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AI for Decision-Making in the Federal Government Training Course

Formats ⟩  Live Virtual: 8 hrs./2 Days  |  In-Person: 12 hrs./2 Days

AI for Decision-Making in the Federal Government equips leaders and analysts with practical strategies to enhance decision quality through AI and data-driven approaches. Participants will explore how AI supports each stage of the federal decision lifecycle—from situational awareness and option analysis to risk assessment and forecasting—while ensuring decisions remain transparent, defensible, and aligned with regulatory expectations. The course also covers the use of generative AI for research, scenario modeling, and decision support, along with critical considerations such as bias, privacy, and governance. By the end, participants will be prepared to lead effective human-AI decision processes that improve outcomes and maintain public trust.

Learning Objectives »

  • Explore how AI enhances federal decision-making.
  • Apply AI-supported decision frameworks.
  • Evaluate AI outputs for accuracy and transparency.
  • Identify and manage risks (bias, privacy, compliance).
  • Use generative AI for decision support and analysis.
  • Support effective human-AI decision partnerships.

Course Agenda

The Future of Decision-Making

  1. Decision-Making in Government
  2. Data-Driven/AI-Augmented Decisions
  3. What “Good” Federal Decisions Require
  4. Where AI Improves Outcomes
  5. Decision Intelligence

Decision Frameworks That Work with AI

  1. The Federal Decision Lifecycle
  2. Structured Decision Models
  3. Enhancing Situation Awareness with AI
  4. Option Generation and Tradeoff
  5. Risk Assessment and Forecasting
  6. Decision Augmentation vs Automation

Defensible, Transparent, Audit-Ready

  1. Defensibility and Traceability
  2. Evaluating AI Outputs
  3. Explainability and Transparency
  4. Documentation and Justification

Generative AI for Decision Support

  1. Decision Speed and Access to Insight
  2. Research, Synthesis, and Briefings
  3. Scenario Modeling and “What-If”
  4. Prompting for Decision Support
  5. Hallucinations and Weak Outputs
  6. AI-Assisted Decision Workflow
  7. AI-Supported Decision Scenario

Risk, Bias, and Responsible Decisions

  1. AI Risk in Federal Decisions
  2. Bias in Data and Impact on Policy
  3. Privacy, Security, and Compliance
  4. Ethical Decision-Making and Red Flags
  5. Building Trust in AI Decisions

Governance and Oversight

  1. AI Guidance and Decision Governance
  2. Roles and Accountability
  3. Evaluating AI Tools and Vendors
  4. Monitoring and Auditing AI Decisions
  5. Decision Effectiveness and Risk

Human + AI Decision Leadership

  1. The Role of Leaders and Analysts
  2. Human Bias vs Algorithmic Bias
  3. Human-AI Decision Partnerships
Technical technology presentation skills for workplace professionals training

AI for Decision-Making in the Federal Government Training Course

Formats ⟩  Live Virtual: 8 hrs./2 Days  |  In-Person: 12 hrs./2 Days

AI for Decision-Making in the Federal Government equips leaders and analysts with practical strategies to enhance decision quality through AI and data-driven approaches. Participants will explore how AI supports each stage of the federal decision lifecycle—from situational awareness and option analysis to risk assessment and forecasting—while ensuring decisions remain transparent, defensible, and aligned with regulatory expectations. The course also covers the use of generative AI for research, scenario modeling, and decision support, along with critical considerations such as bias, privacy, and governance. By the end, participants will be prepared to lead effective human-AI decision processes that improve outcomes and maintain public trust.

Learning Objectives »

  • Explore how AI enhances federal decision-making.
  • Apply AI-supported decision frameworks.
  • Evaluate AI outputs for accuracy and transparency.
  • Identify and manage risks (bias, privacy, compliance).
  • Use generative AI for decision support and analysis.
  • Support effective human-AI decision partnerships.

Course Agenda

The Future of Decision-Making

  1. Decision-Making in Government
  2. Data-Driven/AI-Augmented Decisions
  3. What “Good” Federal Decisions Require
  4. Where AI Improves Outcomes
  5. Decision Intelligence

Decision Frameworks That Work with AI

  1. The Federal Decision Lifecycle
  2. Structured Decision Models
  3. Enhancing Situation Awareness with AI
  4. Option Generation and Tradeoff
  5. Risk Assessment and Forecasting
  6. Decision Augmentation vs Automation

Defensible, Transparent, Audit-Ready

  1. Defensibility and Traceability
  2. Evaluating AI Outputs
  3. Explainability and Transparency
  4. Documentation and Justification

Generative AI for Decision Support

  1. Decision Speed and Access to Insight
  2. Research, Synthesis, and Briefings
  3. Scenario Modeling and “What-If”
  4. Prompting for Decision Support
  5. Hallucinations and Weak Outputs
  6. AI-Assisted Decision Workflow
  7. AI-Supported Decision Scenario

Risk, Bias, and Responsible Decisions

  1. AI Risk in Federal Decisions
  2. Bias in Data and Impact on Policy
  3. Privacy, Security, and Compliance
  4. Ethical Decision-Making and Red Flags
  5. Building Trust in AI Decisions

Governance and Oversight

  1. AI Guidance and Decision Governance
  2. Roles and Accountability
  3. Evaluating AI Tools and Vendors
  4. Monitoring and Auditing AI Decisions
  5. Decision Effectiveness and Risk

Human + AI Decision Leadership

  1. The Role of Leaders and Analysts
  2. Human Bias vs Algorithmic Bias
  3. Human-AI Decision Partnerships
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