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Developing an Artificial Intelligence (AI) Strategy for Federal Government Leaders Training Course

Coming Soon

Formats ⟩  Live Virtual: 4 hrs./1 Day  |  In-Person: 6 hrs./1 Day

Artificial intelligence is no longer experimental. It is becoming a core capability for mission delivery, operational efficiency, and public service innovation across federal agencies. However, successful AI adoption in government requires more than tools. It demands clear strategy, governance, and alignment with mission, policy, and public trust. This course equips federal leaders and program managers with the frameworks, decision models, and practical steps needed to design, govern, and implement an AI strategy responsibly and effectively.

Learning Objectives

  • Define what an AI strategy means in a federal context.
  • Align AI initiatives to agency mission, priorities, and mandates.
  • Identify high-value, low-risk AI use cases.
  • Navigate constraints (policy, procurement, security, compliance).
  • Establish AI governance, risk management, and ethical safeguards.
  • Build a phased, realistic AI roadmap.
  • Lead cross-functional adoption and change management.

Course Agenda »

Identifying Strategic AI Opportunities
(Where AI Should be Applied)

  1. Linking AI to Agency Mission
  2. Use Cases in Federal Government
  3. Efficiency vs. Decision-Making Risk
  4. “Safe” Starting Points
  5. Avoiding High-Risk Early Deployments
  6. Prioritization Framework
  7. Common Federal AI Use Cases

Human-AI Collaboration in Workflows (How AI Fits into Work)

  1. Redefining Work: Humans + AI
  2. Human-in-the-loop Systems
  3. Designing AI-Supported Workflows
  4. Building Trust and Adoption
  5. Case Studies: Federal-Style AI

Governance, Risk and Responsible AI

  1. Why Responsible AI is Non-Negotiable
  2. Core AI Risks in Federal Environments
  3. AI Governance Framework
  4. Vendor and Tool Evaluation
  5. Monitoring and Lifecycle Management

Building and Executing an AI Roadmap

  1. Defining AI Vision Aligned to Mission
  2. AI Roadmap Development Framework
  3. Resource and Capability Planning
  4. Change Management and Adoption
  5. Measuring Success, ROE and ROI

GenAI and the Future of Federal Work

  1. Generative AI in Government Context
  2. Practical GenAI
  3. GenAI Prompting as a Workforce Skill
  4. Emerging Trends

Developing an Artificial Intelligence (AI) Strategy for Federal Government Leaders Training Course

Coming Soon

Formats ⟩  Live Virtual: 4 hrs./1 Day  |  In-Person: 6 hrs./1 Day

Artificial intelligence is no longer experimental. It is becoming a core capability for mission delivery, operational efficiency, and public service innovation across federal agencies. However, successful AI adoption in government requires more than tools. It demands clear strategy, governance, and alignment with mission, policy, and public trust. This course equips federal leaders and program managers with the frameworks, decision models, and practical steps needed to design, govern, and implement an AI strategy responsibly and effectively.

Learning Objectives

  • Define what an AI strategy means in a federal context.
  • Align AI initiatives to agency mission, priorities, and mandates.
  • Identify high-value, low-risk AI use cases.
  • Navigate constraints (policy, procurement, security, compliance).
  • Establish AI governance, risk management, and ethical safeguards.
  • Build a phased, realistic AI roadmap.
  • Lead cross-functional adoption and change management.

Course Agenda »

Identifying Strategic AI Opportunities
(Where AI Should be Applied)

  1. Linking AI to Agency Mission
  2. Use Cases in Federal Government
  3. Efficiency vs. Decision-Making Risk
  4. “Safe” Starting Points
  5. Avoiding High-Risk Early Deployments
  6. Prioritization Framework
  7. Common Federal AI Use Cases

Human-AI Collaboration in Workflows (How AI Fits into Work)

  1. Redefining Work: Humans + AI
  2. Human-in-the-loop Systems
  3. Designing AI-Supported Workflows
  4. Building Trust and Adoption
  5. Case Studies: Federal-Style AI

Governance, Risk and Responsible AI

  1. Why Responsible AI is Non-Negotiable
  2. Core AI Risks in Federal Environments
  3. AI Governance Framework
  4. Vendor and Tool Evaluation
  5. Monitoring and Lifecycle Management

Building and Executing an AI Roadmap

  1. Defining AI Vision Aligned to Mission
  2. AI Roadmap Development Framework
  3. Resource and Capability Planning
  4. Change Management and Adoption
  5. Measuring Success, ROE and ROI

GenAI and the Future of Federal Work

  1. Generative AI in Government Context
  2. Practical GenAI
  3. GenAI Prompting as a Workforce Skill
  4. Emerging Trends
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