced response time from 14 days to 2 days

  • Passed regulatory inspection

Use Case 22: Contract & Legal Document Management

The Challenge: Managing legal documents and contracts:


  • Tracking obligations and deadlines

  • Ensuring renewals not missed

  • Analyzing risks

  • Finding precedents

  • Maintaining versions

AI Solution:


  • Extracts key terms automatically

  • Tracks obligations and deadlines

  • Alerts before expirations

  • Identifies risks

  • Finds similar contracts for precedent

  • Maintains complete version history

Impact:


  • Missed deadlines: eliminated

  • Contract risk: reduced

  • Legal research: faster

  • Compliance: improved

Executive Leadership {#leadership}

For agency directors, CIOs, and executive leadership making strategic decisions.

Use Case 23: Strategic Planning & Resource Allocation

The Challenge: Leadership needs to:


  • Understand current state

  • Identify improvement priorities

  • Allocate resources effectively

  • Demonstrate value to stakeholders

  • Plan multi-year transformations

AI Provides:


  • Data-driven insights

  • Performance metrics

  • Benchmarking

  • ROI analysis

  • Implementation roadmaps

Real Example – Transparency Authority Director:

  • Used AI analytics to demonstrate:

    • Current backlog size and growth rate

    • Projected resources needed under current approach

    • Alternative: AI tools at 1/3 the cost

    • 3-year transformation roadmap

  • Secured budget approval

  • Implemented successfully

  • Now considered model agency

Use Case 24: Demonstrating Value & Justifying Investment

The Challenge: Securing resources requires demonstrating value:


  • Quantifying benefits

  • Comparing alternatives

  • Building stakeholder support

  • Showing return on investment

AI Helps: Leadership can show:


  • “We process 3x more requests with same staff”

  • “Our response time decreased from 21 to 7 days”

  • “We eliminated our 30-year backlog in 18 months”

  • “Citizens rate our service 4.5/5 stars vs. 2.8 previously”

  • “We avoided hiring 3 FTE positions = $300K/year savings”

These numbers win budget battles.

Use Case 25: Change Management & Staff Development

The Challenge: AI transformation requires:


  • Staff acceptance

  • Training and development

  • Change management

  • Culture shift

Successful Approach:


  • Involve staff early in pilot selection

  • Choose quick-win projects showing immediate benefit

  • Celebrate successes

  • Provide training and support

  • Show how AI makes jobs better, not replaceable

  • Focus on eliminating tedious work

Real Example – State Archive: Initial staff resistance to AI:


  • “Will this replace us?”

  • “AI can’t understand archival context”

  • “This feels impersonal”

Approach taken:


  • Pilot with one collection

  • Staff reviewed and refined AI output

  • Highlighted how AI freed them for interesting work

  • Celebrated processing achievements

Result after 6 months:


  • Staff became AI advocates

  • Requested expansion to more collections

  • Morale improved (less tedious work)

  • Quality improved (time for thoughtful analysis)

Implementation Patterns That Work

Pattern 1: Start With Pain Points

Don’t start with “let’s implement AI.”

Start with: “Our biggest problem is ___________.”

Then find AI tools that address that specific problem.

Examples:


  • “We can’t respond to information requests on time” → Request automation

  • “We have a 20-year processing backlog” → Archival description AI

  • “Documents are unfindable” → Intelligent search

  • “Classification takes too much time” → Auto-classification

Pattern 2: Pilot Before Scaling

Successful implementations:


  1. Choose one well-defined pilot project

  2. Small enough to complete quickly (3-6 months)

  3. Large enough to demonstrate value

  4. High visibility (success builds support)

  5. Measure everything

  6. Learn and adjust

  7. Then scale

Unsuccessful implementations:


  • Try to solve everything at once

  • No clear success criteria

  • Insufficient measurement

  • No learning period before scaling

Pattern 3: Combine AI with Human Expertise

AI is not:


  • A replacement for professional judgment

  • Fully autonomous

  • 100% accurate

  • Capable of understanding complex context

AI is:


  • Excellent at mechanical tasks

  • Consistent in application

  • Fast at scale

  • Good at pattern recognition

  • A tool that amplifies human expertise

Best results: AI handles mechanical work, humans provide judgment, context, and expertise.

Pattern 4: Build on Existing Frameworks

The RTA frameworks still apply:


  • Classification schemes

  • Retention schedules

  • Metadata standards

  • Archival principles

  • Access policies

AI doesn’t replace these—it implements them consistently at scale.

Agencies with solid frameworks (like those the RTA developed) get better AI results because AI has clear rules to follow.

Deep dive into the RTA framework →

Pattern 5: Measure and Communicate Success

Track metrics:


  • Time savings (hours per task)

  • Volume improvements (items processed)

  • Quality improvements (accuracy, completeness)

  • User satisfaction (staff and citizens)

  • Cost savings/avoidance

Communicate wins:


  • To staff (celebrate achievements)

  • To leadership (justify continued investment)

  • To stakeholders (demonstrate value)

  • To funders (support budget requests)

📊 Use Cases by Challenge Type {#by-challenge}

Backlog Challenges {#backlog-cases}

“We have years/decades of backlog”

Typical approach: AI processes historical backlog while handling current work in real-time.

