# AI in Government Asset Management

Canonical URL: <https://www.graduateschool.edu/courses/ai-in-government-asset-management>

## Overview

This course provides government property managers, facilities and fleet leaders, procurement professionals, finance staff, and auditors with a practical introduction to artificial intelligence (AI) in public-sector asset management. Participants examine the full asset lifecycle—acquisition, deployment, maintenance, transfer, replacement, and disposal—and learn how AI-enabled tools such as predictive maintenance, smart inventory management, computer vision, and fraud and waste detection are reshaping accountability and operational readiness across federal, state, and local agencies. The course also addresses persistent public-sector challenges, including inaccurate inventories, deferred maintenance, data silos, and compliance reporting burdens.

Through instructor-led discussion, real-world case studies, and hands-on workshop exercises, participants assess organizational readiness for AI adoption, prioritize high-risk and high-value assets, and design pilot projects with defined scope, expected return on investment, and risk considerations. The course closes with a practical implementation roadmap covering data quality, governance, privacy, cybersecurity, and responsible scaling. By the end of the program, participants leave with actionable strategies to modernize asset management practices, strengthen audit readiness, extend asset life, and prepare their organizations for the future of intelligent public-sector operations.

## What you'll learn

- Explain the fundamentals of government asset management and the stages of the asset lifecycle.
- Identify common challenges that limit accountability and efficiency in public-sector asset control.
- Describe how artificial intelligence and automation are transforming asset management practices.
- Apply predictive analytics, smart inventory, and computer vision concepts to public-sector use cases.
- Evaluate real-world case studies to identify lessons learned and transferable practices.
- Assess organizational readiness for AI adoption using a structured scorecard approach.
- Prioritize high-risk and high-value assets for early AI investment.
- Design an AI pilot project with defined scope, expected return on investment, and risk considerations.
- Build an implementation roadmap that supports data quality, governance, and responsible scaling.

## Curriculum

#### Module 1: Foundations of Government Asset Management

- Define what constitutes a government asset across buildings, vehicles, IT equipment, infrastructure, machinery, and public safety equipment.
- Describe the asset lifecycle from acquisition and deployment through maintenance, transfer, disposal, and replacement.
- Explain why disciplined asset management supports taxpayer accountability, budget optimization, and audit readiness.
- Recognize how strong asset practices contribute to operational continuity and risk reduction.

#### Module 2: Current Challenges in Public-Sector Asset Control

- Identify common problems such as inaccurate inventories, missing assets, and reliance on manual spreadsheets.
- Examine the impact of deferred maintenance, poor utilization visibility, and aging infrastructure.
- Discuss the role of data silos and compliance reporting burdens in limiting accountability.
- Recognize cybersecurity risks associated with smart and connected public-sector assets.

#### Module 3: AI Applications in Government Asset Management

- Explain how predictive maintenance uses sensor data and service history to anticipate failures.
- Describe smart inventory management and AI-driven reorder and utilization recommendations.
- Apply computer vision, barcode/RFID automation, and drone inspection concepts to asset tracking.
- Identify how AI supports fraud and waste detection, including duplicate purchases, unused subscriptions, and ghost assets.
- Describe how AI models inform budget forecasting and capital replacement planning.

#### Module 4: Current Case Studies

- Examine smart city asset monitoring programs and the use of sensors and analytics for utilities and infrastructure.
- Review federal real property modernization efforts that use centralized digital records to improve building utilization.
- Explore digital twin programs that support long-term planning and predictive maintenance for roads and utilities.
- Analyze municipal fleet optimization initiatives that combine telematics and AI to improve performance and reduce costs.

#### Module 5: Practical Exercises and Workshops

- Rank a sample portfolio of public-sector assets by mission risk and replacement priority.
- Complete an AI readiness scorecard covering data quality, leadership support, IT integration, and budget.
- Design an AI pilot project with defined asset type, problem statement, expected savings, timeline, and risks.
- Apply structured judgment to evaluate where AI adds value and where human oversight must remain primary.

#### Module 6: Roadmap for Implementation

- Begin with data quality by correcting inventory records before introducing AI tools.
- Start small by piloting one asset category to validate approach and return on investment.
- Measure outcomes through savings, uptime, and labor reduction metrics.
- Scale responsibly after pilot success while maintaining governance, privacy, and cybersecurity safeguards.

## Pricing

**Tuition:** $675