Learning Objectives

After completing this lecture, you will be able to:

• Understand the key concepts of ai use case inventory management in a government context
• Participate in structured workshop activities with real-world scenarios
• Connect ai use case inventory management to your agency's AI initiatives
• Identify next steps for applying these concepts in your role

Key Topics Covered

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Tracking, classifying, updating, reporting

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Tools and templates

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Annual reporting requirements

Why This Matters for Government

Overview

Government agencies face unique challenges when it comes to AI adoption. This lecture addresses these challenges head-on by providing senior managers, procurement officers, program directors with the knowledge and frameworks needed to navigate AI in the public sector responsibly and effectively.

As part of the L3 (AI Strategist) curriculum, this lecture builds on the foundational principle that every AI system in government ultimately serves citizens. Whether you are working with AI tools daily or setting strategy for your agency, understanding ai use case inventory management is essential for responsible, effective government AI adoption.

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TRANSCRIPT: AI Use Case Inventory Management

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What you will learn: Building AI system inventory; classification schemes; data standards; update procedures; reporting frameworks.

You can't govern what you don't know exists. An accurate, maintained inventory of all AI systems is foundational to governance. This lecture teaches you how to build and maintain an effective AI system inventory.

Why AI Inventory Matters

A comprehensive inventory answers critical questions:

• What AI systems does our agency operate?
• What risks do they pose?
• Who's accountable for each system?
• When were they last reviewed?
• Are they performing as expected?
• Are there systems we don't know about?

What to Include in Inventory

Each system entry should include:

Basic Information

• System name
• System description (what does it do?)
• Owner and team
• Status (development, pilot, operational, retired)
• Last update date

Risk Classification

• Risk level (low, moderate, high, critical)
• Rights-impacting (yes/no)
• Safety-impacting (yes/no)
• Key risks identified

Technical Details

• Data sources
• Model type
• Version
• Last update
• Accuracy/performance metrics

Governance

• Last board review date
• Next scheduled review
• Approvals obtained
• Outstanding issues

Monitoring

• Current performance
• Status of safeguards
• Recent incidents
• Audit status

Inventory Structure

Simple approach: Spreadsheet with above fields

Medium approach: Database with reporting

Comprehensive approach: Dedicated inventory system with API access

Start simple. Graduate to more sophisticated as your inventory grows.

Classification Scheme

Develop a consistent classification for:

Risk Level

• Low: Limited impact if fails, no rights/safety implications
• Moderate: Some impact if fails, affects user experience
• High: Significant impact if fails, affects important decisions
• Critical: Severe impact if fails, affects rights or safety

System Type

• Classification
• Prediction/Regression
• Clustering
• Generation
• Ranking
• Recommendation

Mission Area

• Benefits
• HR/Recruitment
• Law Enforcement
• Operations
• Customer Service

Development Status

• Development
• Testing
• Pilot
• Production
• Retired

Inventory Management Procedures

Quarterly Updates

• Owner verifies system still exists and is accurate
• Updates system status, performance metrics
• Notes any changes since last review

Annual Review

• Comprehensive review of all systems
• Risk classification updated if needed
• Last safeguard verification confirmed
• New systems identified

Real-Time Updates

• Major incidents reported immediately
• System shutdowns noted
• Significant changes documented

Reporting

Quarterly Report Contents:

• Total systems count
• Count by risk level
• New systems added
• Systems retired
• Major incidents
• Overdue reviews
• Recommendations

Anti-Patterns

• Inventory that's not maintained (becomes stale)
• Inventory so complex that people avoid using it
• Inventory that doesn't connect to governance processes
• Systems that aren't in inventory
• Duplicate systems in inventory with different names

Key Takeaways

• Comprehensive inventory is foundational to governance.
• Start simple. Graduate to sophistication as you scale.
• Inventory must be actively maintained.
• Inventory should drive governance decisions.
• Regular audits verify inventory accuracy.
• Inventory is a living document, not a snapshot.
• Discrepancies between inventory and reality indicate governance issues.

Government AI CLUB Certification Program

Level 3: AI Practitioner | AI Use Case Inventory Management | Lecture 3.2.10

A GOVT.CLUB initiative.

<- 3.2.6 GAO AI Accountability: Four Principles in Practice
3.2.8 Rights-Impacting and Safety-Impacting AI Safeguards ->

Start Your CLUB Certification

This lecture is part of L3: AI Strategist -- 80 hours of comprehensive government AI training.

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