Volume Challenges {#volume-cases}

“Too much, too fast—we can’t keep up”

Typical approach: AI handles routine volume automatically, staff focuses on exceptions.

Quality Challenges {#quality-cases}

“Work is inconsistent or incomplete”

Typical approach: AI ensures consistency; quality improves because rules applied systematically.

Speed Challenges {#speed-cases}

“We can’t respond fast enough”

Typical approach: AI accelerates mechanical tasks; humans make fast, informed decisions.

Resource Challenges {#resource-cases}

“Limited staff and budget”

Relevant use cases:


  • All of them! AI multiplies what limited staff can accomplish.

Typical approach: Start with highest ROI use cases, use savings to fund expansion.

Getting Started: Your First Implementation {#starting}

Step 1: Identify Your Priority Challenge

Ask your team:


  • What takes the most time?

  • What causes the most frustration?

  • What creates the most citizen complaints?

  • What creates the most compliance risk?

  • Where do we have the longest backlogs?

Pick ONE priority challenge for your first implementation.

Step 2: Find Relevant Use Cases

Use this guide to find use cases addressing your priority:


  • Read the use case details

  • Note the tools mentioned

  • Review the linked articles for depth

Step 3: Explore Tools

Based on use cases, explore specific tools:


  • Request demos

  • Test with your actual documents

  • Talk to government references

  • Understand total costs

  • Plan pilot project

Step 4: Design Your Pilot

Good pilot characteristics:


  • 3-6 month timeline

  • Clear success metrics

  • Well-defined scope

  • High visibility

  • Representative of broader needs

Step 5: Execute, Learn, Scale


  • Implement pilot carefully

  • Measure everything

  • Learn what works

  • Adjust approach

  • Share successes

  • Plan scaling

Expanding: Building on Initial Success {#expanding}

After your first successful implementation:

Approach 1: Expand Same Function

Add AI to related tasks in the same functional area.

Example: Started with request intake automation → Add intelligent search → Add automated redaction → Comprehensive request management

Approach 2: Add Different Functions

Apply AI to different functional areas.

Example: Started with transparency request automation → Add records classification → Add archival description → Comprehensive information management

Approach 3: Deepen Capabilities

Add more sophisticated AI to existing applications.

Example: Started with basic search → Add concept-based discovery → Add cross-system search → Add predictive recommendations → Advanced knowledge management

Most agencies combine all three approaches over 2-3 years.

Transforming: Comprehensive Change {#transforming}

After 2-3 years of successful implementations, consider comprehensive transformation:

Integrated Platform Approach

Instead of point solutions, comprehensive platforms providing:


  • Classification, retention, search, access, preservation

  • Unified user experience

  • Integrated policy management

  • Complete audit trails

  • Full RTA framework implementation

See enterprise platforms →

Organizational Transformation

AI enables new organizational models:


  • Centralized information services

  • Shared services across departments

  • Proactive transparency by default

  • Data-driven decision making

  • Evidence-based policy

The RTA Vision, Fully Realized

The frameworks the RTA developed are finally achievable at scale:


  • Every document properly classified

  • Retention systematically applied

  • Materials accessible to those who need them

  • Preservation ensured

  • Transparency maximized

  • Compliance verified

AI doesn’t change the vision—it makes the vision achievable.

Read about the complete RTA framework evolution →

Learning from Others

Connect with Agencies Using AI

We can connect you with agencies implementing similar use cases:


  • Same functional area

  • Similar size and budget

  • Comparable legal framework

  • Geographic proximity

Conclusion: From Working Groups to Use Cases

The RTA’s working groups brought together professionals facing similar challenges. Transparency officers shared strategies for managing requests. Archivists discussed processing approaches. Records managers collaborated on classification schemes. IT teams tackled interoperability.

Those collaborations produced frameworks that worked—proven methodologies based on real-world implementation and shared learning.

Today, those same professionals face the same fundamental challenges, but now with AI tools that can implement the proven frameworks at scale. The use cases in this guide show how different government functions are using AI to address the challenges the working groups wrestled with.

The professional specializations remain the same. The challenges remain the same. The frameworks remain the same. What’s changed is the ability to implement those frameworks comprehensively, consistently, and at a scale that was never possible manually.

Whether you’re a transparency officer managing requests, a records manager implementing classification, an archivist processing backlogs, an IT director integrating systems, or a legal officer managing compliance—there are use cases here showing how your peers are using AI successfully.

The RTA working groups built the foundation through collaboration and shared expertise. AI provides the implementation capability. Together, they’re transforming government information management.

The question isn’t whether AI can help your function—these use cases prove it can. The question is which use case to implement first.

Start there. Build on success. Transform your work.

The RTA’s collaborative spirit continues—now powered by AI.

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